


"""simple docstring"""





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     MODEL_MAPPING,
     BeitForImageClassification,
     BeitForMaskedImageModeling,
     BeitForSemanticSegmentation,
     BeitModel,
 )
 from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 import PIL
 from PIL import Image

 from transformers import BeitImageProcessor



class 		snake_case_:






     def __init__(	self  :					Optional[int]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					List[str]=1_0_0					,				UpperCamelCase_  :					List[str]=1_3					,				UpperCamelCase_  :					Optional[int]=3_0					,				UpperCamelCase_  :					List[str]=2					,				UpperCamelCase_  :					Union[str, Any]=3					,				UpperCamelCase_  :					str=True					,				UpperCamelCase_  :					int=True					,				UpperCamelCase_  :					Optional[Any]=3_2					,				UpperCamelCase_  :					Union[str, Any]=4					,				UpperCamelCase_  :					int=4					,				UpperCamelCase_  :					Any=3_7					,				UpperCamelCase_  :					str="gelu"					,				UpperCamelCase_  :					Optional[Any]=0.1					,				UpperCamelCase_  :					Optional[int]=0.1					,				UpperCamelCase_  :					int=1_0					,				UpperCamelCase_  :					Tuple=0.02					,				UpperCamelCase_  :					Tuple=3					,				UpperCamelCase_  :					Optional[int]=None					,				UpperCamelCase_  :					Dict=[0, 1, 2, 3]					,				):
           lowerCAmelCase					:	Dict       =   parent
           lowerCAmelCase					:	List[Any]       =   1_0_0
           lowerCAmelCase					:	List[str]       =   batch_size
           lowerCAmelCase					:	Union[str, Any]       =   image_size
           lowerCAmelCase					:	Dict       =   patch_size
           lowerCAmelCase					:	Any       =   num_channels
           lowerCAmelCase					:	List[Any]       =   is_training
           lowerCAmelCase					:	Union[str, Any]       =   use_labels
           lowerCAmelCase					:	Tuple       =   hidden_size
           lowerCAmelCase					:	Dict       =   num_hidden_layers
           lowerCAmelCase					:	Union[str, Any]       =   num_attention_heads
           lowerCAmelCase					:	Optional[Any]       =   intermediate_size
           lowerCAmelCase					:	Dict       =   hidden_act
           lowerCAmelCase					:	Tuple       =   hidden_dropout_prob
           lowerCAmelCase					:	List[Any]       =   attention_probs_dropout_prob
           lowerCAmelCase					:	Union[str, Any]       =   type_sequence_label_size
           lowerCAmelCase					:	Any       =   initializer_range
           lowerCAmelCase					:	Optional[Any]       =   scope
           lowerCAmelCase					:	Tuple       =   out_indices
           lowerCAmelCase					:	List[str]       =   num_labels

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowerCAmelCase					:	List[Any]       =   (image_size // patch_size) ** 2
           lowerCAmelCase					:	List[Any]       =   num_patches + 1






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	Dict       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           lowerCAmelCase					:	List[Any]       =   None
           lowerCAmelCase					:	Optional[int]       =   None
           if self.use_labels:
                 lowerCAmelCase					:	str       =   ids_tensor([self.batch_size]					,				self.type_sequence_label_size				)
                 lowerCAmelCase					:	List[Any]       =   ids_tensor([self.batch_size, self.image_size, self.image_size]					,				self.num_labels				)

           lowerCAmelCase					:	Tuple       =   self.get_config()

           return config, pixel_values, labels, pixel_labels






     def      lowerCamelCase__     (	self  :					Any				):
           return BeitConfig(
               vocab_size=self.vocab_size					,				image_size=self.image_size					,				patch_size=self.patch_size					,				num_channels=self.num_channels					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				intermediate_size=self.intermediate_size					,				hidden_act=self.hidden_act					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				is_decoder=UpperCamelCase_					,				initializer_range=self.initializer_range					,				out_indices=self.out_indices					,				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Union[str, Any]       =   BeitModel(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_				)
           self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)				)






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	List[Any]       =   BeitForMaskedImageModeling(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	Union[str, Any]       =   model(UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length - 1, self.vocab_size)				)






