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()
_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_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",
}
_UpperCAmelCase            =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Optional[Any]						,__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		for attribute in key.split('.'     ):
				A_		:    Dict         =				getattr(_snake_case						,_snake_case     )

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

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




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




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

		A_		:    Union[str, Any]         =				hf_model.feature_extractor
		A_		:    Dict         =				hf_model.adapter

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

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




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Any						,__lowercase						:			str						,__lowercase						:			int						,__lowercase						:			str     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			List[str]						,__lowercase						:			int						,__lowercase						:			Optional[int]						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    str         =				full_name.split('adaptor.'     )[-1]
		A_		:    Dict         =				name.split('.'     )

		if items[1].isdigit():
				A_		:    List[str]         =				int(items[1]     )
		else:
				A_		:    Optional[int]         =				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_		:    Optional[int]         =				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_		:    Dict         =				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_		:    Union[str, Any]         =				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_		:    Tuple         =				value
								logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''     )
		elif isinstance(_snake_case						,_snake_case     ):
				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_		:    Optional[int]         =				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(_snake_case     )




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




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




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			int						,__lowercase						:			str						,__lowercase						:			List[str]						,__lowercase						:			Optional[Any]						,__lowercase						:			List[str]						,__lowercase						:			List[str]						,__lowercase						:			int						,__lowercase						:			Dict						,__lowercase						:			List[str]						,__lowercase						:			Tuple						,):
		'''simple docstring'''




		A_		:    Tuple         =				WavaVecaConfig.from_pretrained(
		    _snake_case						,add_adapter=_snake_case						,adapter_stride=_snake_case						,adapter_kernel_size=_snake_case						,use_auth_token=_snake_case						,output_hidden_size=_snake_case						,)
		A_		:    Union[str, Any]         =				MBartConfig.from_pretrained(_snake_case     )

		# load model
		A_		:    Optional[int]         =				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_		:    Optional[int]         =				model[0].eval()

		# load feature extractor
		A_		:    Union[str, Any]         =				WavaVecaFeatureExtractor.from_pretrained(_snake_case						,use_auth_token=_snake_case     )

		# set weights for wav2vec2 encoder
		A_		:    int         =				WavaVecaModel(_snake_case     )

		recursively_load_weights_wavaveca(model.encoder						,_snake_case     )

		# load decoder weights
		A_		:    Dict         =				MBartForCausalLM(_snake_case     )
		A_		:    Optional[int]         =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,strict=_snake_case     )
		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_		:    Tuple         =				SpeechEncoderDecoderModel(encoder=_snake_case						,decoder=_snake_case     )
		A_		:    Tuple         =				False

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

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

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

		A_		:    int         =				SpeechEncoderDecoderConfig.from_dict(_snake_case     )

		hf_wavavec.save_pretrained(_snake_case     )
		feature_extractor.save_pretrained(_snake_case     )


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("""--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=1024, type=int, help="""encoder output dim""")
	parser.add_argument("""--start_token_id""", default=250004, type=int, help="""`decoder_start_token_id` of model config""")

	_UpperCAmelCase            =   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
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_UpperCAmelCase            =		logging.get_logger(__name__)


_UpperCAmelCase            =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

_UpperCAmelCase            =		{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				(
		    list(range(ord('!'     )						,ord('~'     ) + 1     )     ) + list(range(ord('¡'     )						,ord('¬'     ) + 1     )     ) + list(range(ord('®'     )						,ord('ÿ'     ) + 1     )     )
		)
		A_		:    Any         =				bs[:]
		A_		:    Optional[Any]         =				0
		for b in range(2**8     ):
				if b not in bs:
						bs.append(__lowercase     )
						cs.append(2**8 + n     )
						n += 1
		A_		:    str         =				[chr(__lowercase     ) for n in cs]
		return dict(zip(__lowercase						,__lowercase     )     )




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    List[str]         =				set()
		A_		:    List[Any]         =				word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)     )
				A_		:    Union[str, Any]         =				char
		return pairs





