

'''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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple=1_0_0      ,lowercase__     :  str=1_3      ,lowercase__     :  int=3_0      ,lowercase__     :  Optional[int]=2      ,lowercase__     :  Union[str, Any]=3      ,lowercase__     :  List[str]=True      ,lowercase__     :  str=True      ,lowercase__     :  List[Any]=3_2      ,lowercase__     :  List[Any]=4      ,lowercase__     :  List[str]=4      ,lowercase__     :  Tuple=3_7      ,lowercase__     :  Tuple="gelu"      ,lowercase__     :  int=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  Optional[int]=1_0      ,lowercase__     :  Optional[int]=0.0_2      ,lowercase__     :  Dict=3      ,lowercase__     :  List[str]=None      ,lowercase__     :  int=[0, 1, 2, 3]      ,):
   __lowercase													=				parent
   __lowercase													=				1_0_0
   __lowercase													=				batch_size
   __lowercase													=				image_size
   __lowercase													=				patch_size
   __lowercase													=				num_channels
   __lowercase													=				is_training
   __lowercase													=				use_labels
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				type_sequence_label_size
   __lowercase													=				initializer_range
   __lowercase													=				scope
   __lowercase													=				out_indices
   __lowercase													=				num_labels

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

   __lowercase													=				None
   __lowercase													=				None
   if self.use_labels:
    __lowercase													=				ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
    __lowercase													=				ids_tensor([self.batch_size, self.image_size, self.image_size]      ,self.num_labels    )

   __lowercase													=				self.get_config()

   return config, pixel_values, labels, pixel_labels






  def       SCREAMING_SNAKE_CASE   (   self     :  List[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=lowercase__      ,initializer_range=self.initializer_range      ,out_indices=self.out_indices      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Any    ):
   __lowercase													=				BeitModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Any    ):
   __lowercase													=				BeitForMaskedImageModeling(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length - 1, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Union[str, Any]    ):
   __lowercase													=				self.type_sequence_label_size
   __lowercase													=				BeitForImageClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)    )

   # test greyscale images
   __lowercase													=				1
   __lowercase													=				BeitForImageClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()

   __lowercase													=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   __lowercase													=				model(lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)    )






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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Dict           =    (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Tuple           =    (
      {
          'feature-extraction': BeitModel,
          'image-classification': BeitForImageClassification,
          'image-segmentation': BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE					:						List[str]           =    False
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						Dict           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				BeitModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,has_text_modality=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   self.config_tester.run_common_tests()






  @unittest.skip(reason='''BEiT does not use inputs_embeds'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   pass






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






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				model_class(lowercase__    )
    self.assertIsInstance(model.get_input_embeddings()      ,(nn.Module)    )
    __lowercase													=				model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase__      ,nn.Linear    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				model_class(lowercase__    )
    __lowercase													=				inspect.signature(model.forward    )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase													=				[*signature.parameters.keys()]

    __lowercase													=				['''pixel_values''']
    self.assertListEqual(arg_names[:1]      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   if not self.model_tester.is_training:
    return

   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase													=				True

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

    __lowercase													=				model_class(lowercase__    )
    model.to(lowercase__    )
    model.train()
    __lowercase													=				self._prepare_for_class(lowercase__      ,lowercase__      ,return_labels=lowercase__    )
    __lowercase													=				model(**lowercase__    ).loss
    loss.backward()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   if not self.model_tester.is_training:
    return

   __lowercase													=				False
   __lowercase													=				True

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

    __lowercase													=				model_class(lowercase__    )
    model.gradient_checkpointing_enable()
    model.to(lowercase__    )
    model.train()
    __lowercase													=				self._prepare_for_class(lowercase__      ,lowercase__      ,return_labels=lowercase__    )
    __lowercase													=				model(**lowercase__    ).loss
    loss.backward()






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   __lowercase													=				_config_zero_init(lowercase__    )
   for model_class in self.all_model_classes:
    __lowercase													=				model_class(config=lowercase__    )
    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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				BeitModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






