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 							_A :
      """simple docstring"""
  
      def __init__(							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple=3   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=32   ,    __SCREAMING_SNAKE_CASE			:    str=3   ,    __SCREAMING_SNAKE_CASE			:    List[str]=10   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=[10, 20, 30, 40]   ,    __SCREAMING_SNAKE_CASE			:    Any=[1, 1, 2, 1]   ,    __SCREAMING_SNAKE_CASE			:    List[str]=True   ,    __SCREAMING_SNAKE_CASE			:    Any=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]="relu"   ,    __SCREAMING_SNAKE_CASE			:    Dict=3   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=None   ,    __SCREAMING_SNAKE_CASE			:    Tuple=["stage2", "stage3", "stage4"]   ,    __SCREAMING_SNAKE_CASE			:    Tuple=[2, 3, 4]   ,    )			->       Union[str, Any]:
          __UpperCAmelCase    							=parent
          __UpperCAmelCase    							=batch_size
          __UpperCAmelCase    							=image_size
          __UpperCAmelCase    							=num_channels
          __UpperCAmelCase    							=embeddings_size
          __UpperCAmelCase    							=hidden_sizes
          __UpperCAmelCase    							=depths
          __UpperCAmelCase    							=is_training
          __UpperCAmelCase    							=use_labels
          __UpperCAmelCase    							=hidden_act
          __UpperCAmelCase    							=num_labels
          __UpperCAmelCase    							=scope
          __UpperCAmelCase    							=len(__SCREAMING_SNAKE_CASE			)
          __UpperCAmelCase    							=out_features
          __UpperCAmelCase    							=out_indices
  
  
  
      def _a       (							self			:    int			)			->       List[str]:
          __UpperCAmelCase    							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
          __UpperCAmelCase    							=None
          if self.use_labels:
              __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.num_labels			)
  
          __UpperCAmelCase    							=self.get_config()
  
          return config, pixel_values, labels
  
  
  
      def _a       (							self			:    int			)			->       List[str]:
          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 _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       str:
          __UpperCAmelCase    							=ResNetModel(config=__SCREAMING_SNAKE_CASE			)
          model.to(__SCREAMING_SNAKE_CASE			)
          model.eval()
          __UpperCAmelCase    							=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 _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any			)			->       Dict:
          __UpperCAmelCase    							=self.num_labels
          __UpperCAmelCase    							=ResNetForImageClassification(__SCREAMING_SNAKE_CASE			)
          model.to(__SCREAMING_SNAKE_CASE			)
          model.eval()
          __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
          self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)			)
  
  
  
      def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       int:
          __UpperCAmelCase    							=ResNetBackbone(config=__SCREAMING_SNAKE_CASE			)
          model.to(__SCREAMING_SNAKE_CASE			)
          model.eval()
          __UpperCAmelCase    							=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
          __UpperCAmelCase    							=None
          __UpperCAmelCase    							=ResNetBackbone(config=__SCREAMING_SNAKE_CASE			)
          model.to(__SCREAMING_SNAKE_CASE			)
          model.eval()
          __UpperCAmelCase    							=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 _a       (							self			:    Tuple			)			->       List[str]:
          __UpperCAmelCase    							=self.prepare_config_and_inputs()
          __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase    							=config_and_inputs
          __UpperCAmelCase    							={"""pixel_values""": pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_torch
  class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       int   =   (
          (
              ResNetModel,
              ResNetForImageClassification,
              ResNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase  :       int   =   (
          {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
          if is_torch_available()
          else {}
      )
  
      lowerCamelCase  :       Union[str, Any]   =   True
      lowerCamelCase  :       Any   =   False
      lowerCamelCase  :       List[str]   =   False
      lowerCamelCase  :       Any   =   False
      lowerCamelCase  :       List[Any]   =   False
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Dict:
          __UpperCAmelCase    							=ResNetModelTester(self			)
          __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE   ,    has_text_modality=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       Dict:
          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 _a       (							self			:    Tuple			)			->       Optional[Any]:
          return
  
  
  
      @unittest.skip(reason="""ResNet does not use inputs_embeds"""			)
      def _a       (							self			:    str			)			->       Optional[Any]:
          pass
  
  
  
      @unittest.skip(reason="""ResNet does not support input and output embeddings"""			)
      def _a       (							self			:    Any			)			->       Optional[int]:
          pass
  
  
  
      def _a       (							self			:    Any			)			->       Any:
          __UpperCAmelCase							,   __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              __UpperCAmelCase    							=model_class(__SCREAMING_SNAKE_CASE			)
              __UpperCAmelCase    							=inspect.signature(model.forward			)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __UpperCAmelCase    							=[*signature.parameters.keys()]
  
              __UpperCAmelCase    							=["""pixel_values"""]
              self.assertListEqual(arg_names[:1]   ,    __SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Tuple			)			->       Any:
          __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    str			)			->       str:
          __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Dict			)			->       Tuple:
          __UpperCAmelCase							,   __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              __UpperCAmelCase    							=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 _a       (							self			:    Dict			)			->       Optional[Any]:
          def check_hidden_states_output(__SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[Any]			):
              __UpperCAmelCase    							=model_class(__SCREAMING_SNAKE_CASE			)
              model.to(__SCREAMING_SNAKE_CASE			)
              model.eval()
  
              with torch.no_grad():
                  __UpperCAmelCase    							=model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)			)
  
