





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()
snake_case								=							logging.get_logger(__name__)

snake_case								=							{
    """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""",
}
snake_case								=							[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:Tuple      ,				snake_case__	:Optional[Any]      ,				snake_case__	:Optional[Any]      ,				snake_case__	:Any      ,				snake_case__	:Dict  )   ->    Tuple:
  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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any      ,				snake_case__	:Optional[int]      ,				snake_case__	:Union[str, Any]  )   ->    str:
  _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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[Any]      ,				snake_case__	:str      ,				snake_case__	:Optional[Any]      ,				snake_case__	:int      ,				snake_case__	:Dict  )   ->    Union[str, Any]:
  _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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[Any]      ,				snake_case__	:Tuple      ,				snake_case__	:int=None      ,				snake_case__	:Optional[Any]=None      ,				snake_case__	:Optional[int]=True  )   ->    List[Any]:
  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=1_6000      ,				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__":
      snake_case								=							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"""
      )
      snake_case								=							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"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


