

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE  :						Tuple         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Any         =    {
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
_SCREAMING_SNAKE_CASE  :						Dict         =    [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     for attribute in key.split('''.'''       ):
          SCREAMING_SNAKE_CASE__					=					getattr(_A   ,	_A       )

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

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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     SCREAMING_SNAKE_CASE__					=					fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE__					=					hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE__					=					False
          if "conv_layers" in name:
               load_conv_layer(
                   _A   ,	_A   ,	_A   ,	_A   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
               SCREAMING_SNAKE_CASE__					=					True
          else:
               for key, mapped_key in MAPPING.items():
                    SCREAMING_SNAKE_CASE__					=					'''unispeech_sat.''' + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('''.'''       )[:-1]       ) != key):
                              # special case since naming is very similar
                              continue
                         SCREAMING_SNAKE_CASE__					=					True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE__					=					name.split(_A       )[0].split('''.'''       )[-2]
                              SCREAMING_SNAKE_CASE__					=					mapped_key.replace('''*'''   ,	_A       )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE__					=					'''weight_g'''
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE__					=					'''weight_v'''
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE__					=					'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              SCREAMING_SNAKE_CASE__					=					'''weight'''
                         else:
                              SCREAMING_SNAKE_CASE__					=					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        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




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

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''       )
               SCREAMING_SNAKE_CASE__					=					value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''       )
               SCREAMING_SNAKE_CASE__					=					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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''       )
               SCREAMING_SNAKE_CASE__					=					value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''       )
               SCREAMING_SNAKE_CASE__					=					value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
     else:
          unused_weights.append(_A       )
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A=None   ,	_A=None   ,	_A=True       ):





     '''simple docstring'''




     if config_path is not None:
          SCREAMING_SNAKE_CASE__					=					UniSpeechSatConfig.from_pretrained(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					UniSpeechSatConfig()

     SCREAMING_SNAKE_CASE__					=					''''''

     if is_finetuned:
          SCREAMING_SNAKE_CASE__					=					UniSpeechSatForCTC(_A       )
     else:
          SCREAMING_SNAKE_CASE__					=					UniSpeechSatForPreTraining(_A       )

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]   ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
     SCREAMING_SNAKE_CASE__					=					model[0].eval()

     recursively_load_weights(_A   ,	_A       )

     hf_wavavec.save_pretrained(_A       )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						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_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  :						List[Any]         =    parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=A__ )
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      field(default="question-answering-extractive"		,  metadata={"include_in_asdict_even_if_is_default": True} )
  a     =      Features({"question": Value("string" ), "context": Value("string" )} )
  a     =      Features(
      {
          "answers": Sequence(
              {
                  "text": Value("string" ),
                  "answer_start": Value("int32" ),
              } )
      } )
  a     =      "question"
  a     =      "context"
  a     =      "answers"






  @property
  def    lowercase_	(							self		:	Dict      )	->							Dict[str, str]:
       return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}


