




'''simple docstring'''



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_							:	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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase_							:	Optional[Any]	     =		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  str						)-> int:




     '''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
                    __snake_case            =	'''lm_head'''

          __snake_case            =	getattr(_lowerCamelCase ,						_lowerCamelCase						)

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  str						)-> str:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	fairseq_model.state_dict()

     __snake_case            =	hf_model.unispeech.feature_extractor

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  List[str]						)-> List[str]:




     '''simple docstring'''





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







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  int=None ,						_lowerCamelCase				:  Any=None ,						_lowerCamelCase				:  Tuple=True						)-> Optional[int]:




     '''simple docstring'''





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

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

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

               # fairseq has the <pad> and <s> switched
               __snake_case            =	42
               __snake_case            =	43
               with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as vocab_handle:
                    json.dump(_lowerCamelCase ,						_lowerCamelCase						)
               __snake_case            =	WavaVecaPhonemeCTCTokenizer(
                   _lowerCamelCase ,						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=_lowerCamelCase ,						)
               __snake_case            =	True if config.feat_extract_norm == '''layer''' else False
               __snake_case            =	WavaVecaFeatureExtractor(
                   feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=_lowerCamelCase ,						return_attention_mask=_lowerCamelCase ,						)
               __snake_case            =	WavaVecaProcessor(feature_extractor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
               processor.save_pretrained(_lowerCamelCase						)

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

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

     __snake_case            =	model[0].eval()

     recursively_load_weights(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     hf_unispeech.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	str	     =		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_							:	Union[str, Any]	     =		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,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_							:	Any	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






