



"""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()
__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",
    "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",
}
__a      =				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''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_ :List[Any]						=			"""lm_head"""

												snake_case_ :Union[str, Any]						=			getattr(_lowercase,					_lowercase	)

						if weight_type is not None:
												snake_case_ :str						=			getattr(_lowercase,					_lowercase	).shape
						else:
												snake_case_ :Optional[Any]						=			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_ :Any						=			value
						elif weight_type == "weight_g":
												snake_case_ :Tuple						=			value
						elif weight_type == "weight_v":
												snake_case_ :Optional[int]						=			value
						elif weight_type == "bias":
												snake_case_ :Tuple						=			value
						else:
												snake_case_ :List[Any]						=			value

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







						'''simple docstring'''


						snake_case_ :Dict						=			[]
						snake_case_ :Union[str, Any]						=			fairseq_model.state_dict()

						snake_case_ :str						=			hf_model.unispeech.feature_extractor

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

						logger.warning(f"""Unused weights: {unused_weights}"""	)
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Dict						=			full_name.split("""conv_layers."""	)[-1]
						snake_case_ :List[str]						=			name.split("""."""	)
						snake_case_ :Any						=			int(items[0]	)
						snake_case_ :str						=			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_ :Union[str, Any]						=			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_ :List[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:
																		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_ :int						=			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_ :int						=			value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
						else:
												unused_weights.append(_lowercase	)
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase=None,					_lowercase=None,					_lowercase=True	):







						'''simple docstring'''


						if config_path is not None:
												snake_case_ :str						=			UniSpeechConfig.from_pretrained(_lowercase	)
						else:
												snake_case_ :Tuple						=			UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		snake_case_ :Optional[int]						=			Dictionary.load_from_json(_lowercase	)

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

																		# fairseq has the <pad> and <s> switched
																		snake_case_ :Optional[Any]						=			42
																		snake_case_ :List[Any]						=			43
																		with open(_lowercase,					"""w""",					encoding="""utf-8"""	) as vocab_handle:
																								json.dump(_lowercase,					_lowercase	)
																		snake_case_ :Union[str, Any]						=			WavaVecaPhonemeCTCTokenizer(
																		    _lowercase,					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=_lowercase,					)
																		snake_case_ :List[Any]						=			True if config.feat_extract_norm == """layer""" else False
																		snake_case_ :Any						=			WavaVecaFeatureExtractor(
																		    feature_size=1,					sampling_rate=16000,					padding_value=0,					do_normalize=_lowercase,					return_attention_mask=_lowercase,					)
																		snake_case_ :str						=			WavaVecaProcessor(feature_extractor=_lowercase,					tokenizer=_lowercase	)
																		processor.save_pretrained(_lowercase	)

												snake_case_ :Optional[Any]						=			UniSpeechForCTC(_lowercase	)
						else:
												snake_case_ :str						=			UniSpeechForPreTraining(_lowercase	)

						if is_finetuned:
												snake_case_,       snake_case_,       snake_case_ :int						=			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_ :Dict						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

						snake_case_ :Any						=			model[0].eval()

						recursively_load_weights(_lowercase,					_lowercase,					_lowercase	)

						hf_unispeech.save_pretrained(_lowercase	)


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_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)





'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






