code: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import os
  from functools import reduce
  
  import fairseq
  import torch
  from datasets import load_dataset
  
  from transformers import WavaVecaProcessor, logging
  from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig
  
  # Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
  from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
  from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel
  
  
  logging.set_verbosity_info()
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """post_extract_proj""": """feature_projection.projection""",
      """models.0.layer_norm""": """feature_projection.layer_norm""",
      """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
      """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
      """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
      """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
      """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
      """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
      """fc2""": """encoder.layers.*.feed_forward.output_dense""",
      """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
      """encoder.layer_norm""": """encoder.layer_norm""",
      """w2v_model.layer_norm""": """feature_projection.layer_norm""",
      """w2v_encoder.proj""": """lm_head""",
      """mask_emb""": """masked_spec_embed""",
  }
  __SCREAMING_SNAKE_CASE					=						[
      """lm_head""",
  ]
  
  
  
  def 						__a      (  a,		a,		a,		a,		a					)     ->   List[Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for attribute in key.split("."					):
                _a          =				getattr(a,		a					)
  
         if weight_type is not None:
                _a          =				getattr(a,		a					).shape
         else:
                _a          =				hf_pointer.shape
  
         if hf_shape != value.shape:
                raise ValueError(
                    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
                    F' {value.shape} for {full_name}'					)
  
         if weight_type == "weight":
                _a          =				value
         elif weight_type == "weight_g":
                _a          =				value
         elif weight_type == "weight_v":
                _a          =				value
         elif weight_type == "bias":
                _a          =				value
         else:
                _a          =				value
  
         logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)
  
  
  
  def 						__a      (  a,		a,		a					)     ->   Tuple:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				[]
         _a          =				fairseq_model.state_dict()
  
         if not is_headless:
                _a          =				hf_model.dataavec_audio.feature_extractor
                _a          =				hf_model.dataavec_audio.encoder.pos_conv_embed
  
         else:
                _a          =				hf_model.feature_extractor
                _a          =				hf_model.encoder.pos_conv_embed
  
         for name, value in fairseq_dict.items():
                _a          =				False
                if "conv_layers" in name:
                       load_conv_layer(
                           a,		a,		a,		a,		)
                       _a          =				True
                elif "pos_conv" in name:
                       load_pos_conv_layer(
                           a,		a,		a,		a,		)
                       _a          =				True
                else:
                       for key, mapped_key in MAPPING.items():
                              if not is_headless:
                                     _a          =				"data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                              if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
                                     _a          =				True
                                     if "*" in mapped_key:
                                            _a          =				name.split(a					)[0].split("."					)[-2]
                                            _a          =				mapped_key.replace("*",		a					)
                                     if "weight_g" in name:
                                            _a          =				"weight_g"
                                     elif "weight_v" in name:
                                            _a          =				"weight_v"
                                     elif "bias" in name:
                                            _a          =				"bias"
                                     elif "weight" in name:
                                            # TODO: don't match quantizer.weight_proj
                                            _a          =				"weight"
                                     else:
                                            _a          =				None
                                     set_recursively(a,		a,		a,		a,		a					)
                              continue
                if not is_used:
                       unused_weights.append(a					)
  
         logger.warning(F'Unused weights: {unused_weights}'					)
  
  
  
  def 						__a      (  a,		a					)     ->   int:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				path.split("."					)
         return reduce(a,		a,		a					)
  
  
  
  def 						__a      (  a,		a,		a,		a					)     ->   Optional[int]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				access_by_string(a,		a					)
         _a          =				hf_weight.data
  
         if fsq_value.shape != hf_value.shape:
                raise ValueError(F'{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'					)
         _a          =				fsq_value
         logger.info(F'{full_name} was correctly initialized from {hf_weight_path}.'					)
  
  
  
  def 						__a      (  a,		a,		a,		a					)     ->   Optional[int]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				full_name.split("conv_layers."					)[-1]
         _a          =				name.split("."					)
         _a          =				int(items[0]					)
         _a          =				int(items[1]					)
  