              __UpperCAmelCase    							=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
              __UpperCAmelCase    							=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]   ,    )
  
          __UpperCAmelCase							,   __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCAmelCase    							=["""basic""", """bottleneck"""]
          for model_class in self.all_model_classes:
              for layer_type in layers_type:
                  __UpperCAmelCase    							=layer_type
                  __UpperCAmelCase    							=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"]
                  __UpperCAmelCase    							=True
  
                  check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
  
  
      @unittest.skip(reason="""ResNet does not use feedforward chunking"""			)
      def _a       (							self			:    Optional[int]			)			->       int:
          pass
  
  
  
      def _a       (							self			:    Tuple			)			->       List[str]:
          __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE			)
  
  
  
      @slow
      def _a       (							self			:    Dict			)			->       Union[str, Any]:
          for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCAmelCase    							=ResNetModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
              self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Union[str, Any]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      __UpperCAmelCase    							=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
      return image
  
  
  
  @require_torch
  @require_vision
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      @cached_property
      def _a       (							self			:    Optional[int]			)			->       List[str]:
          return (
              AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
              if is_vision_available()
              else None
          )
  
  
  
      @slow
      def _a       (							self			:    List[Any]			)			->       Dict:
          __UpperCAmelCase    							=ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(__SCREAMING_SNAKE_CASE			)
  
          __UpperCAmelCase    							=self.default_image_processor
          __UpperCAmelCase    							=prepare_img()
          __UpperCAmelCase    							=image_processor(images=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).to(__SCREAMING_SNAKE_CASE			)
  
          # forward pass
          with torch.no_grad():
              __UpperCAmelCase    							=model(**__SCREAMING_SNAKE_CASE			)
  
          # verify the logits
          __UpperCAmelCase    							=torch.Size((1, 1000)			)
          self.assertEqual(outputs.logits.shape   ,    __SCREAMING_SNAKE_CASE			)
  
          __UpperCAmelCase    							=torch.tensor([-11.1_069, -9.7_877, -8.3_777]			).to(__SCREAMING_SNAKE_CASE			)
  
          self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-4			)			)
  
  
  
  
  @require_torch
  class 							_A (    UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Tuple   =   (ResNetBackbone,) if is_torch_available() else ()
      lowerCamelCase  :       int   =   False
      lowerCamelCase  :       Optional[int]   =   ResNetConfig
  
  
  
      def _a       (							self			:    List[Any]			)			->       str:
          __UpperCAmelCase    							=ResNetModelTester(self			)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import argparse
  import os
  import pathlib
  
  import fairseq
  import torch
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import (
      DataaVecTextConfig,
      DataaVecTextForMaskedLM,
      DataaVecTextForSequenceClassification,
      DataaVecTextModel,
  )
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  
  # IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
  # File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")
  
  
  logging.set_verbosity_info()
  __A  							=  logging.get_logger(__name__)
  
  __A  							=  "Hello world! cécé herlolip"
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
         __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
             A_     ,			checkpoint_file=A_		)
         dataavec.eval()  # disable dropout
         __UpperCAmelCase    							=dataavec.models[0]
         __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
         __UpperCAmelCase    							=DataaVecTextConfig(
             vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,			hidden_size=dataavec_model.args.encoder_embed_dim     ,			num_hidden_layers=dataavec_model.args.encoder_layers     ,			num_attention_heads=dataavec_model.args.encoder_attention_heads     ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,			max_position_embeddings=514     ,			type_vocab_size=1     ,			layer_norm_eps=1e-5     ,			)
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
         print("""Our BERT config:"""     ,			A_		)
  
         __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
         __UpperCAmelCase    							=torch.zeros_like(
             model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                __UpperCAmelCase    							=model.dataavec_text.encoder.layer[i]
                __UpperCAmelCase    							=dataavec_sent_encoder.layers[i]
  
                # self attention
                __UpperCAmelCase    							=layer.attention.self
                assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
  
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias
  
                # self-attention output
                __UpperCAmelCase    							=layer.attention.output
                assert (
                    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
                ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias
  
                # intermediate
                __UpperCAmelCase    							=layer.intermediate
                assert (
                    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
  
                # output
                __UpperCAmelCase    							=layer.output
                assert (
                    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.bias
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.bias
         else:
                # LM Head
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         __UpperCAmelCase    							=dataavec.encode(A_		).unsqueeze(0		)  # batch of size 1
  
         __UpperCAmelCase    							=model(A_		)[0]
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
         else:
                __UpperCAmelCase    							=dataavec_model(A_		)[0]
         print(our_output.shape     ,			their_output.shape		)
         __UpperCAmelCase    							=torch.max(torch.abs(our_output - their_output		)		).item()
         print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
         __UpperCAmelCase    							=torch.allclose(A_     ,			A_     ,			atol=1e-3		)
         print("""Do both models output the same tensors?"""     ,			"""🔥""" if success else """💩"""		)
         if not success:
                raise Exception("""Something went wRoNg"""		)
  
         pathlib.Path(A_		).mkdir(parents=A_     ,			exist_ok=A_		)
         print(F'''Saving model to {pytorch_dump_folder_path}'''		)
         model.save_pretrained(A_		)
  
  
  if __name__ == "__main__":
     __A  							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--classification_head", action="store_true", help="Whether to convert a final classification head."
     )
     __A  							=  parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
  
  
  
  