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']
				def __init__(						self		,     lowercase		,     lowercase		,     lowercase="replace"		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=False		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else bos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else eos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else sep_token
						A_		:    Tuple         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else cls_token
						A_		:    List[str]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else unk_token
						A_		:    Optional[int]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						A_		:    int         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

						super().__init__(
						    errors=lowercase		,     bos_token=lowercase		,     eos_token=lowercase		,     unk_token=lowercase		,     sep_token=lowercase		,     cls_token=lowercase		,     pad_token=lowercase		,     mask_token=lowercase		,     add_prefix_space=lowercase		,     **lowercase		,     )

						with open(lowercase		,     encoding='utf-8' ) as vocab_handle:
								A_		:    int         =				json.load(lowercase )
						A_		:    Union[str, Any]         =				{v: k for k, v in self.encoder.items()}
						A_		:    Union[str, Any]         =				errors  # how to handle errors in decoding
						A_		:    Tuple         =				bytes_to_unicode()
						A_		:    str         =				{v: k for k, v in self.byte_encoder.items()}
						with open(lowercase		,     encoding='utf-8' ) as merges_handle:
								A_		:    str         =				merges_handle.read().split('\n' )[1:-1]
						A_		:    Optional[Any]         =				[tuple(merge.split() ) for merge in bpe_merges]
						A_		:    Any         =				dict(zip(lowercase		,     range(len(lowercase ) ) ) )
						A_		:    str         =				{}
						A_		:    List[Any]         =				add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						A_		:    Optional[Any]         =				re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return len(self.encoder )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return dict(self.encoder		,     **self.added_tokens_encoder )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.cache:
								return self.cache[token]
						A_		:    List[Any]         =				tuple(lowercase )
						A_		:    List[Any]         =				get_pairs(lowercase )

						if not pairs:
								return token

						while True:
								A_		:    List[str]         =				min(lowercase		,     key=lambda lowercase : self.bpe_ranks.get(lowercase		,     float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								A_       ,      A_		:    Optional[Any]         =				bigram
								A_		:    List[Any]         =				[]
								A_		:    Tuple         =				0
								while i < len(lowercase ):
										try:
												A_		:    Optional[Any]         =				word.index(lowercase		,     lowercase )
										except ValueError:
												new_word.extend(word[i:] )
												break
										else:
												new_word.extend(word[i:j] )
												A_		:    int         =				j

										if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								A_		:    Union[str, Any]         =				tuple(lowercase )
								A_		:    Tuple         =				new_word
								if len(lowercase ) == 1:
										break
								else:
										A_		:    Optional[int]         =				get_pairs(lowercase )
						A_		:    Optional[Any]         =				' '.join(lowercase )
						A_		:    str         =				word
						return word
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						for token in re.findall(self.pat		,     lowercase ):
								A_		:    Optional[Any]         =				''.join(
								    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase ).split(' ' ) )
						return bpe_tokens
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.encoder.get(lowercase		,     self.encoder.get(self.unk_token ) )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.decoder.get(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				''.join(lowercase )
						A_		:    List[Any]         =				bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'		,     errors=self.errors )
						return text
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if not os.path.isdir(lowercase ):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
								return
						A_		:    Optional[int]         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						A_		:    Dict         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

						with open(lowercase		,     'w'		,     encoding='utf-8' ) as f:
								f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=lowercase		,     ensure_ascii=lowercase ) + '\n' )

						A_		:    Dict         =				0
						with open(lowercase		,     'w'		,     encoding='utf-8' ) as writer:
								writer.write('#version: 0.2\n' )
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda lowercase : kv[1] ):
										if index != token_index:
												logger.warning(
												    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
												    ' Please check that the tokenizer is not corrupted!' )
												A_		:    int         =				token_index
										writer.write(' '.join(lowercase ) + '\n' )
										index += 1

						return vocab_file, merge_file
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A_		:    Tuple         =				[self.cls_token_id]
						A_		:    Any         =				[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





						if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase		,     token_ids_a=lowercase		,     already_has_special_tokens=lowercase )

						if token_ids_a is None:
								return [1] + ([0] * len(lowercase )) + [1]
						return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[self.sep_token_id]
						A_		:    List[Any]         =				[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		,     lowercase		,     lowercase=False		,     **lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				kwargs.pop('add_prefix_space'		,     self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(lowercase ) > 0 and not text[0].isspace()):
								A_		:    int         =				' ' + text
						return (text, kwargs)