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




  """simple docstring"""




  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''    ) if is_vision_available() else None






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''    ).to(lowercase__    )

   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).pixel_values.to(lowercase__    )

   # prepare bool_masked_pos
   __lowercase													=				torch.ones((1, 1_9_6)      ,dtype=torch.bool    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(pixel_values=lowercase__      ,bool_masked_pos=lowercase__    )
   __lowercase													=				outputs.logits

   # verify the logits
   __lowercase													=				torch.Size((1, 1_9_6, 8_1_9_2)    )
   self.assertEqual(logits.shape      ,lowercase__    )

   __lowercase													=				torch.tensor(
       [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]    ).to(lowercase__    )

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''    ).to(lowercase__    )

   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )
   __lowercase													=				outputs.logits

   # verify the logits
   __lowercase													=				torch.Size((1, 1_0_0_0)    )
   self.assertEqual(logits.shape      ,lowercase__    )

   __lowercase													=				torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]    ).to(lowercase__    )

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

   __lowercase													=				2_8_1
   self.assertEqual(logits.argmax(-1    ).item()      ,lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''    ).to(
       lowercase__    )

   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )
   __lowercase													=				outputs.logits

   # verify the logits
   __lowercase													=				torch.Size((1, 2_1_8_4_1)    )
   self.assertEqual(logits.shape      ,lowercase__    )

   __lowercase													=				torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]    ).to(lowercase__    )

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

   __lowercase													=				2_3_9_6
   self.assertEqual(logits.argmax(-1    ).item()      ,lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''    )
   __lowercase													=				model.to(lowercase__    )

   __lowercase													=				BeitImageProcessor(do_resize=lowercase__      ,size=6_4_0      ,do_center_crop=lowercase__    )

   __lowercase													=				load_dataset('''hf-internal-testing/fixtures_ade20k'''      ,split='''test'''    )
   __lowercase													=				Image.open(ds[0]['''file''']    )
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )
   __lowercase													=				outputs.logits

   # verify the logits
   __lowercase													=				torch.Size((1, 1_5_0, 1_6_0, 1_6_0)    )
   self.assertEqual(logits.shape      ,lowercase__    )

   __lowercase													=				version.parse(PIL.__version__    ) < version.parse('''9.0.0'''    )

   if is_pillow_less_than_a:
    __lowercase													=				torch.tensor(
        [
            [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
            [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
            [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
        ]      ,device=lowercase__      ,)
   else:
    __lowercase													=				torch.tensor(
        [
            [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
            [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
            [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
        ]      ,device=lowercase__      ,)

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






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''    )
   __lowercase													=				model.to(lowercase__    )

   __lowercase													=				BeitImageProcessor(do_resize=lowercase__      ,size=6_4_0      ,do_center_crop=lowercase__    )

   __lowercase													=				load_dataset('''hf-internal-testing/fixtures_ade20k'''      ,split='''test'''    )
   __lowercase													=				Image.open(ds[0]['''file''']    )
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )

   __lowercase													=				outputs.logits.detach().cpu()

   __lowercase													=				image_processor.post_process_semantic_segmentation(outputs=lowercase__      ,target_sizes=[(5_0_0, 3_0_0)]    )
   __lowercase													=				torch.Size((5_0_0, 3_0_0)    )
   self.assertEqual(segmentation[0].shape      ,lowercase__    )

   __lowercase													=				image_processor.post_process_semantic_segmentation(outputs=lowercase__    )
   __lowercase													=				torch.Size((1_6_0, 1_6_0)    )
   self.assertEqual(segmentation[0].shape      ,lowercase__    )




'''simple docstring'''






import random
from typing import Any


def 	_A  (					A__   ):






 """simple docstring"""



 for _ in range(len(A__   )   ):
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase						,			__lowercase													=				data[b], data[a]
 return data


if __name__ == "__main__":
 lowerCAmelCase__      		=			[0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase__      		=			['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

