


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__UpperCamelCase    :						Union[str, Any]     =							datasets.utils.logging.get_logger(__name__)



@dataclass
class 							_UpperCamelCase (						datasets.BuilderConfig						):

			'''simple docstring'''







			a_				:							List[str]    = 1_0000
			a_				:							Any    = None
			a_				:							str    = None



class 							_UpperCamelCase (						datasets.ArrowBasedBuilder						):

			'''simple docstring'''







			a_				:							List[str]    = ParquetConfig

			def   _snake_case    (  self  : List[str]							):






									'''simple docstring'''



									return datasets.DatasetInfo(features=self.config.features							)

			def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : Optional[int]							):






									'''simple docstring'''



									if not self.config.data_files:
															raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
									__lowerCamelCase     :      Any       						=						dl_manager.download_and_extract(self.config.data_files							)
									if isinstance(A__       ,  (str, list, tuple)							):
															__lowerCamelCase     :      Union[str, Any]       						=						data_files
															if isinstance(A__       ,  A__							):
																					__lowerCamelCase     :      Any       						=						[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															__lowerCamelCase     :      str       						=						[dl_manager.iter_files(A__							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,  gen_kwargs={"""files""": files}							)]
									__lowerCamelCase     :      List[Any]       						=						[]
									for split_name, files in data_files.items():
															if isinstance(A__       ,  A__							):
																					__lowerCamelCase     :      int       						=						[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															__lowerCamelCase     :      str       						=						[dl_manager.iter_files(A__							) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																					for file in itertools.chain.from_iterable(A__							):
																											with open(A__       ,  """rb"""							) as f:
																																	__lowerCamelCase     :      str       						=						datasets.Features.from_arrow_schema(pq.read_schema(A__							)							)
																											break
															splits.append(datasets.SplitGenerator(name=A__       ,  gen_kwargs={"""files""": files}							)							)
									return splits

			def   _snake_case    (  self  : Optional[Any]       ,  _lowerCamelCase  : Dict							):






									'''simple docstring'''



									if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															__lowerCamelCase     :      List[str]       						=						table_cast(A__       ,  self.info.features.arrow_schema							)
									return pa_table


			def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Optional[Any]							):






									'''simple docstring'''



									__lowerCamelCase     :      List[str]       						=						self.info.features.arrow_schema if self.info.features is not None else None
									if self.info.features is not None and self.config.columns is not None:
															if sorted(field.name for field in schema							) != sorted(self.config.columns							):
																					raise ValueError(
																					    F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""							)
									for file_idx, file in enumerate(itertools.chain.from_iterable(A__							)							):
															with open(A__       ,  """rb"""							) as f:
																					__lowerCamelCase     :      List[Any]       						=						pq.ParquetFile(A__							)
																					try:
																											for batch_idx, record_batch in enumerate(
																											    parquet_file.iter_batches(batch_size=self.config.batch_size       ,  columns=self.config.columns							)							):
																																	__lowerCamelCase     :      Dict       						=						pa.Table.from_batches([record_batch]							)
																																	# Uncomment for debugging (will print the Arrow table size and elements)
																																	# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																	# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																	yield F"""{file_idx}_{batch_idx}""", self._cast_table(A__							)
																					except ValueError as e:
																											logger.error(F"""Failed to read file '{file}' with error {type(A__							)}: {e}"""							)
																											raise







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase    :						Optional[Any]     =							logging.get_logger(__name__)

__UpperCamelCase    :						List[str]     =							{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    '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',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__UpperCamelCase    :						Dict     =							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Any       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      List[Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      Optional[int]       						=						hf_pointer.shape

      assert hf_shape == value.shape, (
          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     :      List[Any]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Tuple       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      List[Any]       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[int]       						=						value
      else:
            __lowerCamelCase     :      Dict       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						[]
      __lowerCamelCase     :      Union[str, Any]       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      Optional[Any]       						=						hf_model.feature_extractor
      __lowerCamelCase     :      List[str]       						=						hf_model.adapter

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      List[str]       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Any       						=						True
            elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
                  load_adapter(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                  __lowerCamelCase     :      Dict       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      Union[str, Any]       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      int       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Dict       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      int       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      List[str]       						=						"""weight_v"""
                              elif "bias" in name:
                                    __lowerCamelCase     :      str       						=						"""bias"""
                              elif "weight" in name:
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      int       						=						None
                              set_recursively(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                        continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase      )

      logger.warning(f"""Unused weights: {unused_weights}"""      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      Tuple       						=						name.split("""."""      )
      __lowerCamelCase     :      Tuple       						=						int(items[0]      )
      __lowerCamelCase     :      str       						=						int(items[1]      )

