





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()
__lowercase				    =      logging.get_logger(__name__)

__lowercase				    =      {
    """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""",
}
__lowercase				    =      [
    """lm_head""",
]


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		Any  ,				__lowercase:		Optional[int]  ,				__lowercase:		Optional[Any]  ,				__lowercase:		Tuple):



  '''simple docstring'''






  for attribute in key.split('''.'''):
    __lowerCAmelCase								=					getattr(a__  ,				a__)

  if weight_type is not None:
    __lowerCAmelCase								=					getattr(a__  ,				a__).shape
  else:
    __lowerCAmelCase								=					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":
    __lowerCAmelCase								=					value
  elif weight_type == "weight_g":
    __lowerCAmelCase								=					value
  elif weight_type == "weight_v":
    __lowerCAmelCase								=					value
  elif weight_type == "bias":
    __lowerCAmelCase								=					value
  else:
    __lowerCAmelCase								=					value

  logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''')


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]  ,				__lowercase:		int  ,				__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase								=					[]
  __lowerCAmelCase								=					fairseq_model.state_dict()

  if not is_headless:
    __lowerCAmelCase								=					hf_model.dataavec_audio.feature_extractor
    __lowerCAmelCase								=					hf_model.dataavec_audio.encoder.pos_conv_embed

  else:
    __lowerCAmelCase								=					hf_model.feature_extractor
    __lowerCAmelCase								=					hf_model.encoder.pos_conv_embed

  for name, value in fairseq_dict.items():
    __lowerCAmelCase								=					False
    if "conv_layers" in name:
      load_conv_layer(
          a__  ,				a__  ,				a__  ,				a__  ,				)
      __lowerCAmelCase								=					True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          a__  ,				a__  ,				a__  ,				a__  ,				)
      __lowerCAmelCase								=					True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          __lowerCAmelCase								=					'''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]:
          __lowerCAmelCase								=					True
          if "*" in mapped_key:
            __lowerCAmelCase								=					name.split(a__)[0].split('''.''')[-2]
            __lowerCAmelCase								=					mapped_key.replace('''*'''  ,				a__)
          if "weight_g" in name:
            __lowerCAmelCase								=					'''weight_g'''
          elif "weight_v" in name:
            __lowerCAmelCase								=					'''weight_v'''
          elif "bias" in name:
            __lowerCAmelCase								=					'''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __lowerCAmelCase								=					'''weight'''
          else:
            __lowerCAmelCase								=					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        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[Any]  ,				__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase								=					path.split('''.''')
  return reduce(a__  ,				a__  ,				a__)


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		int  ,				__lowercase:		Optional[int]  ,				__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase								=					access_by_string(a__  ,				a__)
  __lowerCAmelCase								=					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.''')
  __lowerCAmelCase								=					fsq_value
  logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.''')


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Dict  ,				__lowercase:		str  ,				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					full_name.split('''conv_layers.''')[-1]
  __lowerCAmelCase								=					name.split('''.''')
  __lowerCAmelCase								=					int(items[0])
  __lowerCAmelCase								=					int(items[1])

  __lowerCAmelCase								=					name.split('''.''')[-1]
  if type_id == 0:
    __lowerCAmelCase								=					'''conv'''
  elif type_id == 2:
    __lowerCAmelCase								=					'''layer_norm'''
  else:
    unused_weights.append(a__)
    return

  set_weights(a__  ,				a__  ,				a__  ,				F'''conv_layers.{layer_id}.{layer_type}.{weight_type}''')


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		Any  ,				__lowercase:		Tuple  ,				__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase								=					full_name.split('''pos_conv.''')[-1]
  __lowerCAmelCase								=					name.split('''.''')
  __lowerCAmelCase								=					int(items[0])
  __lowerCAmelCase								=					int(items[1])

  __lowerCAmelCase								=					name.split('''.''')[-1]
  if type_id != 0:
    unused_weights.append(a__)
    return
  else:
    __lowerCAmelCase								=					'''conv'''

  set_weights(a__  ,				a__  ,				a__  ,				F'''layers.{layer_id}.{layer_type}.{weight_type}''')


@torch.no_grad()
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		List[Any]  ,				__lowercase:		Optional[int]=None  ,				__lowercase:		List[str]=None  ,				__lowercase:		Optional[int]=True):



  '''simple docstring'''






  if config_path is not None:
    __lowerCAmelCase								=					DataaVecAudioConfig.from_pretrained(a__)
  else:
    __lowerCAmelCase								=					DataaVecAudioConfig()

  if not is_finetuned:
    # Modify final_proj layer name
    __lowerCAmelCase								=					DataaVecAudioModel(a__)
    __lowerCAmelCase								=					os.path.dirname(a__)

    __lowerCAmelCase								=					torch.load(a__)
    __lowerCAmelCase								=					state_dict['''model'''].pop('''final_proj.0.weight''')
    __lowerCAmelCase								=					state_dict['''model'''].pop('''final_proj.0.bias''')
    __lowerCAmelCase								=					os.path.join(a__  ,				'''converted.pt''')
    torch.save(a__  ,				a__)
  else:
    __lowerCAmelCase								=					DataaVecAudioForCTC(a__)
    __lowerCAmelCase								=					checkpoint_path

  def load_dataavec(__lowercase:		Optional[int]):
    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					fairseq.checkpoint_utils.load_model_ensemble_and_task([path])
    return model[0].eval()

