"""simple docstring"""





import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 (
           FocalNetBackbone,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetModel,
       )
       from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=3_2	,    a=2	,    a=3	,    a=1_6	,    a=[3_2, 6_4, 1_2_8]	,    a=[1, 2, 1]	,    a=[2, 2, 4]	,    a=2	,    a=2.0	,    a=True	,    a=0.0	,    a=0.0	,    a=0.1	,    a="gelu"	,    a=False	,    a=True	,    a=0.02	,    a=1e-5	,    a=True	,    a=None	,    a=True	,    a=1_0	,    a=8	,    a=["stage1", "stage2"]	,    a=[1, 2]	,    ) ->      List[str]:
             lowercase__    :	str     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	List[Any]     					=			image_size
             lowercase__    :	Tuple     					=			patch_size
             lowercase__    :	Optional[int]     					=			num_channels
             lowercase__    :	List[Any]     					=			embed_dim
             lowercase__    :	Dict     					=			hidden_sizes
             lowercase__    :	List[str]     					=			depths
             lowercase__    :	Any     					=			num_heads
             lowercase__    :	Tuple     					=			window_size
             lowercase__    :	int     					=			mlp_ratio
             lowercase__    :	Union[str, Any]     					=			qkv_bias
             lowercase__    :	str     					=			hidden_dropout_prob
             lowercase__    :	str     					=			attention_probs_dropout_prob
             lowercase__    :	Tuple     					=			drop_path_rate
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Union[str, Any]     					=			use_absolute_embeddings
             lowercase__    :	str     					=			patch_norm
             lowercase__    :	Tuple     					=			layer_norm_eps
             lowercase__    :	Optional[Any]     					=			initializer_range
             lowercase__    :	List[Any]     					=			is_training
             lowercase__    :	Any     					=			scope
             lowercase__    :	List[str]     					=			use_labels
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	List[Any]     					=			encoder_stride
             lowercase__    :	Tuple     					=			out_features
             lowercase__    :	int     					=			out_indices


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	List[str]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__    :	Dict     					=			None
             if self.use_labels:
                    lowercase__    :	Tuple     					=			ids_tensor([self.batch_size]	,    self.type_sequence_label_size							)

             lowercase__    :	str     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      Any:
             return FocalNetConfig(
                 image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    embed_dim=self.embed_dim	,    hidden_sizes=self.hidden_sizes	,    depths=self.depths	,    num_heads=self.num_heads	,    window_size=self.window_size	,    mlp_ratio=self.mlp_ratio	,    qkv_bias=self.qkv_bias	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    drop_path_rate=self.drop_path_rate	,    hidden_act=self.hidden_act	,    use_absolute_embeddings=self.use_absolute_embeddings	,    path_norm=self.patch_norm	,    layer_norm_eps=self.layer_norm_eps	,    initializer_range=self.initializer_range	,    encoder_stride=self.encoder_stride	,    out_features=self.out_features	,    out_indices=self.out_indices	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Any:
             lowercase__    :	Union[str, Any]     					=			FocalNetModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Dict     					=			model(a							)

             lowercase__    :	Optional[Any]     					=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
             lowercase__    :	str     					=			int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

             self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, expected_seq_len, expected_dim)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Tuple:
             lowercase__    :	Tuple     					=			FocalNetBackbone(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Optional[Any]     					=			model(a							)

             # verify feature maps
             self.parent.assertEqual(len(result.feature_maps							)	,    len(config.out_features							)							)
             self.parent.assertListEqual(list(result.feature_maps[0].shape							)	,    [self.batch_size, self.image_size, 8, 8]							)

             # verify channels
             self.parent.assertEqual(len(model.channels							)	,    len(config.out_features							)							)
             self.parent.assertListEqual(model.channels	,    config.hidden_sizes[:-1]							)

             # verify backbone works with out_features=None
             lowercase__    :	str     					=			None
             lowercase__    :	List[str]     					=			FocalNetBackbone(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Optional[int]     					=			model(a							)

             # verify feature maps
             self.parent.assertEqual(len(result.feature_maps							)	,    1							)
             self.parent.assertListEqual(list(result.feature_maps[0].shape							)	,    [self.batch_size, self.image_size * 2, 4, 4]							)

             # verify channels
             self.parent.assertEqual(len(model.channels							)	,    1							)
             self.parent.assertListEqual(model.channels	,    [config.hidden_sizes[-1]]							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Dict     					=			FocalNetForMaskedImageModeling(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Optional[int]     					=			model(a							)
             self.parent.assertEqual(
                 result.reconstruction.shape	,    (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

             # test greyscale images
             lowercase__    :	int     					=			1
             lowercase__    :	Tuple     					=			FocalNetForMaskedImageModeling(a							)
             model.to(a							)
             model.eval()

             lowercase__    :	Tuple     					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__    :	Any     					=			model(a							)
             self.parent.assertEqual(result.reconstruction.shape	,    (self.batch_size, 1, self.image_size, self.image_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Optional[Any]     					=			self.type_sequence_label_size
             lowercase__    :	List[str]     					=			FocalNetForImageClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Any     					=			model(a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)							)