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					int				):
           lowerCAmelCase					:	Union[str, Any]       =   self.type_sequence_label_size
           lowerCAmelCase					:	Union[str, Any]       =   BeitForImageClassification(UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	Optional[Any]       =   model(UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)				)

           # test greyscale images
           lowerCAmelCase					:	List[str]       =   1
           lowerCAmelCase					:	str       =   BeitForImageClassification(UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()

           lowerCAmelCase					:	Any       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
           lowerCAmelCase					:	Tuple       =   model(UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					int				):
           lowerCAmelCase					:	List[str]       =   self.num_labels
           lowerCAmelCase					:	int       =   BeitForSemanticSegmentation(UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	int       =   model(UpperCamelCase_				)
           self.parent.assertEqual(
               result.logits.shape					,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
           lowerCAmelCase					:	Tuple       =   model(UpperCamelCase_					,				labels=UpperCamelCase_				)
           self.parent.assertEqual(
               result.logits.shape					,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Optional[Any]       =   self.prepare_config_and_inputs()
           lowerCAmelCase					:	Any       =   config_and_inputs
           lowerCAmelCase					:	Optional[Any]       =   {'''pixel_values''': pixel_values}
           return config, inputs_dict



@require_torch
class 		snake_case_(       a__							,					a__							,					unittest.TestCase			):
     __UpperCamelCase  =					(
         (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __UpperCamelCase  =					(
         {
             '''feature-extraction''': BeitModel,
             '''image-classification''': BeitForImageClassification,
             '''image-segmentation''': BeitForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __UpperCamelCase  =					False
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	str       =   BeitModelTester(self				)
           lowerCAmelCase					:	Tuple       =   ConfigTester(self					,				config_class=UpperCamelCase_					,				has_text_modality=UpperCamelCase_					,				hidden_size=3_7				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           self.config_tester.run_common_tests()






     @unittest.skip(reason='''BEiT does not use inputs_embeds'''				)
     def      lowerCamelCase__     (	self  :					str				):
           pass






     @require_torch_multi_gpu
     @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''				)
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           pass






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	List[Any]       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase					:	Union[str, Any]       =   model_class(UpperCamelCase_				)
                 self.assertIsInstance(model.get_input_embeddings()					,				(nn.Module)				)
                 lowerCAmelCase					:	Dict       =   model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(UpperCamelCase_					,				nn.Linear				)				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Tuple       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase					:	Any       =   model_class(UpperCamelCase_				)
                 lowerCAmelCase					:	Union[str, Any]       =   inspect.signature(model.forward				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCAmelCase					:	List[Any]       =   [*signature.parameters.keys()]

                 lowerCAmelCase					:	Dict       =   ['''pixel_values''']
                 self.assertListEqual(arg_names[:1]					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	str       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           if not self.model_tester.is_training:
                 return

           lowerCAmelCase					:	Dict       =   self.model_tester.prepare_config_and_inputs_for_common()
           lowerCAmelCase					:	Optional[int]       =   True

           for model_class in self.all_model_classes:
                 # we don't test BeitForMaskedImageModeling
                 if model_class in [*get_values(UpperCamelCase_				), BeitForMaskedImageModeling]:
                       continue

                 lowerCAmelCase					:	Tuple       =   model_class(UpperCamelCase_				)
                 model.to(UpperCamelCase_				)
                 model.train()
                 lowerCAmelCase					:	str       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_					,				return_labels=UpperCamelCase_				)
                 lowerCAmelCase					:	List[Any]       =   model(**UpperCamelCase_				).loss
                 loss.backward()






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	List[Any]       =   self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                 return

           lowerCAmelCase					:	Any       =   False
           lowerCAmelCase					:	Dict       =   True

           for model_class in self.all_model_classes:
                 # we don't test BeitForMaskedImageModeling
                 if (
                     model_class in [*get_values(UpperCamelCase_				), BeitForMaskedImageModeling]
                     or not model_class.supports_gradient_checkpointing
                 ):
                       continue

                 lowerCAmelCase					:	str       =   model_class(UpperCamelCase_				)
                 model.gradient_checkpointing_enable()
                 model.to(UpperCamelCase_				)
                 model.train()
                 lowerCAmelCase					:	Tuple       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_					,				return_labels=UpperCamelCase_				)
                 lowerCAmelCase					:	Union[str, Any]       =   model(**UpperCamelCase_				).loss
                 loss.backward()






