





'''simple docstring'''







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

import sentencepiece as spm

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


lowerCAmelCase_					:    int      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    str      	=			'▁'

lowerCAmelCase_					:    Tuple      	=			{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_					:    Union[str, Any]      	=			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCAmelCase_					:    int      	=			{
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
lowerCAmelCase_					:    Optional[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', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =VOCAB_FILES_NAMES
				__a            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a            =PRETRAINED_VOCAB_FILES_MAP
				__a            =['input_ids', 'attention_mask']

				__a            =[]
				__a            =[]




				def __init__(  self     :					Optional[int]					,				__a     :					Dict					,				__a     :					Optional[Any]=None					,				__a     :					Optional[int]=None					,				__a     :					Tuple="</s>"					,				__a     :					List[Any]="</s>"					,				__a     :					Any="<s>"					,				__a     :					int="<unk>"					,				__a     :					Dict="<pad>"					,				__a     :					Tuple="<mask>"					,				__a     :					Optional[Dict[str, Any]] = None					,				**__a     :					List[Any]					,				):
								# Mask token behave like a normal word, i.e. include the space before it
								_a								= AddedToken(__a					,				lstrip=__a					,				rstrip=__a					) if isinstance(__a					,				__a					) else mask_token

								_a								= {} if sp_model_kwargs is None else sp_model_kwargs

								_a								= kwargs.get("additional_special_tokens"					,				[]					)
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    src_lang=__a					,				tgt_lang=__a					,				eos_token=__a					,				unk_token=__a					,				sep_token=__a					,				cls_token=__a					,				pad_token=__a					,				mask_token=__a					,				sp_model_kwargs=self.sp_model_kwargs					,				**__a					,				)

								_a								= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(str(__a					)					)
								_a								= vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								_a								= {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								_a								= 1

								_a								= len(self.sp_model					)
								_a								= {
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a					)
								}
								_a								= {v: k for k, v in self.lang_code_to_id.items()}
								_a								= len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
								_a								= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

								_a								= src_lang if src_lang is not None else "en_XX"
								_a								= self.lang_code_to_id[self._src_lang]
								_a								= tgt_lang
								self.set_src_lang_special_tokens(self._src_lang					)




				@property
				def   UpperCamelCase__   (  self     :					int					):
								return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token




				@property
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								return self._src_lang




				@src_lang.setter
				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					str					):
								_a								= new_src_lang
								self.set_src_lang_special_tokens(self._src_lang					)




				def __getstate__(  self     :					List[str]					):
								_a								= self.__dict__.copy()
								_a								= None
								return state




				def __setstate__(  self     :					Union[str, Any]					,				__a     :					Dict					):
								_a								= d

								# for backward compatibility
								if not hasattr(self					,				"sp_model_kwargs"					):
												_a								= {}

								_a								= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.vocab_file					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= {self.convert_ids_to_tokens(__a					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					str					):
								return self.sp_model.encode(__a					,				out_type=__a					)




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					str					):
								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								_a								= self.sp_model.PieceToId(__a					)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					int					):
								if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset					)




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					Dict					):
								_a								= []
								_a								= ""
								_a								= False
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																if not prev_is_special:
																				out_string += " "
																out_string += self.sp_model.decode(__a					) + token
																_a								= True
																_a								= []
												else:
																current_sub_tokens.append(__a					)
																_a								= False
								out_string += self.sp_model.decode(__a					)
								return out_string.strip()




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					str					,				__a     :					Optional[str] = None					):
								if not os.path.isdir(__a					):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
												return
								_a								= os.path.join(
								    __a					,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(__a					) and os.path.isfile(self.vocab_file					):
												copyfile(self.vocab_file					,				__a					)
								elif not os.path.isfile(self.vocab_file					):
												with open(__a					,				"wb"					) as fi:
																_a								= self.sp_model.serialized_model_proto()
																fi.write(__a					)

								return (out_vocab_file,)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					List[int]					,				__a     :					Optional[List[int]] = None					,				__a     :					bool = False					):

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

								_a								= [1] * len(self.prefix_tokens					)
								_a								= [1] * len(self.suffix_tokens					)
								if token_ids_a is None:
												return prefix_ones + ([0] * len(__a					)) + suffix_ones
								return prefix_ones + ([0] * len(__a					)) + ([0] * len(__a					)) + suffix_ones




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					List[int]					,				__a     :					Optional[List[int]] = None					):
								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   UpperCamelCase__   (  self     :					str					,				__a     :					str					,				__a     :					str					,				__a     :					Optional[str]					,				__a     :					Optional[str]					,				**__a     :					Tuple					):
								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(__a					,				add_special_tokens=__a					,				return_tensors=__a					,				**__a					)
								_a								= self.convert_tokens_to_ids(__a					)
								_a								= tgt_lang_id
								return inputs




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[str]					,				__a     :					str = "en_XX"					,				__a     :					Optional[List[str]] = None					,				__a     :					str = "ro_RO"					,				**__a     :					int					,				):
								_a								= src_lang
								_a								= tgt_lang
								return super().prepare_seqaseq_batch(__a					,				__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					):
								return self.set_src_lang_special_tokens(self.src_lang					)




				def   UpperCamelCase__   (  self     :					int					):
								return self.set_tgt_lang_special_tokens(self.tgt_lang					)




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					str					):
								_a								= self.lang_code_to_id[src_lang]
								_a								= [self.cur_lang_code_id]
								_a								= [self.eos_token_id]




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					str					):
								_a								= self.lang_code_to_id[tgt_lang]
								_a								= [self.cur_lang_code_id]
								_a								= [self.eos_token_id]







'''simple docstring'''







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXForCausalLM',
					    'GPTNeoXForQuestionAnswering',
					    'GPTNeoXForSequenceClassification',
					    'GPTNeoXForTokenClassification',
					    'GPTNeoXLayer',
					    'GPTNeoXModel',
					    'GPTNeoXPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_					:    List[str]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
