



'''simple docstring'''




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase							:List[str]				    =					logging.get_logger(__name__)

lowerCamelCase							: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_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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowerCamelCase							:int				    =					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for attribute in key.split("""."""    ):
						A_	:						Tuple	=     getattr(lowercase_						,      lowercase_    )

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

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[str]	=     []
			A_	:						Optional[Any]	=     fairseq_model.state_dict()

			A_	:						Any	=     hf_model.feature_extractor
			A_	:						Optional[int]	=     hf_model.adapter

			for name, value in fairseq_dict.items():
						A_	:						List[str]	=     False
						if "conv_layers" in name:
									load_conv_layer(
									    lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      hf_model.config.feat_extract_norm == """group"""						,      )
									A_	:						Optional[int]	=     True
						elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
									load_adapter(lowercase_						,      lowercase_						,      lowercase_						,      lowercase_    )
									A_	:						Any	=     True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
															A_	:						int	=     True
															if "*" in mapped_key:
																		A_	:						Dict	=     name.split(lowercase_    )[0].split("""."""    )[-2]
																		A_	:						Optional[Any]	=     mapped_key.replace("""*"""						,      lowercase_    )
															if "weight_g" in name:
																		A_	:						List[Any]	=     """weight_g"""
															elif "weight_v" in name:
																		A_	:						List[str]	=     """weight_v"""
															elif "bias" in name:
																		A_	:						Optional[Any]	=     """bias"""
															elif "weight" in name:
																		A_	:						Dict	=     """weight"""
															else:
																		A_	:						str	=     None
															set_recursively(lowercase_						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_    )
												continue
						if not is_used:
									unused_weights.append(lowercase_    )

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[Any]	=     full_name.split("""adaptor."""    )[-1]
			A_	:						Dict	=     name.split("""."""    )

			if items[1].isdigit():
						A_	:						str	=     int(items[1]    )
			else:
						A_	:						Dict	=     None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
												A_	:						Tuple	=     value
												logger.info(f'Adapter proj layer norm bias was initialized from {full_name}.'    )
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
												A_	:						str	=     value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), f'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
												A_	:						Dict	=     value
												logger.info(f'Adapter proj layer bias was initialized from {full_name}.'    )
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), f'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
												A_	:						Union[str, Any]	=     value
												logger.info(f'Adapter proj layer weight was initialized from {full_name}.'    )
			elif isinstance(lowercase_						,      lowercase_    ):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
									A_	:						str	=     value
									logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
									A_	:						List[Any]	=     value
									logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
			else:
						unused_weights.append(lowercase_    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[str]	=     emb.weight.shape
			A_	:						Union[str, Any]	=     nn.Linear(lowercase_						,      lowercase_						,      bias=lowercase_    )
			A_	:						Optional[Any]	=     emb.weight.data
			return lin_layer



@torch.no_grad()
def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):
			'''simple docstring'''





			A_	:						Optional[Any]	=     WavaVecaConfig.from_pretrained(
			    lowercase_						,      add_adapter=lowercase_						,      adapter_stride=lowercase_						,      adapter_kernel_size=lowercase_						,      use_auth_token=lowercase_						,      output_hidden_size=lowercase_						,      )
			A_	:						List[str]	=     MBartConfig.from_pretrained(lowercase_    )

			# load model
			A_	:						Any	=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]						,      arg_overrides={
			        """config_yaml""": config_yaml_path,
			        """data""": """/""".join(dict_path.split("""/"""    )[:-1]    ),
			        """w2v_path""": checkpoint_path,
			        """load_pretrained_decoder_from""": None,
			    }						,      )
			A_	:						Tuple	=     model[0].eval()

			# load feature extractor
			A_	:						str	=     WavaVecaFeatureExtractor.from_pretrained(lowercase_						,      use_auth_token=lowercase_    )

			# set weights for wav2vec2 encoder
			A_	:						Optional[Any]	=     WavaVecaModel(lowercase_    )

			recursively_load_weights_wavaveca(model.encoder						,      lowercase_    )

			# load decoder weights
			A_	:						Optional[int]	=     MBartForCausalLM(lowercase_    )
			A_	:						List[Any]	=     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,      strict=lowercase_    )
			logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'    )
			logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'    )

			A_	:						Union[str, Any]	=     SpeechEncoderDecoderModel(encoder=lowercase_						,      decoder=lowercase_    )
			A_	:						Optional[int]	=     False

			A_	:						Dict	=     MBartaaTokenizer(lowercase_    )
			tokenizer.save_pretrained(lowercase_    )

			A_	:						str	=     hf_wavavec.config.to_dict()
			A_	:						Union[str, Any]	=     tokenizer.pad_token_id
			A_	:						List[str]	=     tokenizer.bos_token_id
			A_	:						Tuple	=     tokenizer.eos_token_id
			A_	:						List[Any]	=     """mbart50"""
			A_	:						Tuple	=     """wav2vec2"""

			A_	:						Union[str, Any]	=     tokenizer.eos_token_id
			A_	:						Tuple	=     25_00_04
			A_	:						int	=     tokenizer.eos_token_id

			A_	:						Any	=     SpeechEncoderDecoderConfig.from_dict(lowercase_    )

			hf_wavavec.save_pretrained(lowercase_    )
			feature_extractor.save_pretrained(lowercase_    )


if __name__ == "__main__":
						lowerCamelCase							:int				    =					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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
						parser.add_argument(
						    '''--encoder_config_path''',
						    default='''facebook/wav2vec2-xls-r-1b''',
						    type=str,
						    help='''Path to hf encoder wav2vec2 checkpoint config''',
						)
						parser.add_argument(
						    '''--decoder_config_path''',
						    default='''facebook/mbart-large-50-one-to-many-mmt''',
						    type=str,
						    help='''Path to hf decoder checkpoint config''',
						)
						parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
						parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
						parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
						parser.add_argument('''--encoder_output_dim''', default=1_0_2_4, type=int, help='''encoder output dim''')
						parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

						lowerCamelCase							:List[str]				    =					parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    args.config_yaml_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    add_adapter=args.add_adapter,
						    adapter_kernel_size=args.adapter_kernel_size,
						    adapter_stride=args.adapter_stride,
						    decoder_start_token_id=args.start_token_id,
						    encoder_output_dim=args.encoder_output_dim,
						)


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[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			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