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	int       =   self.model_tester.prepare_config_and_inputs_for_common()

           lowerCAmelCase					:	Any       =   _config_zero_init(UpperCamelCase_				)
           for model_class in self.all_model_classes:
                 lowerCAmelCase					:	str       =   model_class(config=UpperCamelCase_				)
                 for name, param in model.named_parameters():
                       # we skip lambda parameters as these require special initial values
                       # determined by config.layer_scale_init_value
                       if "lambda" in name:
                             continue
                       if param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()					,				[0.0, 1.0]					,				msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''					,				)






     @slow
     def      lowerCamelCase__     (	self  :					Any				):
           for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase					:	Tuple       =   BeitModel.from_pretrained(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)



def      _snake_case						(						):
      lowerCAmelCase					:	str       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      return image






@require_torch
@require_vision
class 		snake_case_(       unittest.TestCase			):






     @cached_property
     def      lowerCamelCase__     (	self  :					Dict				):
           return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''				) if is_vision_available() else None






     @slow
     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Union[str, Any]       =   BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''				).to(UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   self.default_image_processor
           lowerCAmelCase					:	Union[str, Any]       =   prepare_img()
           lowerCAmelCase					:	int       =   image_processor(images=UpperCamelCase_					,				return_tensors='''pt'''				).pixel_values.to(UpperCamelCase_				)

           # prepare bool_masked_pos
           lowerCAmelCase					:	List[Any]       =   torch.ones((1, 1_9_6)					,				dtype=torch.bool				).to(UpperCamelCase_				)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase					:	List[Any]       =   model(pixel_values=UpperCamelCase_					,				bool_masked_pos=UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   outputs.logits

           # verify the logits
           lowerCAmelCase					:	List[Any]       =   torch.Size((1, 1_9_6, 8_1_9_2)				)
           self.assertEqual(logits.shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	Optional[int]       =   torch.tensor(
               [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]				).to(UpperCamelCase_				)

           self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]					,				UpperCamelCase_					,				atol=1E-2				)				)






     @slow
     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Dict       =   BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''				).to(UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   self.default_image_processor
           lowerCAmelCase					:	Union[str, Any]       =   prepare_img()
           lowerCAmelCase					:	int       =   image_processor(images=UpperCamelCase_					,				return_tensors='''pt'''				).to(UpperCamelCase_				)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase					:	int       =   model(**UpperCamelCase_				)
           lowerCAmelCase					:	List[Any]       =   outputs.logits

           # verify the logits
           lowerCAmelCase					:	List[str]       =   torch.Size((1, 1_0_0_0)				)
           self.assertEqual(logits.shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	Optional[int]       =   torch.tensor([-1.2_385, -1.0_987, -1.0_108]				).to(UpperCamelCase_				)

           self.assertTrue(torch.allclose(logits[0, :3]					,				UpperCamelCase_					,				atol=1E-4				)				)

           lowerCAmelCase					:	Optional[int]       =   2_8_1
           self.assertEqual(logits.argmax(-1				).item()					,				UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''				).to(
               UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   self.default_image_processor
           lowerCAmelCase					:	Optional[int]       =   prepare_img()
           lowerCAmelCase					:	int       =   image_processor(images=UpperCamelCase_					,				return_tensors='''pt'''				).to(UpperCamelCase_				)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase					:	Optional[int]       =   model(**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   outputs.logits

           # verify the logits
           lowerCAmelCase					:	Tuple       =   torch.Size((1, 2_1_8_4_1)				)
           self.assertEqual(logits.shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   torch.tensor([1.6_881, -0.2_787, 0.5_901]				).to(UpperCamelCase_				)

           self.assertTrue(torch.allclose(logits[0, :3]					,				UpperCamelCase_					,				atol=1E-4				)				)

           lowerCAmelCase					:	Optional[int]       =   2_3_9_6
           self.assertEqual(logits.argmax(-1				).item()					,				UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Optional[Any]       =   BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''				)
           lowerCAmelCase					:	str       =   model.to(UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   BeitImageProcessor(do_resize=UpperCamelCase_					,				size=6_4_0					,				do_center_crop=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   load_dataset('''hf-internal-testing/fixtures_ade20k'''					,				split='''test'''				)
           lowerCAmelCase					:	int       =   Image.open(ds[0]['''file''']				)
           lowerCAmelCase					:	Tuple       =   image_processor(images=UpperCamelCase_					,				return_tensors='''pt'''				).to(UpperCamelCase_				)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase					:	List[Any]       =   model(**UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   outputs.logits

