code: 
  
  
  import inspect
  import unittest
  
  from transformers import ResNetConfig
  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 torch import nn
  
        from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
        from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
        from PIL import Image
  
        from transformers import AutoImageProcessor
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=32    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=10    ,  SCREAMING_SNAKE_CASE_=[10, 20, 30, 40]    ,  SCREAMING_SNAKE_CASE_=[1, 1, 2, 1]    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_="relu"    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=["stage2", "stage3", "stage4"]    ,  SCREAMING_SNAKE_CASE_=[2, 3, 4]    ,  )       ->  int:
           __lowerCamelCase     :			Dict              =	parent
           __lowerCamelCase     :			List[str]              =	batch_size
           __lowerCamelCase     :			List[str]              =	image_size
           __lowerCamelCase     :			Optional[int]              =	num_channels
           __lowerCamelCase     :			Union[str, Any]              =	embeddings_size
           __lowerCamelCase     :			Optional[int]              =	hidden_sizes
           __lowerCamelCase     :			Optional[Any]              =	depths
           __lowerCamelCase     :			List[str]              =	is_training
           __lowerCamelCase     :			List[str]              =	use_labels
           __lowerCamelCase     :			str              =	hidden_act
           __lowerCamelCase     :			List[str]              =	num_labels
           __lowerCamelCase     :			Any              =	scope
           __lowerCamelCase     :			str              =	len(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	out_features
           __lowerCamelCase     :			List[Any]              =	out_indices
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase     :			List[Any]              =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
           __lowerCamelCase     :			Any              =	None
           if self.use_labels:
                __lowerCamelCase     :			Optional[int]              =	ids_tensor([self.batch_size]    ,  self.num_labels			)
  
           __lowerCamelCase     :			Dict              =	self.get_config()
  
           return config, pixel_values, labels
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           return ResNetConfig(
               num_channels=self.num_channels    ,  embeddings_size=self.embeddings_size    ,  hidden_sizes=self.hidden_sizes    ,  depths=self.depths    ,  hidden_act=self.hidden_act    ,  num_labels=self.num_labels    ,  out_features=self.out_features    ,  out_indices=self.out_indices    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
           __lowerCamelCase     :			Dict              =	ResNetModel(config=SCREAMING_SNAKE_CASE_			)
           model.to(SCREAMING_SNAKE_CASE_			)
           model.eval()
           __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_			)
           # 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 // 32, self.image_size // 32)    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Any:
           __lowerCamelCase     :			Union[str, Any]              =	self.num_labels
           __lowerCamelCase     :			Any              =	ResNetForImageClassification(SCREAMING_SNAKE_CASE_			)
           model.to(SCREAMING_SNAKE_CASE_			)
           model.eval()
           __lowerCamelCase     :			Any              =	model(SCREAMING_SNAKE_CASE_    ,  labels=SCREAMING_SNAKE_CASE_			)
           self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           __lowerCamelCase     :			Dict              =	ResNetBackbone(config=SCREAMING_SNAKE_CASE_			)
           model.to(SCREAMING_SNAKE_CASE_			)
           model.eval()
           __lowerCamelCase     :			int              =	model(SCREAMING_SNAKE_CASE_			)
  
           # 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.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
           __lowerCamelCase     :			Tuple              =	None
           __lowerCamelCase     :			Optional[int]              =	ResNetBackbone(config=SCREAMING_SNAKE_CASE_			)
           model.to(SCREAMING_SNAKE_CASE_			)
           model.eval()
           __lowerCamelCase     :			Optional[int]              =	model(SCREAMING_SNAKE_CASE_			)
  
           # 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			)       ->  str:
           __lowerCamelCase     :			str              =	self.prepare_config_and_inputs()
           __lowerCamelCase     :			Optional[int]              =	config_and_inputs
           __lowerCamelCase     :			Any              =	{'pixel_values': pixel_values}
           return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    (
          (
              ResNetModel,
              ResNetForImageClassification,
              ResNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase						:      List[Any]							  =    (
          {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
          if is_torch_available()
          else {}
      )
  
      lowerCamelCase						:      str							  =    True
      lowerCamelCase						:      Tuple							  =    False
      lowerCamelCase						:      List[str]							  =    False
      lowerCamelCase						:      Any							  =    False
      lowerCamelCase						:      List[str]							  =    False
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Optional[Any]              =	ResNetModelTester(self			)
           __lowerCamelCase     :			Tuple              =	ConfigTester(self    ,  config_class=SCREAMING_SNAKE_CASE_    ,  has_text_modality=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  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    lowercase_       (							self			)       ->  Tuple:
           return
  
