code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
     import tensorflow as tf
  
     from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  
  @require_tf
  class 							_A :
      """simple docstring"""
  
      lowerCamelCase  :       List[Any]   =   MarianConfig
      lowerCamelCase  :       Optional[Any]   =   {}
      lowerCamelCase  :       Union[str, Any]   =   'gelu'
  
  
  
      def __init__(							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any=13   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=7   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=False   ,    __SCREAMING_SNAKE_CASE			:    int=99   ,    __SCREAMING_SNAKE_CASE			:    int=32   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=2   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=4   ,    __SCREAMING_SNAKE_CASE			:    int=37   ,    __SCREAMING_SNAKE_CASE			:    Dict=0.1   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=0.1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=20   ,    __SCREAMING_SNAKE_CASE			:    str=2   ,    __SCREAMING_SNAKE_CASE			:    List[str]=1   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=0   ,    )			->       Optional[int]:
          __UpperCAmelCase    							=parent
          __UpperCAmelCase    							=batch_size
          __UpperCAmelCase    							=seq_length
          __UpperCAmelCase    							=is_training
          __UpperCAmelCase    							=use_labels
          __UpperCAmelCase    							=vocab_size
          __UpperCAmelCase    							=hidden_size
          __UpperCAmelCase    							=num_hidden_layers
          __UpperCAmelCase    							=num_attention_heads
          __UpperCAmelCase    							=intermediate_size
  
          __UpperCAmelCase    							=hidden_dropout_prob
          __UpperCAmelCase    							=attention_probs_dropout_prob
          __UpperCAmelCase    							=max_position_embeddings
          __UpperCAmelCase    							=eos_token_id
          __UpperCAmelCase    							=pad_token_id
          __UpperCAmelCase    							=bos_token_id
  
  
  
      def _a       (							self			:    Any			)			->       int:
          __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length - 1]   ,    self.vocab_size			)
          __UpperCAmelCase    							=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)   ,    1			)
          __UpperCAmelCase    							=tf.concat([input_ids, eos_tensor]   ,    axis=1			)
  
          __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)
  
          __UpperCAmelCase    							=self.config_cls(
              vocab_size=self.vocab_size   ,    d_model=self.hidden_size   ,    encoder_layers=self.num_hidden_layers   ,    decoder_layers=self.num_hidden_layers   ,    encoder_attention_heads=self.num_attention_heads   ,    decoder_attention_heads=self.num_attention_heads   ,    encoder_ffn_dim=self.intermediate_size   ,    decoder_ffn_dim=self.intermediate_size   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    eos_token_ids=[2]   ,    bos_token_id=self.bos_token_id   ,    pad_token_id=self.pad_token_id   ,    decoder_start_token_id=self.pad_token_id   ,    **self.config_updates   ,    )
          __UpperCAmelCase    							=prepare_marian_inputs_dict(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
          return config, inputs_dict
  
  
  
      def _a       (							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Tuple:
          __UpperCAmelCase    							=TFMarianModel(config=__SCREAMING_SNAKE_CASE			).get_decoder()
          __UpperCAmelCase    							=inputs_dict["""input_ids"""]
  
          __UpperCAmelCase    							=input_ids[:1, :]
          __UpperCAmelCase    							=inputs_dict["""attention_mask"""][:1, :]
          __UpperCAmelCase    							=inputs_dict["""head_mask"""]
          __UpperCAmelCase    							=1
  
          # first forward pass
          __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    head_mask=__SCREAMING_SNAKE_CASE   ,    use_cache=__SCREAMING_SNAKE_CASE			)
  
          __UpperCAmelCase							,   __UpperCAmelCase    							=outputs.to_tuple()
  
          # create hypothetical next token and extent to next_input_ids
          __UpperCAmelCase    							=ids_tensor((self.batch_size, 3)   ,    config.vocab_size			)
          __UpperCAmelCase    							=tf.cast(ids_tensor((self.batch_size, 3)   ,    2			)   ,    tf.inta			)
  
          # append to next input_ids and
          __UpperCAmelCase    							=tf.concat([input_ids, next_tokens]   ,    axis=-1			)
          __UpperCAmelCase    							=tf.concat([attention_mask, next_attn_mask]   ,    axis=-1			)
  
          __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE			)[0]
          __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    past_key_values=__SCREAMING_SNAKE_CASE			)[0]
  
          self.parent.assertEqual(next_tokens.shape[1]   ,    output_from_past.shape[1]			)
  
