






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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					"▁"

lowercase  							=					{"vocab_file": "sentencepiece.bpe.model"}

lowercase  							=					{
    "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"
        ),
    }
}

lowercase  							=					{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
lowercase  							=					["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  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




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

	lowerCAmelCase													= []
	lowerCAmelCase													= []




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

								snake_case_				=							{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=a       ,			eos_token=a       ,			unk_token=a       ,			sep_token=a       ,			cls_token=a       ,			pad_token=a       ,			mask_token=a       ,			tokenizer_file=a       ,			src_lang=a       ,			tgt_lang=a       ,			additional_special_tokens=a       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**a       ,			)

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(str(a					)					)
								snake_case_				=							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
								snake_case_				=							{'<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
								snake_case_				=							1

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

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

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

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




	def __getstate__(       self					)       ->    Union[str, Any]:
								snake_case_				=							self.__dict__.copy()
								snake_case_				=							None
								snake_case_				=							self.sp_model.serialized_model_proto()
								return state




	def __setstate__(       self       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							d

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

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								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					)       ->    str:
								return self._src_lang




	@src_lang.setter
	def 							_UpperCamelCase			(       self       ,			a					)       ->    None:
								snake_case_				=							new_src_lang
								self.set_src_lang_special_tokens(self._src_lang					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None       ,			a = False					)       ->    List[int]:

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

								snake_case_				=							[1] * len(self.prefix_tokens					)
								snake_case_				=							[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       ,			a       ,			a = None					)       ->    List[int]:
								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       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			**a					)       ->    Dict:
								if src_lang is None or tgt_lang is None:
															raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
								snake_case_				=							src_lang
								snake_case_				=							self(a       ,			add_special_tokens=a       ,			return_tensors=a       ,			**a					)
								snake_case_				=							self.convert_tokens_to_ids(a					)
								snake_case_				=							tgt_lang_id
								return inputs




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							{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       ,			a					)       ->    List[str]:
								return self.sp_model.encode(a       ,			out_type=a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Any:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								snake_case_				=							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       ,			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       ,			a					)       ->    str:
								snake_case_				=							''.join(a					).replace(a       ,			' '					).strip()
								return out_string




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								if not os.path.isdir(a					):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								snake_case_				=							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:
																						snake_case_				=							self.sp_model.serialized_model_proto()
																						fi.write(a					)

								return (out_vocab_file,)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = "en_XX"       ,			a = None       ,			a = "ro_RO"       ,			**a       ,			)       ->    BatchEncoding:
								snake_case_				=							src_lang
								snake_case_				=							tgt_lang
								return super().prepare_seqaseq_batch(a       ,			a       ,			**a					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								return self.set_src_lang_special_tokens(self.src_lang					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								return self.set_tgt_lang_special_tokens(self.tgt_lang					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    None:
								snake_case_				=							self.lang_code_to_id[src_lang]
								snake_case_				=							[]
								snake_case_				=							[self.eos_token_id, self.cur_lang_code]






	def 							_UpperCamelCase			(       self       ,			a					)       ->    None:
								snake_case_				=							self.lang_code_to_id[lang]
								snake_case_				=							[]
								snake_case_				=							[self.eos_token_id, self.cur_lang_code]













import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase  							=					logging.get_logger(__name__)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			*a       ,			**a					)       ->    None:
								warnings.warn(
								    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
								    ' Please use PerceiverImageProcessor instead.'       ,			a       ,			)
								super().__init__(*a       ,			**a					)