           # verify the logits
           lowerCAmelCase					:	List[str]       =   torch.Size((1, 1_5_0, 1_6_0, 1_6_0)				)
           self.assertEqual(logits.shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	List[str]       =   version.parse(PIL.__version__				) < version.parse('''9.0.0'''				)

           if is_pillow_less_than_a:
                 lowerCAmelCase					:	Any       =   torch.tensor(
                     [
                         [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                         [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                         [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
                     ]					,				device=UpperCamelCase_					,				)
           else:
                 lowerCAmelCase					:	Tuple       =   torch.tensor(
                     [
                         [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
                         [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
                         [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
                     ]					,				device=UpperCamelCase_					,				)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]					,				UpperCamelCase_					,				atol=1E-4				)				)






     @slow
     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Union[str, Any]       =   BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''				)
           lowerCAmelCase					:	Optional[int]       =   model.to(UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   BeitImageProcessor(do_resize=UpperCamelCase_					,				size=6_4_0					,				do_center_crop=UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   load_dataset('''hf-internal-testing/fixtures_ade20k'''					,				split='''test'''				)
           lowerCAmelCase					:	str       =   Image.open(ds[0]['''file''']				)
           lowerCAmelCase					:	List[str]       =   image_processor(images=UpperCamelCase_					,				return_tensors='''pt'''				).to(UpperCamelCase_				)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase					:	Optional[Any]       =   model(**UpperCamelCase_				)

           lowerCAmelCase					:	List[str]       =   outputs.logits.detach().cpu()

           lowerCAmelCase					:	str       =   image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_					,				target_sizes=[(5_0_0, 3_0_0)]				)
           lowerCAmelCase					:	List[Any]       =   torch.Size((5_0_0, 3_0_0)				)
           self.assertEqual(segmentation[0].shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	List[str]       =   image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_				)
           lowerCAmelCase					:	str       =   torch.Size((1_6_0, 1_6_0)				)
           self.assertEqual(segmentation[0].shape					,				UpperCamelCase_				)






"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 snake_case__		:     int    				=				'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			Any ,   _snake_case				:			str=None ,   _snake_case				:			str=None ,   _snake_case				:			Dict=None ,   _snake_case				:			Tuple=None ,   _snake_case				:			str=None ,   _snake_case				:			Any=None ,   ):
      if attention_mask is None:
            lowerCAmelCase					:	List[str]       =   np.where(input_ids != config.pad_token_id ,   1 ,   0			)
      if decoder_attention_mask is None:
            lowerCAmelCase					:	Optional[int]       =   np.where(decoder_input_ids != config.pad_token_id ,   1 ,   0			)
      if head_mask is None:
            lowerCAmelCase					:	Any       =   np.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            lowerCAmelCase					:	int       =   np.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            lowerCAmelCase					:	List[str]       =   np.ones((config.decoder_layers, config.decoder_attention_heads)			)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					int=1_3					,				UpperCamelCase_  :					Union[str, Any]=7					,				UpperCamelCase_  :					Union[str, Any]=True					,				UpperCamelCase_  :					List[Any]=False					,				UpperCamelCase_  :					Dict=9_9					,				UpperCamelCase_  :					Optional[int]=1_6					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					List[str]=4					,				UpperCamelCase_  :					List[Any]=4					,				UpperCamelCase_  :					int="gelu"					,				UpperCamelCase_  :					Optional[int]=0.1					,				UpperCamelCase_  :					Any=0.1					,				UpperCamelCase_  :					str=3_2					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					Tuple=1					,				UpperCamelCase_  :					List[Any]=0					,				UpperCamelCase_  :					Any=0.02					,				):
           lowerCAmelCase					:	Tuple       =   parent
           lowerCAmelCase					:	str       =   batch_size
           lowerCAmelCase					:	List[Any]       =   seq_length
           lowerCAmelCase					:	Optional[int]       =   is_training
           lowerCAmelCase					:	int       =   use_labels
           lowerCAmelCase					:	List[Any]       =   vocab_size
           lowerCAmelCase					:	str       =   hidden_size
           lowerCAmelCase					:	List[Any]       =   num_hidden_layers
           lowerCAmelCase					:	Any       =   num_attention_heads
           lowerCAmelCase					:	List[Any]       =   intermediate_size
           lowerCAmelCase					:	Optional[int]       =   hidden_act
           lowerCAmelCase					:	Dict       =   hidden_dropout_prob
           lowerCAmelCase					:	Optional[int]       =   attention_probs_dropout_prob
           lowerCAmelCase					:	List[Any]       =   max_position_embeddings
           lowerCAmelCase					:	Union[str, Any]       =   eos_token_id
           lowerCAmelCase					:	Dict       =   pad_token_id
           lowerCAmelCase					:	Optional[Any]       =   bos_token_id
           lowerCAmelCase					:	List[str]       =   initializer_range






