
'''simple docstring'''





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_ta import TaTokenizer
else:
						UpperCAmelCase    					=   None


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase    					=   {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = TaTokenizer

				_snake_case		    = []




				def __init__(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict=None,    SCREAMING_SNAKE_CASE_	:				int=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]="</s>",    SCREAMING_SNAKE_CASE_	:				Any="<unk>",    SCREAMING_SNAKE_CASE_	:				List[str]="<pad>",    SCREAMING_SNAKE_CASE_	:				List[str]=1_00,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    **SCREAMING_SNAKE_CASE_	:				str,    ):
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
																snake_case       :     List[Any] =    [F"""<extra_id_{i}>""" for i in range(SCREAMING_SNAKE_CASE_      )]
										elif extra_ids > 0 and additional_special_tokens is not None:
																# Check that we have the right number of extra special tokens
																snake_case       :     str =    len(set(filter(lambda SCREAMING_SNAKE_CASE_      : bool('''extra_id_''' in str(SCREAMING_SNAKE_CASE_      )      ),    SCREAMING_SNAKE_CASE_      )      )      )
																if extra_tokens != extra_ids:
																						raise ValueError(
																						    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																						    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																						    ''' tokens'''      )

										super().__init__(
										    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    pad_token=SCREAMING_SNAKE_CASE_,    extra_ids=SCREAMING_SNAKE_CASE_,    additional_special_tokens=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     str =    vocab_file
										snake_case       :     List[Any] =    False if not self.vocab_file else True
										snake_case       :     Optional[Any] =    extra_ids




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																snake_case       :     List[str] =    TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																if init_max_model_length is not None and init_max_model_length != max_model_length:
																						return init_max_model_length
																elif init_max_model_length is None:
																						warnings.warn(
																						    '''This tokenizer was incorrectly instantiated with a model max length of'''
																						    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																						    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																						    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																						    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
																						    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																						    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																						    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																						    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.''',    SCREAMING_SNAKE_CASE_,    )

										return max_model_length




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																    '''tokenizer.'''      )

										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_      ):
																copyfile(self.vocab_file,    SCREAMING_SNAKE_CASE_      )
																logger.info(F"""Copy vocab file to {out_vocab_file}"""      )

										return (out_vocab_file,)




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):
										snake_case       :     List[Any] =    token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																return self.prefix_tokens + token_ids_a
										else:
																snake_case       :     List[str] =    token_ids_a + [self.eos_token_id]
																return self.prefix_tokens + token_ids_a + token_ids_a




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):
										snake_case       :     Dict =    [self.eos_token_id]

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




				def 	__snake_case							(							self	:				Tuple      ):
										return list(
										    set(filter(lambda SCREAMING_SNAKE_CASE_      : bool(re.search(R'''<extra_id_\d+>''',    SCREAMING_SNAKE_CASE_      )      ) is not None,    self.additional_special_tokens      )      )      )




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										return [self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      ) for token in self.get_sentinel_tokens()]



'''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)

