

'''simple docstring'''





import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       """▁"""

UpperCamelCase_    						=       {"""vocab_file""": """prophetnet.tokenizer"""}

UpperCamelCase_    						=       {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

UpperCamelCase_    						=       {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

UpperCamelCase_    						=       {
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}






def 				_lowerCAmelCase			(   __magic_name__						:     Any						)				->  str:
						lowercase :							Any    		=collections.OrderedDict()
						with open(__magic_name__   , '''r'''   , encoding='''utf-8'''						) as reader:
												lowercase :							str    		=reader.readlines()
						for index, token in enumerate(__magic_name__						):
												lowercase :							str    		=token.rstrip('''\n'''						)
												lowercase :							Dict    		=index
						return vocab







class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    VOCAB_FILES_NAMES
				lowerCamelCase_		       =    PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_		       =    ['input_ids', 'attention_mask']







				def __init__(       self     :		List[str]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Dict="[SEP]"		,					UpperCAmelCase__     :		Optional[Any]="[SEP]"		,					UpperCAmelCase__     :		List[str]="[SEP]"		,					UpperCAmelCase__     :		Optional[int]="[UNK]"		,					UpperCAmelCase__     :		Tuple="[PAD]"		,					UpperCAmelCase__     :		Optional[int]="[CLS]"		,					UpperCAmelCase__     :		Union[str, Any]="[MASK]"		,					UpperCAmelCase__     :		Optional[Dict[str, Any]] = None		,					**UpperCAmelCase__     :		Optional[int]		,					):
										'''simple docstring'''
										lowercase :							int    		={} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=UpperCAmelCase__		,					eos_token=UpperCAmelCase__		,					sep_token=UpperCAmelCase__		,					unk_token=UpperCAmelCase__		,					pad_token=UpperCAmelCase__		,					cls_token=UpperCAmelCase__		,					mask_token=UpperCAmelCase__		,					sp_model_kwargs=self.sp_model_kwargs		,					**UpperCAmelCase__		,					)

										try:
																import sentencepiece as spm
										except ImportError:
																logger.warning(
																    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																    ''' pip install sentencepiece'''   )
																raise

										lowercase :							Dict    		=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(str(UpperCAmelCase__   )   )
										lowercase :							Any    		=vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# put special tokens and [unused] tokens into the vocab
										lowercase :							Tuple    		={'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

										for i in range(10   ):
																lowercase :							Any    		=F'''[unused{i}]'''
																lowercase :							List[Any]    		=5 + i

										# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
										lowercase :							Dict    		=12
										lowercase :							List[Any]    		={v: k for k, v in self.fairseq_tokens_to_ids.items()}
										for k in self.fairseq_tokens_to_ids.keys():
																self.unique_no_split_tokens.append(UpperCAmelCase__   )







				def __getstate__(       self     :		str   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.__dict__.copy()
										lowercase :							str    		=None
										return state







				def __setstate__(       self     :		List[str]		,					UpperCAmelCase__     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=d
										try:
																import sentencepiece as spm
										except ImportError:
																logger.warning(
																    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																    ''' pip install sentencepiece'''   )
																raise

										# for backward compatibility
										if not hasattr(self		,					'''sp_model_kwargs'''   ):
																lowercase :							List[str]    		={}

										lowercase :							Union[str, Any]    		=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(self.vocab_file   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None		,					UpperCAmelCase__     :		bool = False   ):
										'''simple docstring'''

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=UpperCAmelCase__		,					token_ids_a=UpperCAmelCase__		,					already_has_special_tokens=UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None   ):
										'''simple docstring'''
										lowercase :							str    		=[self.sep_token_id]

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







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return len(self.sp_model   ) + self.fairseq_offset







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							str    		={self.convert_ids_to_tokens(UpperCAmelCase__   ): i for i in range(self.vocab_size   )}
										vocab.update(self.added_tokens_encoder   )
										return vocab







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return self.sp_model.encode(UpperCAmelCase__		,					out_type=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										lowercase :							Dict    		=self.sp_model.PieceToId(UpperCAmelCase__   )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										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  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										lowercase :							Tuple    		=''''''.join(UpperCAmelCase__   ).replace(UpperCAmelCase__		,					''' '''   ).strip()
										return out_string







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Optional[str] = None   ):
										'''simple docstring'''
										if not os.path.isdir(UpperCAmelCase__   ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
																return
										lowercase :							Tuple    		=os.path.join(
										    UpperCAmelCase__		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

										if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase__   ) and os.path.isfile(self.vocab_file   ):
																copyfile(self.vocab_file		,					UpperCAmelCase__   )
										elif not os.path.isfile(self.vocab_file   ):
																with open(UpperCAmelCase__		,					'''wb'''   ) as fi:
																						lowercase :							Any    		=self.sp_model.serialized_model_proto()
																						fi.write(UpperCAmelCase__   )

										return (out_vocab_file,)