  __lowerCAmelCase								=					load_dataavec(a__)

  recursively_load_weights(a__  ,				a__  ,				not is_finetuned)

  __lowerCAmelCase								=					WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60''')

  __lowerCAmelCase								=					load_dataset('''patrickvonplaten/librispeech_asr_dummy'''  ,				'''clean'''  ,				split='''validation''')
  __lowerCAmelCase								=					[x['''array'''] for x in ds[:4]['''audio''']]

  __lowerCAmelCase								=					processor(a__  ,				return_tensors='''pt'''  ,				padding=a__)

  __lowerCAmelCase								=					inputs.input_values
  __lowerCAmelCase								=					inputs.attention_mask
  #    input_values = inputs.input_values[:, :-1]
  #    attention_mask = inputs.attention_mask[:, :-1]

  hf_wavavec.eval()
  model.eval()
  if is_finetuned:
    __lowerCAmelCase								=					model(source=a__  ,				padding_mask=(1 - attention_mask)  ,				mask=a__  ,				features_only=a__)[
        '''encoder_out'''
    ].transpose(0  ,				1)
    __lowerCAmelCase								=					hf_wavavec(a__  ,				attention_mask=a__)['''logits''']

    __lowerCAmelCase								=					torch.argmax(a__  ,				dim=-1)
    __lowerCAmelCase								=					processor.batch_decode(a__)

    print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}''')
  else:
    __lowerCAmelCase								=					model(source=a__  ,				padding_mask=(1 - attention_mask)  ,				mask=a__  ,				features_only=a__)[
        '''layer_results'''
    ][-1][0].transpose(0  ,				1)
    __lowerCAmelCase								=					hf_wavavec(a__  ,				attention_mask=a__)['''last_hidden_state''']

  print(our_output.shape  ,				their_output.shape)
  __lowerCAmelCase								=					torch.max(torch.abs(our_output - their_output)).item()
  print(F'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
  __lowerCAmelCase								=					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__":
      __lowercase				    =      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"""
      )
      __lowercase				    =      parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            __UpperCamelCase  ,						split=__UpperCamelCase  ,						features=__UpperCamelCase  ,						cache_dir=__UpperCamelCase  ,						keep_in_memory=__UpperCamelCase  ,						streaming=__UpperCamelCase  ,						num_proc=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							path_or_paths if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else {self.split: path_or_paths}
        UpperCamelCase_											=							Csv(
            cache_dir=__UpperCamelCase  ,						data_files=__UpperCamelCase  ,						features=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self.streaming:
               UpperCamelCase_											=							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None

               self.builder.download_and_prepare(
                   download_config=__UpperCamelCase  ,						download_mode=__UpperCamelCase  ,						verification_mode=__UpperCamelCase  ,						base_path=__UpperCamelCase  ,						num_proc=self.num_proc  ,						)
               UpperCamelCase_											=							self.builder.as_dataset(
                   split=self.split  ,						verification_mode=__UpperCamelCase  ,						in_memory=self.keep_in_memory  )
        return dataset



class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        if num_proc is not None and num_proc <= 0:
               raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''  )

        UpperCamelCase_											=							dataset
        UpperCamelCase_											=							path_or_buf
        UpperCamelCase_											=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCamelCase_											=							num_proc
        UpperCamelCase_											=							"""utf-8"""
        UpperCamelCase_											=							to_csv_kwargs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.to_csv_kwargs.pop("""path_or_buf"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""header"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""index"""  ,						__UpperCamelCase  )

        if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)  ):
               with open(self.path_or_buf  ,						"""wb+"""  ) as buffer:
                      UpperCamelCase_											=							self._write(file_obj=__UpperCamelCase  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        else:
               UpperCamelCase_											=							self._write(file_obj=self.path_or_buf  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        return written

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							args

        UpperCamelCase_											=							query_table(
            table=self.dataset.data  ,						key=slice(__UpperCamelCase  ,						offset + self.batch_size  )  ,						indices=self.dataset._indices  ,						)
        UpperCamelCase_											=							batch.to_pandas().to_csv(
            path_or_buf=__UpperCamelCase  ,						header=header if (offset == 0) else False  ,						index=__UpperCamelCase  ,						**__UpperCamelCase  )
        return csv_str.encode(self.encoding  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							0

        if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0  ,						len(self.dataset  )  ,						self.batch_size  )  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                      UpperCamelCase_											=							self._batch_csv((offset, header, index, to_csv_kwargs)  )
                      written += file_obj.write(__UpperCamelCase  )

        else:
               UpperCamelCase_							,					UpperCamelCase_											=							len(self.dataset  ), self.batch_size
               with multiprocessing.Pool(self.num_proc  ) as pool:
                      for csv_str in logging.tqdm(
                          pool.imap(
                              self._batch_csv  ,						[(offset, header, index, to_csv_kwargs) for offset in range(0  ,						__UpperCamelCase  ,						__UpperCamelCase  )]  ,						)  ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                             written += file_obj.write(__UpperCamelCase  )

        return written