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	List[Any]       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size				)					,				3					,				self.vocab_size				)
           lowerCAmelCase					:	str       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,				dtype=np.intaa				))					,				-1				)

           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)

           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotConfig(
               vocab_size=self.vocab_size					,				d_model=self.hidden_size					,				encoder_layers=self.num_hidden_layers					,				decoder_layers=self.num_hidden_layers					,				encoder_attention_heads=self.num_attention_heads					,				decoder_attention_heads=self.num_attention_heads					,				encoder_ffn_dim=self.intermediate_size					,				decoder_ffn_dim=self.intermediate_size					,				dropout=self.hidden_dropout_prob					,				attention_dropout=self.attention_probs_dropout_prob					,				max_position_embeddings=self.max_position_embeddings					,				eos_token_id=self.eos_token_id					,				bos_token_id=self.bos_token_id					,				pad_token_id=self.pad_token_id					,				initializer_range=self.initializer_range					,				use_cache=UpperCamelCase_					,				)
           lowerCAmelCase					:	Union[str, Any]       =   prepare_blenderbot_inputs_dict(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self.prepare_config_and_inputs()
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	int       =   2_0
           lowerCAmelCase					:	Tuple       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   model.encode(inputs_dict['''input_ids''']				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           lowerCAmelCase					:	str       =   model.init_cache(decoder_input_ids.shape[0]					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,				dtype='''i4'''				)

           lowerCAmelCase					:	Tuple       =   jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)
           lowerCAmelCase					:	List[Any]       =   model.decode(
               decoder_input_ids[:, :-1]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Any       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4'''				)
           lowerCAmelCase					:	List[str]       =   model.decode(
               decoder_input_ids[:, -1:]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Union[str, Any]       =   model.decode(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	int       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
           self.parent.assertTrue(diff < 1E-3					,				msg=F'''Max diff is {diff}'''				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Optional[int]       =   2_0
           lowerCAmelCase					:	List[Any]       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   model.encode(inputs_dict['''input_ids''']				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   (
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           lowerCAmelCase					:	str       =   jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
               ]					,				axis=-1					,				)

           lowerCAmelCase					:	Union[str, Any]       =   model.init_cache(decoder_input_ids.shape[0]					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	str       =   jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)

           lowerCAmelCase					:	Dict       =   model.decode(
               decoder_input_ids[:, :-1]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)
           lowerCAmelCase					:	Any       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,				dtype='''i4'''				)
           lowerCAmelCase					:	Union[str, Any]       =   model.decode(
               decoder_input_ids[:, -1:]					,				UpperCamelCase_					,				past_key_values=outputs_cache.past_key_values					,				decoder_attention_mask=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)

           lowerCAmelCase					:	Dict       =   model.decode(UpperCamelCase_					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
           self.parent.assertTrue(diff < 1E-3					,				msg=F'''Max diff is {diff}'''				)



