


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase__   	=							{
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase__   	=							[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Dict:







 """simple docstring"""


 for attribute in key.split('''.'''						):
  if is_finetuned:
   if attribute in ["quantizer", "project_q", "project_hid"]:
    # those layers are only relevant for pretraining and should be dropped
    return

   if attribute == "ctc_proj":
    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
    _lowercase							      ='''lm_head'''

  _lowercase							      =getattr(__snake_case     ,						__snake_case						)

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

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =[]
 _lowercase							      =fairseq_model.state_dict()

 _lowercase							      =hf_model.unispeech.feature_extractor

 for name, value in fairseq_dict.items():
  _lowercase							      =False
  if "conv_layers" in name:
   load_conv_layer(
       __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						hf_model.config.feat_extract_norm == '''group'''     ,						)
   _lowercase							      =True
  else:
   for key, mapped_key in MAPPING.items():
    _lowercase							      ='''unispeech.''' + 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]:
     _lowercase							      =True
     if "*" in mapped_key:
      _lowercase							      =name.split(__snake_case						)[0].split('''.'''						)[-2]
      _lowercase							      =mapped_key.replace('''*'''     ,						__snake_case						)
     if "weight_g" in name:
      _lowercase							      ='''weight_g'''
     elif "weight_v" in name:
      _lowercase							      ='''weight_v'''
     elif "bias" in name:
      _lowercase							      ='''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      _lowercase							      ='''weight'''
     else:
      _lowercase							      =None
     set_recursively(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
    continue
  if not is_used:
   unused_weights.append(__snake_case						)

 logger.warning(F"Unused weights: {unused_weights}"						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


 _lowercase							      =full_name.split('''conv_layers.'''						)[-1]
 _lowercase							      =name.split('''.'''						)
 _lowercase							      =int(items[0]						)
 _lowercase							      =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."
   )
   _lowercase							      =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."
   )
   _lowercase							      =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."
   )
   _lowercase							      =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."
   )
   _lowercase							      =value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(__snake_case						)
@torch.no_grad()
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=True						)     ->			List[Any]:







 """simple docstring"""


 if config_path is not None:
  _lowercase							      =UniSpeechConfig.from_pretrained(__snake_case						)
 else:
  _lowercase							      =UniSpeechConfig()

 if is_finetuned:
  if dict_path:
   _lowercase							      =Dictionary.load_from_json(__snake_case						)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   _lowercase							      =target_dict.pad_index
   _lowercase							      =target_dict.bos_index
   _lowercase							      =target_dict.eos_index
   _lowercase							      =len(target_dict.symbols						)
   _lowercase							      =os.path.join(__snake_case     ,						'''vocab.json'''						)
   if not os.path.isdir(__snake_case						):
    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case						)						)
    return
   os.makedirs(__snake_case     ,						exist_ok=__snake_case						)
   _lowercase							      =target_dict.indices

   # fairseq has the <pad> and <s> switched
   _lowercase							      =42
   _lowercase							      =43
   with open(__snake_case     ,						'''w'''     ,						encoding='''utf-8'''						) as vocab_handle:
    json.dump(__snake_case     ,						__snake_case						)
   _lowercase							      =WavaVecaPhonemeCTCTokenizer(
       __snake_case     ,						unk_token=target_dict.unk_word     ,						pad_token=target_dict.pad_word     ,						bos_token=target_dict.bos_word     ,						eos_token=target_dict.eos_word     ,						word_delimiter_token='''|'''     ,						do_lower_case=__snake_case     ,						)
   _lowercase							      =True if config.feat_extract_norm == '''layer''' else False
   _lowercase							      =WavaVecaFeatureExtractor(
       feature_size=1     ,						sampling_rate=16000     ,						padding_value=0     ,						do_normalize=__snake_case     ,						return_attention_mask=__snake_case     ,						)
   _lowercase							      =WavaVecaProcessor(feature_extractor=__snake_case     ,						tokenizer=__snake_case						)
   processor.save_pretrained(__snake_case						)

  _lowercase							      =UniSpeechForCTC(__snake_case						)
 else:
  _lowercase							      =UniSpeechForPreTraining(__snake_case						)

 if is_finetuned:
  _lowercase      ,    _lowercase      ,    _lowercase							      =fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]     ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						), '''w2v_path''': checkpoint_path}						)
 else:
  _lowercase      ,    _lowercase      ,    _lowercase							      =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

 _lowercase							      =model[0].eval()

 recursively_load_weights(__snake_case     ,						__snake_case     ,						__snake_case						)

 hf_unispeech.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__   	=							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'''
  )
  UpperCAmelCase__   	=							parser.parse_args()
  convert_unispeech_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_torch_available


lowercase__    :      List[str]						=    {
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Any						=    [
      '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GitForCausalLM''',
      '''GitModel''',
      '''GitPreTrainedModel''',
      '''GitVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

  lowercase__    :      List[str]						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)