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                  )
                  __lowerCamelCase     :      Dict       						=						value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                  )
                  __lowerCamelCase     :      List[str]       						=						value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                      " found."
                  )
                  __lowerCamelCase     :      Any       						=						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                  )
                  __lowerCamelCase     :      str       						=						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						full_name.split("""adaptor."""      )[-1]
      __lowerCamelCase     :      Any       						=						name.split("""."""      )

      if items[1].isdigit():
            __lowerCamelCase     :      Dict       						=						int(items[1]      )
      else:
            __lowerCamelCase     :      List[str]       						=						None

      if "adaptor" not in full_name:
            if "proj_ln" in full_name:
                  # has to be layer norm
                  if "bias" in name:
                        assert (
                            value.shape == adapter.proj_layer_norm.bias.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                        __lowerCamelCase     :      str       						=						value
                        logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""      )
                  if "weight" in name:
                        assert (
                            value.shape == adapter.proj_layer_norm.weight.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                        __lowerCamelCase     :      str       						=						value
            else:
                  # has to be projection layer
                  if "bias" in name:
                        assert (
                            value.shape == adapter.proj.bias.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                        __lowerCamelCase     :      Optional[int]       						=						value
                        logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""      )
                  if "weight" in name:
                        assert (
                            value.shape == adapter.proj.weight.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                        __lowerCamelCase     :      List[str]       						=						value
                        logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""      )
      elif isinstance(UpperCAmelCase						,	UpperCAmelCase      ):
            if "bias" in name:
                  assert (
                      value.shape == adapter.layers[layer_id].conv.bias.data.shape
                  ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
                  __lowerCamelCase     :      int       						=						value
                  logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
            elif "weight" in name:
                  assert (
                      value.shape == adapter.layers[layer_id].conv.weight.data.shape
                  ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
                  __lowerCamelCase     :      Tuple       						=						value
                  logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						emb.weight.shape
      __lowerCamelCase     :      Any       						=						nn.Linear(UpperCAmelCase						,	UpperCAmelCase						,	bias=UpperCAmelCase      )
      __lowerCamelCase     :      List[Any]       						=						emb.weight.data
      return lin_layer





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Optional[Any]						,	):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						WavaVecaConfig.from_pretrained(
          UpperCAmelCase						,	add_adapter=UpperCAmelCase						,	adapter_stride=UpperCAmelCase						,	adapter_kernel_size=UpperCAmelCase						,	use_auth_token=UpperCAmelCase						,	output_hidden_size=UpperCAmelCase						,	)
      __lowerCamelCase     :      Optional[int]       						=						MBartConfig.from_pretrained(UpperCAmelCase      )

      # load model
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,	arg_overrides={
              """config_yaml""": config_yaml_path,
              """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
              """w2v_path""": checkpoint_path,
              """load_pretrained_decoder_from""": None,
          }						,	)
      __lowerCamelCase     :      Union[str, Any]       						=						model[0].eval()

      # load feature extractor
      __lowerCamelCase     :      Any       						=						WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase						,	use_auth_token=UpperCAmelCase      )

      # set weights for wav2vec2 encoder
      __lowerCamelCase     :      Tuple       						=						WavaVecaModel(UpperCAmelCase      )

      recursively_load_weights_wavaveca(model.encoder						,	UpperCAmelCase      )

      # load decoder weights
      __lowerCamelCase     :      Dict       						=						MBartForCausalLM(UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      int       						=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,	strict=UpperCAmelCase      )
      logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
      logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

      __lowerCamelCase     :      List[Any]       						=						SpeechEncoderDecoderModel(encoder=UpperCAmelCase						,	decoder=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						False

      __lowerCamelCase     :      List[str]       						=						MBartaaTokenizer(UpperCAmelCase      )
      tokenizer.save_pretrained(UpperCAmelCase      )

      __lowerCamelCase     :      int       						=						hf_wavavec.config.to_dict()
      __lowerCamelCase     :      str       						=						tokenizer.pad_token_id
      __lowerCamelCase     :      Optional[Any]       						=						tokenizer.bos_token_id
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Tuple       						=						"""mbart50"""
      __lowerCamelCase     :      List[str]       						=						"""wav2vec2"""

      __lowerCamelCase     :      List[str]       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Optional[int]       						=						250_004
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id

      __lowerCamelCase     :      Optional[Any]       						=						SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase      )

      hf_wavavec.save_pretrained(UpperCAmelCase      )
      feature_extractor.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Dict     =							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_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
    parser.add_argument(
        '--encoder_config_path',
        default='facebook/wav2vec2-xls-r-1b',
        type=str,
        help='Path to hf encoder wav2vec2 checkpoint config',
    )
    parser.add_argument(
        '--decoder_config_path',
        default='facebook/mbart-large-50-one-to-many-mmt',
        type=str,
        help='Path to hf decoder checkpoint config',
    )
    parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
    parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
    parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
    parser.add_argument('--encoder_output_dim', default=1024, type=int, help='encoder output dim')
    parser.add_argument('--start_token_id', default=250004, type=int, help='`decoder_start_token_id` of model config')

    __UpperCamelCase    :						int     =							parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        args.config_yaml_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        add_adapter=args.add_adapter,
        adapter_kernel_size=args.adapter_kernel_size,
        adapter_stride=args.adapter_stride,
        decoder_start_token_id=args.start_token_id,
        encoder_output_dim=args.encoder_output_dim,
    )



