






'''simple docstring'''
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a_					= logging.get_logger(__name__)

a_					= {
    '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',
}
a_					= [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Any,	UpperCamelCase__				:						Dict,	UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






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

      if weight_type is not None:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=getattr(UpperCamelCase__,	UpperCamelCase__							).shape
      else:
            SCREAMING_SNAKE_CASE__     :				Any									=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__     :				Union[str, Any]									=value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE__     :				str									=value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=value
      else:
            SCREAMING_SNAKE_CASE__     :				Tuple									=value

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





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[]
      SCREAMING_SNAKE_CASE__     :				Dict									=fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE__     :				List[str]									=hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE__     :				Any									=False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	hf_model.config.feat_extract_norm == '''group''',	)
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									='''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__     :				str									=True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE__     :				Any									=name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                                    SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=mapped_key.replace('''*''',	UpperCamelCase__							)
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE__     :				List[str]									='''weight_g'''
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE__     :				Any									='''weight_v'''
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE__     :				Any									='''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    SCREAMING_SNAKE_CASE__     :				Tuple									='''weight'''
                              else:
                                    SCREAMING_SNAKE_CASE__     :				Dict									=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  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						int,	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Dict									=full_name.split('''conv_layers.'''							)[-1]
      SCREAMING_SNAKE_CASE__     :				List[str]									=name.split('''.'''							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=int(items[0]							)
      SCREAMING_SNAKE_CASE__     :				List[str]									=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__     :				str									=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__     :				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:
                  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__     :				Dict									=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__     :				str									=value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."							)
      else:
            unused_weights.append(UpperCamelCase__							)





@torch.no_grad()
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Dict,	UpperCamelCase__				:						Optional[Any]=None,	UpperCamelCase__				:						Dict=None,	UpperCamelCase__				:						Any=True							):




      '''simple docstring'''






      if config_path is not None:
            SCREAMING_SNAKE_CASE__     :				str									=UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
      else:
            SCREAMING_SNAKE_CASE__     :				int									=UniSpeechSatConfig()

      SCREAMING_SNAKE_CASE__     :				Dict									=''''''

      if is_finetuned:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=UniSpeechSatForCTC(UpperCamelCase__							)
      else:
            SCREAMING_SNAKE_CASE__     :				List[str]									=UniSpeechSatForPreTraining(UpperCamelCase__							)

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

      recursively_load_weights(UpperCamelCase__,	UpperCamelCase__							)

      hf_wavavec.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  a_					= 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'
  )
  a_					= 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
  )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)