         _a          =				name.split("."					)[-1]
         if type_id == 0:
                _a          =				"conv"
         elif type_id == 2:
                _a          =				"layer_norm"
         else:
                unused_weights.append(a					)
                return
  
         set_weights(a,		a,		a,		F'conv_layers.{layer_id}.{layer_type}.{weight_type}'					)
  
  
  
  def 						__a      (  a,		a,		a,		a					)     ->   Dict:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				full_name.split("pos_conv."					)[-1]
         _a          =				name.split("."					)
         _a          =				int(items[0]					)
         _a          =				int(items[1]					)
  
         _a          =				name.split("."					)[-1]
         if type_id != 0:
                unused_weights.append(a					)
                return
         else:
                _a          =				"conv"
  
         set_weights(a,		a,		a,		F'layers.{layer_id}.{layer_type}.{weight_type}'					)
  
  
  
  @torch.no_grad()
  def 						__a      (  a,		a,		a=None,		a=None,		a=True					)     ->   Optional[Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if config_path is not None:
                _a          =				DataaVecAudioConfig.from_pretrained(a					)
         else:
                _a          =				DataaVecAudioConfig()
  
         if not is_finetuned:
                # Modify final_proj layer name
                _a          =				DataaVecAudioModel(a					)
                _a          =				os.path.dirname(a					)
  
                _a          =				torch.load(a					)
                _a          =				state_dict["model"].pop("final_proj.0.weight"					)
                _a          =				state_dict["model"].pop("final_proj.0.bias"					)
                _a          =				os.path.join(a,		"converted.pt"					)
                torch.save(a,		a					)
         else:
                _a          =				DataaVecAudioForCTC(a					)
                _a          =				checkpoint_path
  
         def load_dataavec(a					):
                _a   , _a   , _a          =				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]					)
                return model[0].eval()
  
         _a          =				load_dataavec(a					)
  
         recursively_load_weights(a,		a,		not is_finetuned					)
  
         _a          =				WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"					)
  
         _a          =				load_dataset("patrickvonplaten/librispeech_asr_dummy",		"clean",		split="validation"					)
         _a          =				[x["array"] for x in ds[:4]["audio"]]
  
         _a          =				processor(a,		return_tensors="pt",		padding=a					)
  
         _a          =				inputs.input_values
         _a          =				inputs.attention_mask
         #    input_values = inputs.input_values[:, :-1]
         #    attention_mask = inputs.attention_mask[:, :-1]
  
         hf_wavavec.eval()
         model.eval()
         if is_finetuned:
                _a          =				model(source=a,		padding_mask=(1 - attention_mask),		mask=a,		features_only=a					)[
                    "encoder_out"
                ].transpose(0,		1					)
                _a          =				hf_wavavec(a,		attention_mask=a					)["logits"]
  
                _a          =				torch.argmax(a,		dim=-1					)
                _a          =				processor.batch_decode(a					)
  
                print(F'Expected Output: {ds[:4]["text"]}, Pred: {output_string}'					)
         else:
                _a          =				model(source=a,		padding_mask=(1 - attention_mask),		mask=a,		features_only=a					)[
                    "layer_results"
                ][-1][0].transpose(0,		1					)
                _a          =				hf_wavavec(a,		attention_mask=a					)["last_hidden_state"]
  
         print(our_output.shape,		their_output.shape					)
         _a          =				torch.max(torch.abs(our_output - their_output					)					).item()
         print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
         _a          =				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"					)
  
         hf_wavavec.save_pretrained(a					)
  
         if is_finetuned:
                processor.save_pretrained(a					)
         else:
                processor.feature_extractor.save_pretrained(a					)
  
  
  if __name__ == "__main__":
     __SCREAMING_SNAKE_CASE					=						argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     __SCREAMING_SNAKE_CASE					=						parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vit import ViTImageProcessor
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Tuple				,  *UpperCamelCase__							:Tuple				,  **UpperCamelCase__							:Any       ):
            warnings.warn(
                "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use ViTImageProcessor instead."				,  UpperCamelCase__				,  )
            super().__init__(*UpperCamelCase__				,  **UpperCamelCase__       )
  