


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Tuple=None       ):






							"""simple docstring"""





							assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
							UpperCamelCase    :	Optional[Any]   	=		nn.Parameter(lowercase_       )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
														UpperCamelCase    :	Optional[Any]   	=		nn.Parameter(lowercase_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






							"""simple docstring"""





							UpperCamelCase    :	List[Any]   	=		np.asarray(weights[0]       )
							UpperCamelCase    :	Any   	=		np.asarray(weights[1]       )
							UpperCamelCase    :	str   	=		np.asarray(weights[2]       )

							set_param(
							    torch_layer.self_attention.query_key							,			torch.tensor(lowercase_       ).transpose(1							,			2       ).contiguous().view(-1							,			lowercase_       )							,			)
							set_param(
							    torch_layer.self_attention.value							,			torch.tensor(lowercase_       ).transpose(1							,			2       ).contiguous().view(-1							,			lowercase_       )							,			)
							set_param(
							    torch_layer.output.dense							,			torch.tensor(lowercase_       ).view(-1							,			lowercase_       ).contiguous().transpose(0							,			1       )							,			)


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






							"""simple docstring"""





							UpperCamelCase    :	int   	=		np.asarray(weights[0]       )
							UpperCamelCase    :	Optional[int]   	=		np.asarray(weights[1]       )
							UpperCamelCase    :	str   	=		np.asarray(weights[2]       )
							UpperCamelCase    :	List[str]   	=		np.asarray(weights[3]       )

							set_param(
							    torch_layer.self_attention.query							,			torch.tensor(lowercase_       ).transpose(1							,			2       ).contiguous().view(-1							,			lowercase_       )							,			)
							set_param(
							    torch_layer.self_attention.key							,			torch.tensor(lowercase_       ).transpose(1							,			2       ).contiguous().view(-1							,			lowercase_       )							,			)
							set_param(
							    torch_layer.self_attention.value							,			torch.tensor(lowercase_       ).transpose(1							,			2       ).contiguous().view(-1							,			lowercase_       )							,			)
							set_param(
							    torch_layer.output.dense							,			torch.tensor(lowercase_       ).view(-1							,			lowercase_       ).contiguous().transpose(0							,			1       )							,			)


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		str       ):






							"""simple docstring"""





							UpperCamelCase    :	Union[str, Any]   	=		weights[0][0][0]
							UpperCamelCase    :	Any   	=		np.asarray(layer_norm_a[0]       )
							UpperCamelCase    :	int   	=		np.asarray(layer_norm_a[1]       )
							set_param(
							    torch_block.attention.layer_norm							,			torch.tensor(lowercase_       )							,			torch.tensor(lowercase_       )							,			)

							# lsh weights + output
							UpperCamelCase    :	List[Any]   	=		weights[0][1]
							if len(lowercase_       ) < 4:
														set_layer_weights_in_torch_lsh(lowercase_							,			torch_block.attention							,			lowercase_       )
							else:
														set_layer_weights_in_torch_local(lowercase_							,			torch_block.attention							,			lowercase_       )

							# intermediate weighs
							UpperCamelCase    :	Dict   	=		weights[2][0][1][2]

							# Chunked Feed Forward
							if len(lowercase_       ) == 4:
														UpperCamelCase    :	Tuple   	=		intermediate_weights[2]

							# layernorm 2
							UpperCamelCase    :	int   	=		np.asarray(intermediate_weights[0][0]       )
							UpperCamelCase    :	Optional[int]   	=		np.asarray(intermediate_weights[0][1]       )
							set_param(
							    torch_block.feed_forward.layer_norm							,			torch.tensor(lowercase_       )							,			torch.tensor(lowercase_       )							,			)

							# intermediate dense
							UpperCamelCase    :	List[Any]   	=		np.asarray(intermediate_weights[1][0]       )
							UpperCamelCase    :	Tuple   	=		np.asarray(intermediate_weights[1][1]       )
							set_param(
							    torch_block.feed_forward.dense.dense							,			torch.tensor(lowercase_       ).transpose(0							,			1       ).contiguous()							,			torch.tensor(lowercase_       )							,			)

