

import inspect
import unittest

from transformers import ConvNextConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
		from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor







class        a__       :







		"""simple docstring"""




		def __init__(			self    : List[Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : str=1_3				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3				,				UpperCAmelCase__    : Optional[Any]=4				,				UpperCAmelCase__    : Dict=[1_0, 2_0, 3_0, 4_0]				,				UpperCAmelCase__    : int=[2, 2, 3, 2]				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Dict=3_7				,				UpperCAmelCase__    : str="gelu"				,				UpperCAmelCase__    : Dict=1_0				,				UpperCAmelCase__    : List[Any]=0.02				,				UpperCAmelCase__    : Optional[int]=["stage2", "stage3", "stage4"]				,				UpperCAmelCase__    : int=[2, 3, 4]				,				UpperCAmelCase__    : List[Any]=None				,				)					->Optional[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[Any]       =   parent
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   batch_size
								SCREAMING_SNAKE_CASE :      List[str]       =   image_size
								SCREAMING_SNAKE_CASE :      Dict       =   num_channels
								SCREAMING_SNAKE_CASE :      Dict       =   num_stages
								SCREAMING_SNAKE_CASE :      Tuple       =   hidden_sizes
								SCREAMING_SNAKE_CASE :      Any       =   depths
								SCREAMING_SNAKE_CASE :      List[str]       =   is_training
								SCREAMING_SNAKE_CASE :      Dict       =   use_labels
								SCREAMING_SNAKE_CASE :      Optional[int]       =   intermediate_size
								SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_act
								SCREAMING_SNAKE_CASE :      Tuple       =   num_labels
								SCREAMING_SNAKE_CASE :      int       =   initializer_range
								SCREAMING_SNAKE_CASE :      Dict       =   out_features
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   out_indices
								SCREAMING_SNAKE_CASE :      Tuple       =   scope


		def  _lowercase      (			self    : List[str]		)					->Optional[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								SCREAMING_SNAKE_CASE :      Optional[Any]       =   None
								if self.use_labels:
														SCREAMING_SNAKE_CASE :      Tuple       =   ids_tensor([self.batch_size]				,				self.num_labels		)

								SCREAMING_SNAKE_CASE :      List[Any]       =   self.get_config()
								return config, pixel_values, labels


		def  _lowercase      (			self    : Optional[int]		)					->Optional[int]:
								"""simple docstring"""







								return ConvNextConfig(
								    num_channels=self.num_channels				,				hidden_sizes=self.hidden_sizes				,				depths=self.depths				,				num_stages=self.num_stages				,				hidden_act=self.hidden_act				,				is_decoder=UpperCAmelCase__				,				initializer_range=self.initializer_range				,				out_features=self.out_features				,				out_indices=self.out_indices				,				num_labels=self.num_labels				,				)


		def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Union[str, Any]		)					->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      str       =   ConvNextModel(config=UpperCAmelCase__		)
								model.to(UpperCAmelCase__		)
								model.eval()
								SCREAMING_SNAKE_CASE :      str       =   model(UpperCAmelCase__		)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape				,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)				,				)


		def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Tuple		)					->Optional[int]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      int       =   ConvNextForImageClassification(UpperCAmelCase__		)
								model.to(UpperCAmelCase__		)
								model.eval()
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(UpperCAmelCase__				,				labels=UpperCAmelCase__		)
								self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)		)


		def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : List[str]		)					->Optional[int]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      str       =   ConvNextBackbone(config=UpperCAmelCase__		)
								model.to(UpperCAmelCase__		)
								model.eval()
								SCREAMING_SNAKE_CASE :      List[str]       =   model(UpperCAmelCase__		)

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps		)				,				len(config.out_features		)		)
								self.parent.assertListEqual(list(result.feature_maps[0].shape		)				,				[self.batch_size, self.hidden_sizes[1], 4, 4]		)

								# 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
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   None
								SCREAMING_SNAKE_CASE :      int       =   ConvNextBackbone(config=UpperCAmelCase__		)
								model.to(UpperCAmelCase__		)
								model.eval()
								SCREAMING_SNAKE_CASE :      int       =   model(UpperCAmelCase__		)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps		)				,				1		)
								self.parent.assertListEqual(list(result.feature_maps[0].shape		)				,				[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

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




		def  _lowercase      (			self    : Dict		)					->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      List[Any]       =   self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   config_and_inputs
								SCREAMING_SNAKE_CASE :      Tuple       =   {"""pixel_values""": pixel_values}
								return config, inputs_dict







@require_torch
class        a__       (   UpperCAmelCase	,			UpperCAmelCase	,			unittest.TestCase       ):







		"""simple docstring"""




		UpperCAmelCase__      : str			       =(
		    (
		        ConvNextModel,
		        ConvNextForImageClassification,
		        ConvNextBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase__      : List[str]			       =(
		    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase__      : str			       =True
		UpperCAmelCase__      : Optional[Any]			       =False
		UpperCAmelCase__      : int			       =False
		UpperCAmelCase__      : List[Any]			       =False
		UpperCAmelCase__      : int			       =False


		def  _lowercase      (			self    : List[str]		)					->Optional[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      List[Any]       =   ConvNextModelTester(self		)
								SCREAMING_SNAKE_CASE :      Any       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				has_text_modality=UpperCAmelCase__				,				hidden_size=3_7		)


		def  _lowercase      (			self    : str		)					->Optional[Any]:
								"""simple docstring"""







