code: 
  
  
  
  
  
  
  import itertools
  import random
  import unittest
  
  import numpy as np
  
  from transformers import EncodecFeatureExtractor
  from transformers.testing_utils import require_torch
  from transformers.utils.import_utils import is_torch_available
  
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
  
  
  if is_torch_available():
     import torch
  
  
  __A  							=  random.Random()
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Union[str, Any]     ,			A_:	Union[str, Any]=1.0     ,			A_:	List[str]=None     ,			A_:	Dict=None		)  ->							Dict:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      if rng is None:
          __UpperCAmelCase    							=global_rng
  
      __UpperCAmelCase    							=[]
      for batch_idx in range(shape[0]		):
          values.append([]		)
          for _ in range(shape[1]		):
              values[-1].append(rng.random() * scale		)
  
      return values
  
  
  
  @require_torch
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    List[str]=7   ,    __SCREAMING_SNAKE_CASE			:    List[str]=400   ,    __SCREAMING_SNAKE_CASE			:    Tuple=2000   ,    __SCREAMING_SNAKE_CASE			:    int=1   ,    __SCREAMING_SNAKE_CASE			:    Any=0.0   ,    __SCREAMING_SNAKE_CASE			:    str=24000   ,    __SCREAMING_SNAKE_CASE			:    Tuple=True   ,    )			->       List[Any]:
          __UpperCAmelCase    							=parent
          __UpperCAmelCase    							=batch_size
          __UpperCAmelCase    							=min_seq_length
          __UpperCAmelCase    							=max_seq_length
          __UpperCAmelCase    							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          __UpperCAmelCase    							=feature_size
          __UpperCAmelCase    							=padding_value
          __UpperCAmelCase    							=sampling_rate
          __UpperCAmelCase    							=return_attention_mask
  
  
  
      def _a       (							self			:    str			)			->       int:
          return {
              "feature_size": self.feature_size,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "return_attention_mask": self.return_attention_mask,
          }
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[str]=False   ,    __SCREAMING_SNAKE_CASE			:    str=False			)			->       List[Any]:
          def _flatten(__SCREAMING_SNAKE_CASE			:    Dict			):
              return list(itertools.chain(*__SCREAMING_SNAKE_CASE			)			)
  
          if equal_length:
              __UpperCAmelCase    							=floats_list((self.batch_size, self.max_seq_length)			)
          else:
              # make sure that inputs increase in size
              __UpperCAmelCase    							=[
                  _flatten(floats_list((x, self.feature_size)			)			)
                  for x in range(self.min_seq_length   ,    self.max_seq_length   ,    self.seq_length_diff			)
              ]
  
          if numpify:
              __UpperCAmelCase    							=[np.asarray(__SCREAMING_SNAKE_CASE			) for x in audio_inputs]
  
          return audio_inputs
  
  
  
  
  @require_torch
  class 							_A (    UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Dict   =   EncodecFeatureExtractor
  
  
  
      def _a       (							self			:    int			)			->       Any:
          __UpperCAmelCase    							=EnCodecFeatureExtractionTester(self			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       str:
          # Tests that all call wrap to encode_plus and batch_encode_plus
          __UpperCAmelCase    							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          # create three inputs of length 800, 1000, and 1200
          __UpperCAmelCase    							=[floats_list((1, x)			)[0] for x in range(800   ,    1400   ,    200			)]
          __UpperCAmelCase    							=[np.asarray(__SCREAMING_SNAKE_CASE			) for audio_input in audio_inputs]
  
          # Test not batched input
          __UpperCAmelCase    							=feat_extract(audio_inputs[0]   ,    return_tensors="""np"""			).input_values
          __UpperCAmelCase    							=feat_extract(np_audio_inputs[0]   ,    return_tensors="""np"""			).input_values
          self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)
  
          # Test batched
          __UpperCAmelCase    							=feat_extract(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""np"""			).input_values
          __UpperCAmelCase    							=feat_extract(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""np"""			).input_values
          for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
              self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)
  
  
  
      def _a       (							self			:    Optional[Any]			)			->       Optional[Any]:
          __UpperCAmelCase    							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          __UpperCAmelCase    							=np.random.rand(100			).astype(np.floataa			)
          __UpperCAmelCase    							=np_audio_inputs.tolist()
  
          for inputs in [py_audio_inputs, np_audio_inputs]:
              __UpperCAmelCase    							=feature_extractor.pad([{"""input_values""": inputs}]   ,    return_tensors="""np"""			)
              self.assertTrue(np_processed.input_values.dtype == np.floataa			)
              __UpperCAmelCase    							=feature_extractor.pad([{"""input_values""": inputs}]   ,    return_tensors="""pt"""			)
              self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)
  
