


import os
from typing import List, Optional, Union

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


snake_case							:	Dict					      =				logging.get_logger(__name__)

snake_case							:	List[str]					      =				{'''vocab_file''': '''vocab.txt'''}

snake_case							:	Optional[int]					      =				{
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

snake_case							:	List[str]					      =				{
    '''facebook/esm2_t6_8M_UR50D''': 10_24,
    '''facebook/esm2_t12_35M_UR50D''': 10_24,
}






def 							__lowercase       (			__lowerCAmelCase							:    Dict       ):
							with open(__lowerCAmelCase ,   'r'       ) as f:
														a__					=					f.read().splitlines()
														return [l.strip() for l in lines]







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[str]   				=      VOCAB_FILES_NAMES
					UpperCAmelCase__ :   Optional[int]   				=      PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__ :   List[Any]   				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__ :   int   				=      ['''input_ids''', '''attention_mask''']
					def __init__(				self       :Optional[int]      ,__snake_case       :int      ,__snake_case       :int="<unk>"      ,__snake_case       :Any="<cls>"      ,__snake_case       :Optional[Any]="<pad>"      ,__snake_case       :List[str]="<mask>"      ,__snake_case       :Union[str, Any]="<eos>"      ,**__snake_case       :List[Any]      ,) ->   Dict:
												super().__init__(**__snake_case				)
												a__					=					load_vocab_file(__snake_case				)
												a__					=					dict(enumerate(self.all_tokens				)				)
												a__					=					{tok: ind for ind, tok in enumerate(self.all_tokens				)}
												a__					=					unk_token
												a__					=					cls_token
												a__					=					pad_token
												a__					=					mask_token
												a__					=					eos_token
												a__					=					self.all_tokens
												self._create_trie(self.unique_no_split_tokens				)
					def 			lowerCamelCase__(				self       :Dict      ,__snake_case       :int				) ->   str:
												return self._id_to_token.get(__snake_case      ,self.unk_token				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :str				) ->   int:
												return self._token_to_id.get(__snake_case      ,self._token_to_id.get(self.unk_token				)				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :List[Any]      ,**__snake_case       :int				) ->   Any:
												return text.split()
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :Any=False				) ->   str:
												return len(self._id_to_token				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[Any]:
												return {token: i for i, token in enumerate(self.all_tokens				)}
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :str				) ->   int:
												return self._token_to_id.get(__snake_case      ,self._token_to_id.get(self.unk_token				)				)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :int				) ->   str:
												return self._id_to_token.get(__snake_case      ,self.unk_token				)
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[int]      ,__snake_case       :Optional[List[int]] = None				) ->   List[int]:
												a__					=					[self.cls_token_id]
												a__					=					[self.eos_token_id]  # No sep token in ESM vocabulary
												if token_ids_a is None:
																			if self.eos_token_id is None:
																										return cls + token_ids_a
																			else:
																										return cls + token_ids_a + sep
												elif self.eos_token_id is None:
																			raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'				)
												return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :List      ,__snake_case       :Optional[List] = None      ,__snake_case       :bool = False				) ->   List[int]:
												if already_has_special_tokens:
																			if token_ids_a is not None:
																										raise ValueError(
																										    'You should not supply a second sequence if the provided sequence of '
																										    'ids is already formatted with special tokens for the model.'				)

																			return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
												a__					=					[1] + ([0] * len(__snake_case				)) + [1]
												if token_ids_a is not None:
																			mask += [0] * len(__snake_case				) + [1]
												return mask
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :str      ,__snake_case       :List[str]				) ->   Optional[Any]:
												a__					=					os.path.join(__snake_case      ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'				)
												with open(__snake_case      ,'w'				) as f:
																			f.write('\n'.join(self.all_tokens				)				)
												return (vocab_file,)
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   int:
												return self.get_vocab_size(with_added_tokens=__snake_case				)



					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Union[List[str], List[AddedToken]]      ,__snake_case       :bool = False				) ->   int:
												return super()._add_tokens(__snake_case      ,special_tokens=__snake_case				)








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()
snake_case							:	str					      =				logging.get_logger(__name__)

snake_case							:	Optional[Any]					      =				{
    '''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''',
}
snake_case							:	Dict					      =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict       ):
							for attribute in key.split('.'       ):
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

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

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






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[Any]       ):
							a__					=					[]
							a__					=					fairseq_model.state_dict()

							a__					=					hf_model.feature_extractor

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

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

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






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[int]       ):
							a__					=					full_name.split('conv_layers.'       )[-1]
							a__					=					name.split('.'       )
							a__					=					int(items[0]       )
							a__					=					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.'
																					)
																					a__					=					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.'
																					)
																					a__					=					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."
																					)
																					a__					=					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.'
																					)
																					a__					=					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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[Any]=None       ):
							# load the pre-trained checkpoints
							a__					=					torch.load(__lowerCAmelCase       )
							a__					=					WavLMConfigOrig(checkpoint['cfg']       )
							a__					=					WavLMOrig(__lowerCAmelCase       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

							if config_path is not None:
														a__					=					WavLMConfig.from_pretrained(__lowerCAmelCase       )
							else:
														a__					=					WavLMConfig()

							a__					=					WavLMModel(__lowerCAmelCase       )

							recursively_load_weights(__lowerCAmelCase ,   __lowerCAmelCase       )

							hf_wavlm.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	Union[str, Any]					      =				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''')
						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