  
  
  
  
  
  
      @unittest.skip(reason='ResNet does not use inputs_embeds'			)
      def    lowercase_       (							self			)       ->  Dict:
           pass
  
  
  
  
  
  
  
      @unittest.skip(reason='ResNet does not support input and output embeddings'			)
      def    lowercase_       (							self			)       ->  List[Any]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase     :			Union[str, Any]              =	self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			str              =	model_class(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Optional[Any]              =	inspect.signature(model.forward			)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                __lowerCamelCase     :			Union[str, Any]              =	[*signature.parameters.keys()]
  
                __lowerCamelCase     :			List[str]              =	['pixel_values']
                self.assertListEqual(arg_names[:1]    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			str              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			List[str]              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			Union[str, Any]              =	self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			Any              =	model_class(config=SCREAMING_SNAKE_CASE_			)
                for name, module in model.named_modules():
                     if isinstance(SCREAMING_SNAKE_CASE_    ,  (nn.BatchNormad, nn.GroupNorm)			):
                          self.assertTrue(
                              torch.all(module.weight == 1			)    ,  msg=f'Parameter {name} of model {model_class} seems not properly initialized'    ,  )
                          self.assertTrue(
                              torch.all(module.bias == 0			)    ,  msg=f'Parameter {name} of model {model_class} seems not properly initialized'    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           def check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase     :			Optional[Any]              =	model_class(SCREAMING_SNAKE_CASE_			)
                model.to(SCREAMING_SNAKE_CASE_			)
                model.eval()
  
                with torch.no_grad():
                     __lowerCamelCase     :			int              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)
  
                __lowerCamelCase     :			Any              =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
                __lowerCamelCase     :			Union[str, Any]              =	self.model_tester.num_stages
                self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  expected_num_stages + 1			)
  
                # ResNet'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]    ,  )
  
           __lowerCamelCase     :			Dict              =	self.model_tester.prepare_config_and_inputs_for_common()
           __lowerCamelCase     :			List[Any]              =	['basic', 'bottleneck']
           for model_class in self.all_model_classes:
                for layer_type in layers_type:
                     __lowerCamelCase     :			List[str]              =	layer_type
                     __lowerCamelCase     :			List[str]              =	True
                     check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     __lowerCamelCase     :			Optional[int]              =	True
  
                     check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @unittest.skip(reason='ResNet does not use feedforward chunking'			)
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			Dict              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  Dict:
           for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCamelCase     :			Tuple              =	ResNetModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[Any]:
       __lowerCamelCase     :			int              =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       return image
  
  
  
  
  
  @require_torch
  @require_vision
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  int:
           return (
               AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
               if is_vision_available()
               else None
           )
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase     :			Any              =	ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Dict              =	self.default_image_processor
           __lowerCamelCase     :			Optional[int]              =	prepare_img()
           __lowerCamelCase     :			Any              =	image_processor(images=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)
  
           # forward pass
           with torch.no_grad():
                __lowerCamelCase     :			Tuple              =	model(**SCREAMING_SNAKE_CASE_			)
  
           # verify the logits
           __lowerCamelCase     :			Optional[int]              =	torch.Size((1, 10_00)			)
           self.assertEqual(outputs.logits.shape    ,  SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Dict              =	torch.tensor([-11.10_69, -9.7_8_7_7, -8.3_7_7_7]			).to(SCREAMING_SNAKE_CASE_			)
  
           self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  SCREAMING_SNAKE_CASE_    ,  atol=1E-4			)			)
  
  
  
  
  
  
  
  @require_torch
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      str							  =    (ResNetBackbone,) if is_torch_available() else ()
      lowerCamelCase						:      Any							  =    False
      lowerCamelCase						:      Union[str, Any]							  =    ResNetConfig
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			str              =	ResNetModelTester(self			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  