  
  
      def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       Optional[Any]:
          from datasets import load_dataset
  
          __UpperCAmelCase    							=load_dataset("""hf-internal-testing/librispeech_asr_dummy"""   ,    """clean"""   ,    split="""validation"""			)
          # automatic decoding with librispeech
          __UpperCAmelCase    							=ds.sort("""id"""			).select(range(__SCREAMING_SNAKE_CASE			)			)[:num_samples]["""audio"""]
  
          return [x["array"] for x in audio_samples]
  
  
  
      def _a       (							self			:    Optional[int]			)			->       int:
          # fmt: off
          __UpperCAmelCase    							=torch.tensor(
              [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
               3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
               2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
               4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
               7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
               4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
          # fmt: on
          __UpperCAmelCase    							=self._load_datasamples(1			)
          __UpperCAmelCase    							=EncodecFeatureExtractor()
          __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(input_values.shape   ,    (1, 1, 93680)			)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-6			)			)
  
  
  
      def _a       (							self			:    Dict			)			->       List[str]:
          # fmt: off
          __UpperCAmelCase    							=torch.tensor(
              [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
               3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
               2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
               4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
               7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
               4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
          # fmt: on
          __UpperCAmelCase    							=self._load_datasamples(1			)
          __UpperCAmelCase    							=[np.tile(input_audio[0][None]   ,    reps=(2, 1)			)]
          input_audio[0][1] *= 0.5
          __UpperCAmelCase    							=EncodecFeatureExtractor(feature_size=2			)
          __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(input_values.shape   ,    (1, 2, 93680)			)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-6			)			)
          self.assertTrue(torch.allclose(input_values[0, 1, :30]   ,    EXPECTED_INPUT_VALUES * 0.5   ,    atol=1e-6			)			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       Optional[int]:
          __UpperCAmelCase    							=self._load_datasamples(2			)
          # would be easier if the stride was like
          __UpperCAmelCase    							=EncodecFeatureExtractor(feature_size=1   ,    chunk_length_s=1   ,    overlap=0.01			)
  
          # pad and trunc raise an error ?
          with self.assertRaisesRegex(
              __SCREAMING_SNAKE_CASE   ,    """^Both padding and truncation were set. Make sure you only set one.$"""   ,    ):
              __UpperCAmelCase    							=feature_extractor(
                  __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
  
          # truncate to chunk
          __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (2, 1, 71520)			)  # 2 chunks
  
          # force truncate to max_length
          __UpperCAmelCase    							=feature_extractor(
              __SCREAMING_SNAKE_CASE   ,    truncation=__SCREAMING_SNAKE_CASE   ,    max_length=48000   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (2, 1, 48000)			)
  
          # pad to chunk
          __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(padded_outputs.shape   ,    (2, 1, 95280)			)
  
          # pad to chunk
          __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (2, 1, 95280)			)
  
          # force pad to max length
          __UpperCAmelCase    							=feature_extractor(
              __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=100000   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (2, 1, 100000)			)
  
          # force no pad
          with self.assertRaisesRegex(
              __SCREAMING_SNAKE_CASE   ,    """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""   ,    ):
              __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
  
          __UpperCAmelCase    							=feature_extractor(input_audio[0]   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (1, 1, 93680)			)
  
          # no pad if no chunk_length_s
          __UpperCAmelCase    							=None
          with self.assertRaisesRegex(
              __SCREAMING_SNAKE_CASE   ,    """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""   ,    ):
              __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
  
          __UpperCAmelCase    							=feature_extractor(input_audio[0]   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (1, 1, 93680)			)
  
          # no pad if no overlap
          __UpperCAmelCase    							=2
          __UpperCAmelCase    							=None
          with self.assertRaisesRegex(
              __SCREAMING_SNAKE_CASE   ,    """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""   ,    ):
              __UpperCAmelCase    							=feature_extractor(__SCREAMING_SNAKE_CASE   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
  
          __UpperCAmelCase    							=feature_extractor(input_audio[0]   ,    padding=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""			).input_values
          self.assertEquals(truncated_outputs.shape   ,    (1, 1, 93680)			)
  
  
  
  
  
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
     )
  
  
  
  
  