             # test greyscale images
             lowercase__    :	Optional[int]     					=			1
             lowercase__    :	Optional[Any]     					=			FocalNetForImageClassification(a							)
             model.to(a							)
             model.eval()

             lowercase__    :	int     					=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__    :	Dict     					=			model(a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Dict     					=			self.prepare_config_and_inputs()

             lowercase__					,   lowercase__					,   lowercase__    :	Optional[int]     					=			config_and_inputs
             lowercase__    :	int     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[Any]										=						(
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase__	:					Optional[Any]										=						(
          {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCamelCase__	:					Dict										=						False

      lowerCamelCase__	:					Tuple										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					int										=						False


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	List[Any]     					=			FocalNetModelTester(self							)
             lowercase__    :	List[str]     					=			ConfigTester(self	,    config_class=a	,    embed_dim=3_7	,    has_text_modality=a							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             self.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             return


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Tuple     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*a							)


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*a							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	List[Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a							)


      @unittest.skip(reason='FocalNet does not use inputs_embeds'							)
      def 	_UpperCAmelCase (     self							) ->      str:
             pass


      @unittest.skip(reason='FocalNet does not use feedforward chunking'							)
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes[:-1]:
                    lowercase__    :	Optional[Any]     					=			model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)							)
                    lowercase__    :	Dict     					=			model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a	,    nn.Linear							)							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__					,   lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes[:-1]:
                    lowercase__    :	Dict     					=			model_class(a							)
                    lowercase__    :	Any     					=			inspect.signature(model.forward							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__    :	Dict     					=			[*signature.parameters.keys()]

                    lowercase__    :	Optional[Any]     					=			['pixel_values']
                    self.assertListEqual(arg_names[:1]	,    a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a							) ->      Union[str, Any]:
             lowercase__    :	List[str]     					=			model_class(a							)
             model.to(a							)
             model.eval()

             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**self._prepare_for_class(a	,    a							)							)

             lowercase__    :	Tuple     					=			outputs.hidden_states

             lowercase__    :	Tuple     					=			getattr(
                 self.model_tester	,    'expected_num_hidden_layers'	,    len(self.model_tester.depths							) + 1							)
             self.assertEqual(len(a							)	,    a							)

             # FocalNet has a different seq_length
             lowercase__    :	List[Any]     					=			(
                 config.patch_size
                 if isinstance(config.patch_size	,    collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__    :	List[Any]     					=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]							)	,    [num_patches, self.model_tester.embed_dim]	,    )

             lowercase__    :	int     					=			outputs.reshaped_hidden_states
             self.assertEqual(len(a							)	,    a							)

             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Dict     					=			reshaped_hidden_states[0].shape
             lowercase__    :	str     					=			(
                 reshaped_hidden_states[0].view(a	,    a	,    height * width							).permute(0	,    2	,    1							)
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-2:]							)	,    [num_patches, self.model_tester.embed_dim]	,    )


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__    :	List[Any]     					=			(
                 self.model_tester.image_size
                 if isinstance(self.model_tester.image_size	,    collections.abc.Iterable							)
                 else (self.model_tester.image_size, self.model_tester.image_size)
             )

             for model_class in self.all_model_classes[:-1]:
                    lowercase__    :	Tuple     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	str     					=			True

                    self.check_hidden_states_output(a	,    a	,    a	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	List[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	str     					=			3

             lowercase__    :	Any     					=			(
                 self.model_tester.image_size
                 if isinstance(self.model_tester.image_size	,    collections.abc.Iterable							)
                 else (self.model_tester.image_size, self.model_tester.image_size)
             )
             lowercase__    :	str     					=			(
                 config.patch_size
                 if isinstance(config.patch_size	,    collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__    :	Optional[int]     					=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             lowercase__    :	int     					=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

             for model_class in self.all_model_classes[:-1]:
                    lowercase__    :	List[Any]     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    (padded_height, padded_width)							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	str     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    (padded_height, padded_width)							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	List[str]     					=			FocalNetModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__    :	Any     					=			_config_zero_init(a							)
             for model_class in self.all_model_classes:
                    lowercase__    :	Optional[Any]     					=			model_class(config=a							)
                    for name, param in model.named_parameters():
                           if "embeddings" not in name and 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"""	,    )





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


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             # TODO update organization
             return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'							) if is_vision_available() else None


      @slow
      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Tuple     					=			FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'							).to(a							)
             lowercase__    :	str     					=			self.default_image_processor

             lowercase__    :	Optional[int]     					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             lowercase__    :	int     					=			image_processor(images=a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	Optional[int]     					=			model(**a							)

             # verify the logits
             lowercase__    :	List[str]     					=			torch.Size((1, 1_0_0_0)							)
             self.assertEqual(outputs.logits.shape	,    a							)
             lowercase__    :	List[Any]     					=			torch.tensor([0.2_166, -0.4_368, 0.2_191]							).to(a							)
             self.assertTrue(torch.allclose(outputs.logits[0, :3]	,    a	,    atol=1e-4							)							)
             self.assertTrue(outputs.logits.argmax(dim=-1							).item()	,    2_8_1							)





@require_torch
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						(FocalNetBackbone,) if is_torch_available() else ()
      lowerCamelCase__	:					Any										=						FocalNetConfig

      lowerCamelCase__	:					Optional[int]										=						False


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FocalNetModelTester(self							)


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


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


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