          # select random slice
          __UpperCAmelCase    							=int(ids_tensor((1,)   ,    output_from_past.shape[-1]			)			)
          __UpperCAmelCase    							=output_from_no_past[:, -3:, random_slice_idx]
          __UpperCAmelCase    							=output_from_past[:, :, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    rtol=1e-3			)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Any     ,			A_:	Dict     ,			A_:	List[str]     ,			A_:	Union[str, Any]=None     ,			A_:	Tuple=None     ,			A_:	Union[str, Any]=None     ,			A_:	Tuple=None     ,			A_:	Dict=None     ,			)  ->							Optional[int]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      if attention_mask is None:
          __UpperCAmelCase    							=tf.cast(tf.math.not_equal(A_     ,			config.pad_token_id		)     ,			tf.inta		)
      if decoder_attention_mask is None:
          __UpperCAmelCase    							=tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape     ,			dtype=tf.inta		),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,			config.pad_token_id		)     ,			tf.inta		),
              ]     ,			axis=-1     ,			)
      if head_mask is None:
          __UpperCAmelCase    							=tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
          __UpperCAmelCase    							=tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
          __UpperCAmelCase    							=tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }
  
  
  
  @require_tf
  class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      lowerCamelCase  :       Optional[int]   =   (TFMarianMTModel,) if is_tf_available() else ()
      lowerCamelCase  :       Optional[Any]   =   (
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      lowerCamelCase  :       Tuple   =   True
      lowerCamelCase  :       int   =   False
      lowerCamelCase  :       str   =   False
  
  
  
      def _a       (							self			:    str			)			->       Dict:
          __UpperCAmelCase    							=TFMarianModelTester(self			)
          __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[Any]			)			->       List[str]:
          self.config_tester.run_common_tests()
  
  
  
      def _a       (							self			:    Dict			)			->       Any:
          __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE			)
  
  
  
  
  @require_tf
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Dict   =   1_000  # show more chars for failing integration tests
  
  
  
      @classmethod
      def _a       (							cls			:    Dict			)			->       None:
          __UpperCAmelCase    							=f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
          return cls
  
  
  
      @cached_property
      def _a       (							self			:    Tuple			)			->       MarianTokenizer:
          return AutoTokenizer.from_pretrained(self.model_name			)
  
  
  
      @property
      def _a       (							self			:    Dict			)			->       int:
          return self.tokenizer.eos_token_id
  
  
  
      @cached_property
      def _a       (							self			:    Any			)			->       List[str]:
          warnings.simplefilter("""error"""			)
          __UpperCAmelCase    							=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
          assert isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
          __UpperCAmelCase    							=model.config
          self.assertListEqual(c.bad_words_ids   ,    [[c.pad_token_id]]			)
          self.assertEqual(c.max_length   ,    512			)
          self.assertEqual(c.decoder_start_token_id   ,    c.pad_token_id			)
          return model
  
  
  
      def _a       (							self			:    Union[str, Any]   ,    **__SCREAMING_SNAKE_CASE			:    List[Any]			)			->       Union[str, Any]:
          __UpperCAmelCase    							=self.translate_src_text(**__SCREAMING_SNAKE_CASE			)
          self.assertListEqual(self.expected_text   ,    __SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    str   ,    **__SCREAMING_SNAKE_CASE			:    List[str]			)			->       List[Any]:
          __UpperCAmelCase    							=self.tokenizer(self.src_text   ,    **__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""tf"""			)
          __UpperCAmelCase    							=self.model.generate(
              model_inputs.input_ids   ,    attention_mask=model_inputs.attention_mask   ,    num_beams=2   ,    max_length=128			)
          __UpperCAmelCase    							=self.tokenizer.batch_decode(generated_ids.numpy()   ,    skip_special_tokens=__SCREAMING_SNAKE_CASE			)
          return generated_words
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       int   =   'mt'
      lowerCamelCase  :       Optional[int]   =   'en'
      lowerCamelCase  :       Optional[Any]   =   ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      lowerCamelCase  :       Dict   =   ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    Dict			)			->       int:
          self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[Any]   =   'en'
      lowerCamelCase  :       Dict   =   'zh'
      lowerCamelCase  :       Optional[Any]   =   ['My name is Wolfgang and I live in Berlin']
      lowerCamelCase  :       Dict   =   ['我叫沃尔夫冈 我住在柏林']
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    Optional[int]			)			->       int:
          self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Dict   =   'en'
      lowerCamelCase  :       Any   =   'ROMANCE'
      lowerCamelCase  :       Union[str, Any]   =   [
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      lowerCamelCase  :       int   =   [
          'Ne passez pas autant de temps à regarder la télé.',
          'A sua mensagem foi enviada.',
          'Es dos años más viejo que yo.',
      ]
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    Optional[int]			)			->       List[Any]:
          self._assert_generated_batch_equal_expected()
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    str			)			->       Any:
          __UpperCAmelCase    							=TranslationPipeline(self.model   ,    self.tokenizer   ,    framework="""tf"""			)
          __UpperCAmelCase    							=pipeline(self.src_text			)
          self.assertEqual(self.expected_text   ,    [x["""translation_text"""] for x in output]			)
  
  
  
  
  
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
     )
  
  
  
  
  