
'''simple docstring'''





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

import sentencepiece as spm

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


UpperCAmelCase    					=   "▁"

UpperCAmelCase    					=   {"vocab_file": "spiece.model"}

UpperCAmelCase    					=   {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

UpperCAmelCase    					=   {
    "google/pegasus-xsum": 512,
}


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES

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




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Tuple="<pad>",    SCREAMING_SNAKE_CASE_	:				int="</s>",    SCREAMING_SNAKE_CASE_	:				List[str]="<unk>",    SCREAMING_SNAKE_CASE_	:				List[str]="<mask_2>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<mask_1>",    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Any=1_03,    SCREAMING_SNAKE_CASE_	:				Optional[Dict[str, Any]] = None,    **SCREAMING_SNAKE_CASE_	:				str,    ):
										snake_case       :     List[str] =    offset
										if additional_special_tokens is not None:
																if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ):
																						raise TypeError(
																						    F"""additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE_      )}, but is"""
																						    F""" {type(SCREAMING_SNAKE_CASE_      )}"""      )

																snake_case       :     int =    (
																    ([mask_token_sent] + additional_special_tokens)
																    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																    else additional_special_tokens
																)
																# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																additional_special_tokens_extended += [
																    F"""<unk_{i}>""" for i in range(len(SCREAMING_SNAKE_CASE_      ),    self.offset - 1      )
																]

																if len(set(SCREAMING_SNAKE_CASE_      )      ) != len(SCREAMING_SNAKE_CASE_      ):
																						raise ValueError(
																						    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																						    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""      )
																snake_case       :     Any =    additional_special_tokens_extended
										else:
																snake_case       :     Union[str, Any] =    [mask_token_sent] if mask_token_sent is not None else []
																additional_special_tokens += [F"""<unk_{i}>""" for i in range(2,    self.offset      )]

										snake_case       :     Union[str, Any] =    {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    mask_token=SCREAMING_SNAKE_CASE_,    pad_token=SCREAMING_SNAKE_CASE_,    mask_token_sent=SCREAMING_SNAKE_CASE_,    offset=SCREAMING_SNAKE_CASE_,    additional_special_tokens=SCREAMING_SNAKE_CASE_,    sp_model_kwargs=self.sp_model_kwargs,    **SCREAMING_SNAKE_CASE_,    )
										snake_case       :     Union[str, Any] =    mask_token_sent
										snake_case       :     List[str] =    vocab_file
										snake_case       :     Tuple =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(SCREAMING_SNAKE_CASE_      )

										# add special tokens to encoder dict
										snake_case       :     Dict[int, str] =    {
										    0: self.pad_token,
										    1: self.eos_token,
										}

										if self.mask_token_sent is not None:
																self.encoder.update(
																    {
																        2: self.mask_token_sent,
																        3: self.mask_token,
																    }      )

										if self.offset > 0:
																# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																# mask_token_sent is already added to list -> so start at 1
																self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,    self.offset - 1      )}      )

										snake_case       :     Dict[str, int] =    {v: k for k, v in self.encoder.items()}




				@property
				def 	__snake_case							(							self	:				Optional[Any]      ):
										return len(self.sp_model      ) + self.offset




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     int =    {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab




				def __getstate__(							self	:				Any      ):
										snake_case       :     Tuple =    self.__dict__.copy()
										snake_case       :     List[str] =    None
										return state




				def __setstate__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     Dict =    d

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

										snake_case       :     str =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(self.vocab_file      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str      ):
										return self.sp_model.encode(SCREAMING_SNAKE_CASE_,    out_type=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
										if token in self.decoder:
																return self.decoder[token]
										elif token in self.added_tokens_decoder:
																return self.added_tokens_decoder[token]
										snake_case       :     Optional[Any] =    self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_      )
										return sp_id + self.offset




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				int      ):
										if index in self.encoder:
																return self.encoder[index]
										elif index in self.added_tokens_encoder:
																return self.added_tokens_encoder[index]
										else:
																snake_case       :     Any =    self.sp_model.IdToPiece(index - self.offset      )
										return token




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     Dict =    []
										snake_case       :     List[Any] =    ''''''
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_      ) + token
																						snake_case       :     int =    []
																else:
																						current_sub_tokens.append(SCREAMING_SNAKE_CASE_      )
										out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_      )
										return out_string.strip()




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]=False      ):
										return 1




