






'''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    :      Dict  			=		logging.get_logger(__name__)

UpperCamelCase    :      Optional[int]  			=		{
    '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[int]  			=		[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 						A__			(				__lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			List[str]						):
						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
																								lowerCamelCase__         =  """lm_head"""

												lowerCamelCase__         =  getattr(__lowerCAmelCase   ,    __lowerCAmelCase						)

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

						logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			int						):
						lowerCamelCase__         =  []
						lowerCamelCase__         =  fairseq_model.state_dict()

						lowerCamelCase__         =  hf_model.unispeech.feature_extractor

						for name, value in fairseq_dict.items():
												lowerCamelCase__         =  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    hf_model.config.feat_extract_norm == """group"""   ,    )
																		lowerCamelCase__         =  True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowerCamelCase__         =  """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]:
																														lowerCamelCase__         =  True
																														if "*" in mapped_key:
																																				lowerCamelCase__         =  name.split(__lowerCAmelCase						)[0].split("""."""						)[-2]
																																				lowerCamelCase__         =  mapped_key.replace("""*"""   ,    __lowerCAmelCase						)
																														if "weight_g" in name:
																																				lowerCamelCase__         =  """weight_g"""
																														elif "weight_v" in name:
																																				lowerCamelCase__         =  """weight_v"""
																														elif "bias" in name:
																																				lowerCamelCase__         =  """bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowerCamelCase__         =  """weight"""
																														else:
																																				lowerCamelCase__         =  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 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  full_name.split("""conv_layers."""						)[-1]
						lowerCamelCase__         =  name.split("""."""						)
						lowerCamelCase__         =  int(items[0]						)
						lowerCamelCase__         =  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.'''
																		)
																		lowerCamelCase__         =  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.'''
																		)
																		lowerCamelCase__         =  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."
																		)
																		lowerCamelCase__         =  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.'''
																		)
																		lowerCamelCase__         =  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 						A__			(				__lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Optional[Any]=None   ,    __lowerCAmelCase		:			Tuple=None   ,    __lowerCAmelCase		:			Union[str, Any]=True						):
						if config_path is not None:
												lowerCamelCase__         =  UniSpeechConfig.from_pretrained(__lowerCAmelCase						)
						else:
												lowerCamelCase__         =  UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		lowerCamelCase__         =  Dictionary.load_from_json(__lowerCAmelCase						)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowerCamelCase__         =  target_dict.pad_index
																		lowerCamelCase__         =  target_dict.bos_index
																		lowerCamelCase__         =  target_dict.eos_index
																		lowerCamelCase__         =  len(target_dict.symbols						)
																		lowerCamelCase__         =  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						)
																		lowerCamelCase__         =  target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		lowerCamelCase__         =  42
																		lowerCamelCase__         =  43
																		with open(__lowerCAmelCase   ,    """w"""   ,    encoding="""utf-8"""						) as vocab_handle:
																								json.dump(__lowerCAmelCase   ,    __lowerCAmelCase						)
																		lowerCamelCase__         =  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   ,    )
																		lowerCamelCase__         =  True if config.feat_extract_norm == """layer""" else False
																		lowerCamelCase__         =  WavaVecaFeatureExtractor(
																		    feature_size=1   ,    sampling_rate=1_6000   ,    padding_value=0   ,    do_normalize=__lowerCAmelCase   ,    return_attention_mask=__lowerCAmelCase   ,    )
																		lowerCamelCase__         =  WavaVecaProcessor(feature_extractor=__lowerCAmelCase   ,    tokenizer=__lowerCAmelCase						)
																		processor.save_pretrained(__lowerCAmelCase						)

												lowerCamelCase__         =  UniSpeechForCTC(__lowerCAmelCase						)
						else:
												lowerCamelCase__         =  UniSpeechForPreTraining(__lowerCAmelCase						)

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

						lowerCamelCase__         =  model[0].eval()

						recursively_load_weights(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)

						hf_unispeech.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      int  			=		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    :      Tuple  			=		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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase    :      int  			=		logging.get_logger(__name__)

UpperCamelCase    :      Optional[int]  			=		{'vocab_file': 'sentencepiece.bpe.model'}

