


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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbart import MBartTokenizer
else:
      snake_case							:	Dict					      =				None


snake_case							:	List[Any]					      =				logging.get_logger(__name__)


snake_case							:	str					      =				{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:	Any					      =				{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

snake_case							:	Tuple					      =				{
    '''facebook/mbart-large-en-ro''': 10_24,
    '''facebook/mbart-large-cc25''': 10_24,
}

# fmt: off
snake_case							:	List[Any]					      =				['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']







class 	snake_case_       (lowercase__				):
     UpperCAmelCase__ :   int   				=      VOCAB_FILES_NAMES
     UpperCAmelCase__ :   List[str]   				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :   List[str]   				=      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :   List[Any]   				=      ["""input_ids""", """attention_mask"""]
     UpperCAmelCase__ :   Any   				=      MBartTokenizer

     UpperCAmelCase__ :   List[int]   				=      []
     UpperCAmelCase__ :   List[int]   				=      []
     def __init__(				self       :Tuple      ,__snake_case       :Tuple=None      ,__snake_case       :Optional[int]=None      ,__snake_case       :Union[str, Any]="<s>"      ,__snake_case       :int="</s>"      ,__snake_case       :Union[str, Any]="</s>"      ,__snake_case       :Union[str, Any]="<s>"      ,__snake_case       :int="<unk>"      ,__snake_case       :Tuple="<pad>"      ,__snake_case       :List[str]="<mask>"      ,__snake_case       :List[str]=None      ,__snake_case       :List[Any]=None      ,__snake_case       :Any=None      ,**__snake_case       :Any      ,) ->   str:
            a__					=					AddedToken(lowercase_      ,lstrip=lowercase_      ,rstrip=lowercase_				) if isinstance(lowercase_      ,lowercase_				) else mask_token

            super().__init__(
                vocab_file=lowercase_      ,tokenizer_file=lowercase_      ,bos_token=lowercase_      ,eos_token=lowercase_      ,sep_token=lowercase_      ,cls_token=lowercase_      ,unk_token=lowercase_      ,pad_token=lowercase_      ,mask_token=lowercase_      ,src_lang=lowercase_      ,tgt_lang=lowercase_      ,additional_special_tokens=lowercase_      ,**lowercase_      ,)

            a__					=					vocab_file
            a__					=					False if not self.vocab_file else True

            a__					=					FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

            self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}				)
            a__					=					{
                lang_code: self.convert_tokens_to_ids(lowercase_				) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            a__					=					src_lang if src_lang is not None else "en_XX"
            a__					=					self.convert_tokens_to_ids(self._src_lang				)
            a__					=					tgt_lang
            self.set_src_lang_special_tokens(self._src_lang				)
     @property
     def 			lowerCamelCase__(				self       :Dict				) ->   Optional[int]:
            return self._src_lang
     @src_lang.setter
     def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :List[Any]				) ->   List[Any]:
            a__					=					new_src_lang
            self.set_src_lang_special_tokens(self._src_lang				)
     def 			lowerCamelCase__(				self       :int      ,__snake_case       :Any      ,__snake_case       :Dict = None				) ->   Union[str, Any]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
     def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :List[Any]      ,__snake_case       :List[str] = None				) ->   List[Any]:
            a__					=					[self.sep_token_id]
            a__					=					[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]
     def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :List[str]      ,__snake_case       :Any      ,__snake_case       :Tuple      ,__snake_case       :Dict      ,**__snake_case       :Optional[int]				) ->   Any:
            if src_lang is None or tgt_lang is None:
                   raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
            a__					=					src_lang
            a__					=					self(lowercase_      ,add_special_tokens=lowercase_      ,return_tensors=lowercase_      ,**lowercase_				)
            a__					=					self.convert_tokens_to_ids(lowercase_				)
            a__					=					tgt_lang_id
            return inputs
     def 			lowerCamelCase__(				self       :int      ,__snake_case       :List[str]      ,__snake_case       :str = "en_XX"      ,__snake_case       :Dict = None      ,__snake_case       :List[str] = "ro_RO"      ,**__snake_case       :Dict      ,) ->   Union[str, Any]:
            a__					=					src_lang
            a__					=					tgt_lang
            return super().prepare_seqaseq_batch(lowercase_      ,lowercase_      ,**lowercase_				)
     def 			lowerCamelCase__(				self       :Optional[int]				) ->   Optional[int]:
            return self.set_src_lang_special_tokens(self.src_lang				)
     def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang				)
     def 			lowerCamelCase__(				self       :int      ,__snake_case       :Optional[Any]				) ->   Union[str, Any]:
            a__					=					self.convert_tokens_to_ids(lowercase_				)
            a__					=					[]
            a__					=					[self.eos_token_id, self.cur_lang_code]

            a__					=					self.convert_ids_to_tokens(self.prefix_tokens				)
            a__					=					self.convert_ids_to_tokens(self.suffix_tokens				)

            a__					=					processors.TemplateProcessing(
                single=prefix_tokens_str + ['$A'] + suffix_tokens_str      ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str      ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,self.prefix_tokens + self.suffix_tokens				)				)      ,)
     def 			lowerCamelCase__(				self       :str      ,__snake_case       :List[str]				) ->   Tuple:
            a__					=					self.convert_tokens_to_ids(lowercase_				)
            a__					=					[]
            a__					=					[self.eos_token_id, self.cur_lang_code]

            a__					=					self.convert_ids_to_tokens(self.prefix_tokens				)
            a__					=					self.convert_ids_to_tokens(self.suffix_tokens				)

            a__					=					processors.TemplateProcessing(
                single=prefix_tokens_str + ['$A'] + suffix_tokens_str      ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str      ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,self.prefix_tokens + self.suffix_tokens				)				)      ,)



     def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :str      ,__snake_case       :int = None				) ->   List[Any]:
            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(lowercase_				):
                   logger.error(F'Vocabulary path ({save_directory}) should be a directory.'				)
                   return
            a__					=					os.path.join(
                lowercase_      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

            if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				):
                   copyfile(self.vocab_file      ,lowercase_				)

            return (out_vocab_file,)








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)