							# intermediate out
							UpperCamelCase    :	Union[str, Any]   	=		np.asarray(intermediate_weights[4][0]       )
							UpperCamelCase    :	List[Any]   	=		np.asarray(intermediate_weights[4][1]       )
							set_param(
							    torch_block.feed_forward.output.dense							,			torch.tensor(lowercase_       ).transpose(0							,			1       ).contiguous()							,			torch.tensor(lowercase_       )							,			)


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






							"""simple docstring"""





							UpperCamelCase    :	Optional[Any]   	=		torch_model.reformer

							# word embeds
							UpperCamelCase    :	List[str]   	=		np.asarray(weights[1]       )
							set_param(
							    torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(lowercase_       )							,			)

							if isinstance(weights[3]							,			lowercase_       ):
														UpperCamelCase    :	Tuple   	=		torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights       )       ):
																					UpperCamelCase    :	List[str]   	=		np.asarray(weights[3][emb_idx][0]       )
																					assert (
																					    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																					), F"""{position_embeddings[emb_idx]} emb does not match"""
																					UpperCamelCase    :	Dict   	=		nn.Parameter(torch.tensor(lowercase_       )       )

							UpperCamelCase    :	Optional[Any]   	=		weights[5]
							assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
							    lowercase_       ), "HF and trax model do not have the same number of layers"
							for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers       ):
														UpperCamelCase    :	List[Any]   	=		trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(lowercase_							,			lowercase_							,			lowercase_       )

							# output layer norm
							UpperCamelCase    :	int   	=		np.asarray(weights[7][0]       )
							UpperCamelCase    :	List[Any]   	=		np.asarray(weights[7][1]       )
							set_param(
							    torch_model_reformer.encoder.layer_norm							,			torch.tensor(lowercase_       )							,			torch.tensor(lowercase_       )							,			)

							# output embeddings
							UpperCamelCase    :	str   	=		np.asarray(weights[9][0]       )
							UpperCamelCase    :	Any   	=		np.asarray(weights[9][1]       )
							set_param(
							    torch_model.lm_head.decoder							,			torch.tensor(lowercase_       ).transpose(0							,			1       ).contiguous()							,			torch.tensor(lowercase_       )							,			)


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		int       ):






							"""simple docstring"""





							UpperCamelCase    :	Dict   	=		ReformerConfig.from_json_file(lowercase_       )
							print(F"""Building PyTorch model from configuration: {config}"""       )
							UpperCamelCase    :	Dict   	=		ReformerModelWithLMHead(lowercase_       )

							with open(lowercase_							,			'''rb'''       ) as f:
														UpperCamelCase    :	Optional[Any]   	=		pickle.load(lowercase_       )["weights"]

							set_model_weights_in_torch(lowercase_							,			lowercase_							,			config.hidden_size       )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
							torch.save(model.state_dict()							,			lowercase_       )


if __name__ == "__main__":
		__UpperCAmelCase : Dict 			=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained Reformer model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCAmelCase : Tuple 			=				parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase : Any 			=				logging.get_logger(__name__)

__UpperCAmelCase : int 			=				"▁"

__UpperCAmelCase : Tuple 			=				{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

__UpperCAmelCase : Dict 			=				{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

__UpperCAmelCase : Dict 			=				{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

__UpperCAmelCase : str 			=				{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

__UpperCAmelCase : Optional[int] 			=				{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					["input_ids"]

       __UpperCamelCase      :     List[str]							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     List[Any]							=					PRETRAINED_INIT_CONFIGURATION
       __UpperCamelCase      :     List[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     int							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     List[str]							=					RESOURCE_FILES_NAMES






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="utf8" ,					__SCREAMING_SNAKE_CASE="[UNK]" ,					__SCREAMING_SNAKE_CASE="[SEP]" ,					__SCREAMING_SNAKE_CASE="[PAD]" ,					__SCREAMING_SNAKE_CASE="[CLS]" ,					__SCREAMING_SNAKE_CASE="[MASK]" ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	int   	=		{} if sp_model_kwargs is None else sp_model_kwargs
              super().__init__(
                  do_lower_case=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					vocab_file=__SCREAMING_SNAKE_CASE ,					encoding=__SCREAMING_SNAKE_CASE ,					sp_model_kwargs=self.sp_model_kwargs ,					**__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[str]   	=		do_lower_case
              UpperCamelCase    :	Dict   	=		sentencepiece_model_ckpt
              UpperCamelCase    :	str   	=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
              self.sp_model.Load(__SCREAMING_SNAKE_CASE						)

              # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
              if vocab_file is not None:
                     UpperCamelCase    :	Optional[Any]   	=		self.load_vocab(filepath=__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	int   	=		{self.sp_model.id_to_piece(__SCREAMING_SNAKE_CASE						): id for id in range(self.sp_model.get_piece_size()						)}
              UpperCamelCase    :	str   	=		{v: k for k, v in self.vocab.items()}






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if text is None:
                     return None

              UpperCamelCase    :	str   	=		self.tokenize(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase	, UpperCamelCase    :	str   	=		'''''', []

              for i, ch in enumerate(__SCREAMING_SNAKE_CASE						):
                     if ch in self.SP_CHAR_MAPPING:
                            UpperCamelCase    :	Optional[int]   	=		self.SP_CHAR_MAPPING.get(__SCREAMING_SNAKE_CASE						)
                     else:
                            UpperCamelCase    :	Optional[Any]   	=		unicodedata.normalize('''NFKC''' ,					__SCREAMING_SNAKE_CASE						)
                     if self.is_whitespace(__SCREAMING_SNAKE_CASE						):
                            continue
                     normalized_text += ch
                     char_mapping.extend([i] * len(__SCREAMING_SNAKE_CASE						)						)

              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Tuple   	=		normalized_text, [], 0

              if self.do_lower_case:
                     UpperCamelCase    :	Tuple   	=		text.lower()

              for token in split_tokens:
                     if token[:1] == "▁":
                            UpperCamelCase    :	Any   	=		token[1:]
                     UpperCamelCase    :	Optional[int]   	=		text[offset:].index(__SCREAMING_SNAKE_CASE						) + offset
                     UpperCamelCase    :	List[Any]   	=		start + len(__SCREAMING_SNAKE_CASE						)

                     token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)						)
                     UpperCamelCase    :	str   	=		end
              return token_mapping






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return len(self.vocab						)






       def  _lowercase   (     self						):




              """simple docstring"""




              return dict(self.vocab ,					**self.added_tokens_encoder						)






       def __getstate__(     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.__dict__.copy()
              UpperCamelCase    :	str   	=		None
              return state






       def __setstate__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		d

              # for backward compatibility
              if not hasattr(self ,					'''sp_model_kwargs'''						):
                     UpperCamelCase    :	Optional[int]   	=		{}

              UpperCamelCase    :	Optional[Any]   	=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
              self.sp_model.Load(self.sentencepiece_model_ckpt						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return "".join((self.SP_CHAR_MAPPING.get(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) for c in text)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=64 ,					__SCREAMING_SNAKE_CASE=0.1						):




              """simple docstring"""




              if self.sp_model_kwargs.get('''enable_sampling'''						) is True:
                     UpperCamelCase    :	List[str]   	=		True
              if self.sp_model_kwargs.get('''alpha'''						) is not None:
                     UpperCamelCase    :	Any   	=		self.sp_model_kwargs.get('''alpha'''						)
              if self.sp_model_kwargs.get('''nbest_size'''						) is not None:
                     UpperCamelCase    :	Tuple   	=		self.sp_model_kwargs.get('''nbest_size'''						)