@require_flax
class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					99






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	List[Any]       =   np.array(
               [
                   [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                   [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                   [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                   [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                   [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                   [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                   [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                   [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                   [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                   [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                   [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                   [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                   [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
               ]					,				dtype=np.intaa					,				)

           lowerCAmelCase					:	List[Any]       =   input_ids.shape[0]
           lowerCAmelCase					:	Optional[Any]       =   BlenderbotConfig(
               vocab_size=self.vocab_size					,				d_model=2_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=3_2					,				decoder_ffn_dim=3_2					,				max_position_embeddings=4_8					,				eos_token_id=2					,				pad_token_id=1					,				bos_token_id=0					,				)
           return config, input_ids, batch_size






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_config_and_data()
           lowerCAmelCase					:	Any       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   lm_model(input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Any       =   BlenderbotConfig(
               vocab_size=self.vocab_size					,				d_model=1_4					,				encoder_layers=2					,				decoder_layers=2					,				encoder_attention_heads=2					,				decoder_attention_heads=2					,				encoder_ffn_dim=8					,				decoder_ffn_dim=8					,				max_position_embeddings=4_8					,				)
           lowerCAmelCase					:	int       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	List[str]       =   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	List[Any]       =   lm_model(input_ids=UpperCamelCase_					,				decoder_input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	str       =   (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Any       =   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)
           lowerCAmelCase					:	Optional[int]       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           lowerCAmelCase					:	str       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           self.assertEqual(shifted.shape					,				input_ids.shape				)
           self.assertEqual(UpperCamelCase_					,				n_pad_before - 1				)
           self.assertTrue(np.equal(shifted[:, 0]					,				2				).all()				)



@require_flax
class 		snake_case_(       a__							,					unittest.TestCase							,					a__			):
     __UpperCamelCase  =					True
     __UpperCamelCase  =					(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     __UpperCamelCase  =					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Any       =   FlaxBlenderbotModelTester(self				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	str       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                       lowerCAmelCase					:	Optional[int]       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_				)
                       lowerCAmelCase					:	Optional[Any]       =   model_class(UpperCamelCase_				)

                       @jax.jit
                       def encode_jitted(UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Optional[Any]=None					,				**UpperCamelCase_  :					List[str]				):
                             return model.encode(input_ids=UpperCamelCase_					,				attention_mask=UpperCamelCase_				)

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	List[str]       =   encode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	int       =   encode_jitted(**UpperCamelCase_				).to_tuple()

                       self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
                       for jitted_output, output in zip(UpperCamelCase_					,				UpperCamelCase_				):
                             self.assertEqual(jitted_output.shape					,				output.shape				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__				):
                       lowerCAmelCase					:	Tuple       =   model_class(UpperCamelCase_				)
                       lowerCAmelCase					:	int       =   model.encode(inputs_dict['''input_ids''']					,				inputs_dict['''attention_mask''']				)

                       lowerCAmelCase					:	List[Any]       =   {
                           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                           '''encoder_outputs''': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					int				):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				encoder_outputs=UpperCamelCase_					,				)

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	str       =   decode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	Union[str, Any]       =   decode_jitted(**UpperCamelCase_				).to_tuple()

                       self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
                       for jitted_output, output in zip(UpperCamelCase_					,				UpperCamelCase_				):
                             self.assertEqual(jitted_output.shape					,				output.shape				)






     @slow
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase					:	Optional[int]       =   model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 lowerCAmelCase					:	int       =   np.ones((1, 1)				) * model.config.eos_token_id
                 lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)






     @unittest.skipUnless(jax_device != '''cpu'''					,				'''3B test too slow on CPU.'''				)
     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
           lowerCAmelCase					:	List[str]       =   {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

           lowerCAmelCase					:	Tuple       =   FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''					,				from_pt=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

           lowerCAmelCase					:	List[Any]       =   ['''Sam''']
           lowerCAmelCase					:	str       =   tokenizer(UpperCamelCase_					,				return_tensors='''jax'''				)

           lowerCAmelCase					:	Union[str, Any]       =   model.generate(**UpperCamelCase_					,				**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   '''Sam is a great name. It means "sun" in Gaelic.'''

           lowerCAmelCase					:	Union[str, Any]       =   tokenizer.batch_decode(UpperCamelCase_					,				**UpperCamelCase_				)
           assert generated_txt[0].strip() == tgt_text