								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  _lowercase      (			self    : int		)					->Optional[int]:
								"""simple docstring"""







								return


		@unittest.skip(reason="""ConvNext does not use inputs_embeds"""		)
		def  _lowercase      (			self    : Optional[int]		)					->List[Any]:
								"""simple docstring"""







								pass


		@unittest.skip(reason="""ConvNext does not support input and output embeddings"""		)
		def  _lowercase      (			self    : Any		)					->Optional[Any]:
								"""simple docstring"""







								pass


		@unittest.skip(reason="""ConvNext does not use feedforward chunking"""		)
		def  _lowercase      (			self    : int		)					->Any:
								"""simple docstring"""







								pass


		def  _lowercase      (			self    : Union[str, Any]		)					->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()

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

														SCREAMING_SNAKE_CASE :      str       =   ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]				,				UpperCAmelCase__		)


		def  _lowercase      (			self    : Optional[int]		)					->List[str]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase__		)


		def  _lowercase      (			self    : Dict		)					->Union[str, Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      int       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*UpperCAmelCase__		)


		def  _lowercase      (			self    : List[Any]		)					->Optional[Any]:
								"""simple docstring"""







								def check_hidden_states_output(UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Union[str, Any]		):
														SCREAMING_SNAKE_CASE :      str       =   model_class(UpperCAmelCase__		)
														model.to(UpperCAmelCase__		)
														model.eval()

														with torch.no_grad():
																				SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(**self._prepare_for_class(UpperCAmelCase__				,				UpperCAmelCase__		)		)

														SCREAMING_SNAKE_CASE :      List[str]       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.model_tester.num_stages
														self.assertEqual(len(UpperCAmelCase__		)				,				expected_num_stages + 1		)

														# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]		)				,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,				)

								SCREAMING_SNAKE_CASE :      Dict       =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE :      Tuple       =   True
														check_hidden_states_output(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														SCREAMING_SNAKE_CASE :      Optional[int]       =   True

														check_hidden_states_output(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)


		def  _lowercase      (			self    : Optional[Any]		)					->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)




		@slow
		def  _lowercase      (			self    : List[Any]		)					->Dict:
								"""simple docstring"""







								for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ConvNextModel.from_pretrained(UpperCAmelCase__		)
														self.assertIsNotNone(UpperCAmelCase__		)






def 					__lowercase      (     )     ->						Tuple:
						SCREAMING_SNAKE_CASE :      Any       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image







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







		"""simple docstring"""




		@cached_property
		def  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
								"""simple docstring"""







								return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""		) if is_vision_available() else None




		@slow
		def  _lowercase      (			self    : Optional[Any]		)					->Union[str, Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      List[Any]       =   ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""		).to(UpperCAmelCase__		)

								SCREAMING_SNAKE_CASE :      str       =   self.default_image_processor
								SCREAMING_SNAKE_CASE :      List[Any]       =   prepare_img()
								SCREAMING_SNAKE_CASE :      Union[str, Any]       =   image_processor(images=UpperCAmelCase__				,				return_tensors="""pt"""		).to(UpperCAmelCase__		)

								# forward pass
								with torch.no_grad():
														SCREAMING_SNAKE_CASE :      Dict       =   model(**UpperCAmelCase__		)

								# verify the logits
								SCREAMING_SNAKE_CASE :      int       =   torch.Size((1, 1_0_0_0)		)
								self.assertEqual(outputs.logits.shape				,				UpperCAmelCase__		)

								SCREAMING_SNAKE_CASE :      Dict       =   torch.tensor([-0.02_60, -0.47_39, 0.19_11]		).to(UpperCAmelCase__		)

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







@require_torch
class        a__       (   unittest.TestCase	,			UpperCAmelCase       ):







		"""simple docstring"""




		UpperCAmelCase__      : int			       =(ConvNextBackbone,) if is_torch_available() else ()
		UpperCAmelCase__      : Optional[Any]			       =ConvNextConfig

		UpperCAmelCase__      : Tuple			       =False


		def  _lowercase      (			self    : List[str]		)					->Tuple:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Tuple       =   ConvNextModelTester(self		)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     List[Any]				    =    {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Union[str, Any]				    =    ["""ViTFeatureExtractor"""]
  UpperCAmelCase__ :     List[Any]				    =    ["""ViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[Any]				    =    [
      """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTForImageClassification""",
      """ViTForMaskedImageModeling""",
      """ViTModel""",
      """ViTPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Tuple				    =    [
      """TFViTForImageClassification""",
      """TFViTModel""",
      """TFViTPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     str				    =    [
      """FlaxViTForImageClassification""",
      """FlaxViTModel""",
      """FlaxViTPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

  UpperCAmelCase__ :     Optional[Any]				    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