              if not enable_sampling:
                     UpperCamelCase    :	int   	=		self.sp_model.EncodeAsPieces(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Optional[Any]   	=		self.sp_model.SampleEncodeAsPieces(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		[]
              for pi, piece in enumerate(__SCREAMING_SNAKE_CASE						):
                     if piece == SPIECE_UNDERLINE:
                            if not pieces[pi + 1].startswith(__SCREAMING_SNAKE_CASE						) and pi != 0:
                                   new_pieces.append(__SCREAMING_SNAKE_CASE						)
                                   continue
                            else:
                                   continue
                     UpperCamelCase    :	Any   	=		0
                     for i, chunk in enumerate(__SCREAMING_SNAKE_CASE						):
                            if chunk == SPIECE_UNDERLINE:
                                   continue
                            if self.is_ch_char(__SCREAMING_SNAKE_CASE						) or self.is_punct(__SCREAMING_SNAKE_CASE						):
                                   if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                          new_pieces.append(piece[lst_i:i]						)
                                   new_pieces.append(__SCREAMING_SNAKE_CASE						)
                                   UpperCamelCase    :	Dict   	=		i + 1
                            elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                                   if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                          new_pieces.append(piece[lst_i:i]						)
                                   UpperCamelCase    :	Union[str, Any]   	=		i
                            elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                                   if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                          new_pieces.append(piece[lst_i:i]						)
                                   UpperCamelCase    :	Any   	=		i
                     if len(__SCREAMING_SNAKE_CASE						) > lst_i:
                            new_pieces.append(piece[lst_i:]						)
              return new_pieces






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		''''''.join(__SCREAMING_SNAKE_CASE						).replace(__SCREAMING_SNAKE_CASE ,					''' '''						).strip()
              return out_string






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		''''''.join(__SCREAMING_SNAKE_CASE						).replace(__SCREAMING_SNAKE_CASE ,					''' '''						).strip()
              return out_string






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.vocab.get(__SCREAMING_SNAKE_CASE ,					self.vocab.get(self.unk_token						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.reverse_vocab.get(__SCREAMING_SNAKE_CASE ,					self.unk_token						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              UpperCamelCase    :	Any   	=		[self.cls_token_id]
              UpperCamelCase    :	str   	=		[self.sep_token_id]
              return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              if offset_mapping_a is None:
                     return [(0, 0)] + offset_mapping_a + [(0, 0)]

              return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




              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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

              if token_ids_a is not None:
                     return [1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1]
              return [1] + ([0] * len(__SCREAMING_SNAKE_CASE						)) + [1]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              if token_ids_a is None:
                     # [CLS] X [SEP]
                     return (len(__SCREAMING_SNAKE_CASE						) + 2) * [0]

              # [CLS] A [SEP] [SEP] B [SEP]
              return [0] * (len(__SCREAMING_SNAKE_CASE						) + 1) + [1] * (len(__SCREAMING_SNAKE_CASE						) + 3)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if "\u4e00" <= char <= "\u9fff":
                     return True
              return False






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                     return True
              return False






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if char in ",;:.?!~，；：。？！《》【】":
                     return True
              return False






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if char == " " or char == "\t" or char == "\n" or char == "\r":
                     return True
              if len(__SCREAMING_SNAKE_CASE						) == 1:
                     UpperCamelCase    :	Optional[int]   	=		unicodedata.category(__SCREAMING_SNAKE_CASE						)
                     if cat == "Zs":
                            return True
              return False






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		{}
              with io.open(__SCREAMING_SNAKE_CASE ,					'''r''' ,					encoding='''utf-8'''						) as f:
                     for index, line in enumerate(__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		line.rstrip('''\n'''						)
                            UpperCamelCase    :	List[Any]   	=		int(__SCREAMING_SNAKE_CASE						)

              return token_to_idx






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		0
              if os.path.isdir(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Dict   	=		os.path.join(
                         __SCREAMING_SNAKE_CASE ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
              with open(__SCREAMING_SNAKE_CASE ,					'''w''' ,					encoding='''utf-8'''						) as writer:
                     for token, token_index in sorted(self.vocab.items() ,					key=lambda __SCREAMING_SNAKE_CASE						: kv[1]						):
                            if index != token_index:
                                   logger.warning(
                                       f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                                       ''' Please check that the vocabulary is not corrupted!'''						)
                                   UpperCamelCase    :	List[Any]   	=		token_index
                            writer.write(token + '''\n'''						)
                            index += 1

              UpperCamelCase    :	Tuple   	=		os.path.join(__SCREAMING_SNAKE_CASE ,					'''sentencepiece.bpe.model'''						)
              with open(__SCREAMING_SNAKE_CASE ,					'''wb'''						) as fi:
                     UpperCamelCase    :	List[Any]   	=		self.sp_model.serialized_model_proto()
                     fi.write(__SCREAMING_SNAKE_CASE						)

              return (vocab_file,)