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None   ):
										'''simple docstring'''

										if token_ids_a is None:
																return token_ids_a + [self.sep_token_id]
										lowercase :							Any    		=[self.sep_token_id]
										return token_ids_a + sep + token_ids_a + sep








'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    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.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase_    						=       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Tuple						)				->  str:
						for attribute in key.split('''.'''						):
												lowercase :							Tuple    		=getattr(__magic_name__   , __magic_name__						)

						if weight_type is not None:
												lowercase :							Optional[int]    		=getattr(__magic_name__   , __magic_name__						).shape
						else:
												lowercase :							List[Any]    		=hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    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 :							Any    		=value
						elif weight_type == "weight_g":
												lowercase :							List[Any]    		=value
						elif weight_type == "weight_v":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "bias":
												lowercase :							Tuple    		=value
						elif weight_type == "running_mean":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "running_var":
												lowercase :							str    		=value
						elif weight_type == "num_batches_tracked":
												lowercase :							Tuple    		=value
						elif weight_type == "inv_freq":
												lowercase :							Optional[Any]    		=value
						else:
												lowercase :							Tuple    		=value

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






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						lowercase :							Optional[int]    		=[]
						lowercase :							Tuple    		=fairseq_model.state_dict()

						lowercase :							List[Any]    		=hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase :							Tuple    		=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , hf_model.config.feat_extract_norm == '''group'''   , )
																		lowercase :							List[Any]    		=True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase :							Optional[int]    		='''wav2vec2_conformer.''' + 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 :							Union[str, Any]    		=True
																														if "*" in mapped_key:
																																				lowercase :							Optional[int]    		=name.split(__magic_name__						)[0].split('''.'''						)[-2]
																																				lowercase :							List[str]    		=mapped_key.replace('''*'''   , __magic_name__						)
																														if "pos_bias_u" in name:
																																				lowercase :							Optional[Any]    		=None
																														elif "pos_bias_v" in name:
																																				lowercase :							Union[str, Any]    		=None
																														elif "weight_g" in name:
																																				lowercase :							Any    		='''weight_g'''
																														elif "weight_v" in name:
																																				lowercase :							Tuple    		='''weight_v'''
																														elif "bias" in name:
																																				lowercase :							Optional[int]    		='''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase :							Optional[int]    		='''weight'''
																														elif "running_mean" in name:
																																				lowercase :							Union[str, Any]    		='''running_mean'''
																														elif "inv_freq" in name:
																																				lowercase :							Any    		='''inv_freq'''
																														elif "running_var" in name:
																																				lowercase :							Tuple    		='''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase :							Dict    		='''num_batches_tracked'''
																														else:
																																				lowercase :							str    		=None
																														set_recursively(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
																								continue
												if not is_used:
																		unused_weights.append(__magic_name__						)

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






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple						)				->  int:
						lowercase :							Optional[Any]    		=full_name.split('''conv_layers.'''						)[-1]
						lowercase :							Any    		=name.split('''.'''						)
						lowercase :							List[str]    		=int(items[0]						)
						lowercase :							Union[str, Any]    		=int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
																		lowercase :							Union[str, Any]    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
																		lowercase :							Optional[Any]    		=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:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
																		lowercase :							Optional[int]    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
																		lowercase :							str    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(__magic_name__						)






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple=None   , __magic_name__						:     Optional[Any]=None   , __magic_name__						:     Union[str, Any]=True						)				->  Union[str, Any]:
						if config_path is not None:
												lowercase :							Optional[Any]    		=WavaVecaConformerConfig.from_pretrained(__magic_name__   , hidden_act='''swish'''						)
						else:
												lowercase :							Optional[int]    		=WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												lowercase :							Dict    		='''rotary'''

						if is_finetuned:
												if dict_path:
																		lowercase :							Optional[Any]    		=Dictionary.load(__magic_name__						)

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

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

												lowercase :							str    		=WavaVecaConformerForCTC(__magic_name__						)
						else:
												lowercase :							Tuple    		=WavaVecaConformerForPreTraining(__magic_name__						)

						if is_finetuned:
												lowercase		,	lowercase		,	lowercase :							Optional[int]    		=fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]   , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
						else:
												lowercase :							Dict    		=argparse.Namespace(task='''audio_pretraining'''						)
												lowercase :							Optional[int]    		=fairseq.tasks.setup_task(__magic_name__						)

												lowercase		,	lowercase		,	lowercase :							str    		=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   , task=__magic_name__						)

						lowercase :							List[Any]    		=model[0].eval()

						recursively_load_weights(__magic_name__   , __magic_name__   , not is_finetuned						)

						hf_wavavec.save_pretrained(__magic_name__						)


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