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     str =    set(self.all_special_ids      )  # call it once instead of inside list comp
										all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

										return [1 if x in all_special_ids else 0 for x in seq]




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List,    SCREAMING_SNAKE_CASE_	:				Optional[List] = None,    SCREAMING_SNAKE_CASE_	:				bool = False      ):
										if already_has_special_tokens:
																return self._special_token_mask(SCREAMING_SNAKE_CASE_      )
										elif token_ids_a is None:
																return self._special_token_mask(SCREAMING_SNAKE_CASE_      ) + [1]
										else:
																return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple=None      ):
										if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
																return
										snake_case       :     List[Any] =    os.path.join(
										    SCREAMING_SNAKE_CASE_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE_      ) and os.path.isfile(self.vocab_file      ):
																copyfile(self.vocab_file,    SCREAMING_SNAKE_CASE_      )
										elif not os.path.isfile(self.vocab_file      ):
																with open(SCREAMING_SNAKE_CASE_,    '''wb'''      ) as fi:
																						snake_case       :     Optional[int] =    self.sp_model.serialized_model_proto()
																						fi.write(SCREAMING_SNAKE_CASE_      )

										return (out_vocab_file,)



'''simple docstring'''





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, 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.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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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    					=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def      A (       A_							:					Dict    ,							A_							:					Any    ,							A_							:					List[str]    ,							A_							:					Tuple    ,							A_							:					Optional[Any]       ):
						for attribute in key.split('''.'''       ):
												snake_case       :     Tuple =    getattr(A_    ,							A_       )

						if weight_type is not None:
												snake_case       :     Optional[Any] =    getattr(A_    ,							A_       ).shape
						else:
												snake_case       :     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       :     Optional[Any] =    value
						elif weight_type == "weight_g":
												snake_case       :     Any =    value
						elif weight_type == "weight_v":
												snake_case       :     int =    value
						elif weight_type == "bias":
												snake_case       :     Any =    value
						else:
												snake_case       :     Optional[int] =    value

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

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     Dict =    []
						snake_case       :     Optional[Any] =    fairseq_model.state_dict()

						snake_case       :     int =    hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case       :     List[Any] =    False
												if "conv_layers" in name:
																		load_conv_layer(
																		    A_    ,							A_    ,							A_    ,							A_    ,							hf_model.config.feat_extract_norm == '''group'''    ,							)
																		snake_case       :     int =    True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
																														snake_case       :     Optional[int] =    True
																														if "*" in mapped_key:
																																				snake_case       :     Dict =    name.split(A_       )[0].split('''.'''       )[-2]
																																				snake_case       :     List[str] =    mapped_key.replace('''*'''    ,							A_       )
																														if "weight_g" in name:
																																				snake_case       :     Optional[Any] =    '''weight_g'''
																														elif "weight_v" in name:
																																				snake_case       :     Optional[Any] =    '''weight_v'''
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				snake_case       :     Any =    '''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				snake_case       :     Optional[int] =    '''weight'''
																														else:
																																				snake_case       :     Union[str, Any] =    None

																														set_recursively(A_    ,							A_    ,							A_    ,							A_    ,							A_       )
																								continue
												if not is_used:
																		unused_weights.append(A_       )

						logger.warning(F"""Unused weights: {unused_weights}"""       )

def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					int    ,							A_							:					Dict    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[str] =    full_name.split('''conv_layers.'''       )[-1]
						snake_case       :     List[Any] =    name.split('''.'''       )
						snake_case       :     Dict =    int(items[0]       )
						snake_case       :     Any =    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       :     Tuple =    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       :     Dict =    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       :     str =    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       :     Tuple =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(A_       )

@torch.no_grad()
def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					Optional[Any]=None       ):
						# load the pre-trained checkpoints
						snake_case       :     Any =    torch.load(A_       )
						snake_case       :     List[str] =    WavLMConfigOrig(checkpoint['''cfg''']       )
						snake_case       :     Union[str, Any] =    WavLMOrig(A_       )
						model.load_state_dict(checkpoint['''model''']       )
						model.eval()

						if config_path is not None:
												snake_case       :     Union[str, Any] =    WavLMConfig.from_pretrained(A_       )
						else:
												snake_case       :     Any =    WavLMConfig()

						snake_case       :     Optional[int] =    WavLMModel(A_       )

						recursively_load_weights(A_    ,							A_       )

						hf_wavlm.save_pretrained(A_       )


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						UpperCAmelCase    					=   parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