UpperCamelCase    :      Dict  			=		{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCamelCase    :      List[Any]  			=		{
    'camembert-base': 5_12,
}

UpperCamelCase    :      List[str]  			=		'▁'




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					VOCAB_FILES_NAMES
		_UpperCamelCase				  =					PRETRAINED_VOCAB_FILES_MAP
		_UpperCamelCase				  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_UpperCamelCase				  =					['input_ids', 'attention_mask']






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase="<s>"   ,_lowerCAmelCase="</s>"   ,_lowerCAmelCase="</s>"   ,_lowerCAmelCase="<s>"   ,_lowerCAmelCase="<unk>"   ,_lowerCAmelCase="<pad>"   ,_lowerCAmelCase="<mask>"   ,_lowerCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase__         =  AddedToken(_lowerCAmelCase   ,lstrip=_lowerCAmelCase   ,rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ) else mask_token

								lowerCamelCase__         =  {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_lowerCAmelCase   ,eos_token=_lowerCAmelCase   ,unk_token=_lowerCAmelCase   ,sep_token=_lowerCAmelCase   ,cls_token=_lowerCAmelCase   ,pad_token=_lowerCAmelCase   ,mask_token=_lowerCAmelCase   ,additional_special_tokens=_lowerCAmelCase   ,sp_model_kwargs=self.sp_model_kwargs   ,**_lowerCAmelCase   ,)
								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(str(_lowerCAmelCase       )       )
								lowerCamelCase__         =  vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								lowerCamelCase__         =  {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
								lowerCamelCase__         =  len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):

								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase__         =  [self.cls_token_id]
								lowerCamelCase__         =  [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False       ):
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowerCAmelCase   ,token_ids_a=_lowerCAmelCase   ,already_has_special_tokens=_lowerCAmelCase       )

								if token_ids_a is None:
														return [1] + ([0] * len(_lowerCAmelCase       )) + [1]
								return [1] + ([0] * len(_lowerCAmelCase       )) + [1, 1] + ([0] * len(_lowerCAmelCase       )) + [1]






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								lowerCamelCase__         =  [self.sep_token_id]
								lowerCamelCase__         =  [self.cls_token_id]

								if token_ids_a is None:
														return len(cls + token_ids_a + sep       ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






		@property
		def 			UpperCamelCase_					(      self       ):
								return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  {self.convert_ids_to_tokens(_lowerCAmelCase       ): i for i in range(self.vocab_size       )}
								vocab.update(self.added_tokens_encoder       )
								return vocab






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self.sp_model.encode(_lowerCAmelCase   ,out_type=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(_lowerCAmelCase       ) == 0:
														# Convert sentence piece unk token to fairseq unk token index
														return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  []
								lowerCamelCase__         =  """"""
								lowerCamelCase__         =  False
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				if not prev_is_special:
																										out_string += " "
																				out_string += self.sp_model.decode(_lowerCAmelCase       ) + token
																				lowerCamelCase__         =  True
																				lowerCamelCase__         =  []
														else:
																				current_sub_tokens.append(_lowerCAmelCase       )
																				lowerCamelCase__         =  False
								out_string += self.sp_model.decode(_lowerCAmelCase       )
								return out_string.strip()






		def __getstate__(      self       ):
								lowerCamelCase__         =  self.__dict__.copy()
								lowerCamelCase__         =  None
								return state






		def __setstate__(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  d

								# for backward compatibility
								if not hasattr(self   ,"""sp_model_kwargs"""       ):
														lowerCamelCase__         =  {}

								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(self.vocab_file       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								if not os.path.isdir(_lowerCAmelCase       ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
														return
								lowerCamelCase__         =  os.path.join(
								    _lowerCAmelCase   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

								if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowerCAmelCase       ) and os.path.isfile(self.vocab_file       ):
														copyfile(self.vocab_file   ,_lowerCAmelCase       )
								elif not os.path.isfile(self.vocab_file       ):
														with open(_lowerCAmelCase   ,"""wb"""       ) as fi:
																				lowerCamelCase__         =  self.sp_model.serialized_model_proto()
																				fi.write(_lowerCAmelCase       )

								return (out_vocab_file,)

