



"""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


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''': 10_24,
    '''facebook/mbart-large-cc25''': 10_24,
}

# 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 			lowercase__			(      A    ):
							'''simple docstring'''







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

							_UpperCAmelCase      				=					[]
							_UpperCAmelCase      				=					[]






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

									_UpperCAmelCase							 =		{} if sp_model_kwargs is None else sp_model_kwargs

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

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

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

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

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






							def __getstate__(  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.__dict__.copy()
									_UpperCAmelCase							 =		None
									_UpperCAmelCase							 =		self.sp_model.serialized_model_proto()
									return state






							def __setstate__(  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		d

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

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






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token






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






							@src_lang.setter
							def        lowerCamelCase_   (  self	,       snake_case						)			->							None:
									_UpperCAmelCase							 =		new_src_lang
									self.set_src_lang_special_tokens(self._src_lang						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None	,       snake_case = False						)			->							List[int]:

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

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = 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        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None						)			->							List[int]:
									_UpperCAmelCase							 =		[self.sep_token_id]
									_UpperCAmelCase							 =		[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	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       **snake_case						)			->							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'						)
									_UpperCAmelCase							 =		src_lang
									_UpperCAmelCase							 =		self(snake_case	,       add_special_tokens=snake_case	,       return_tensors=snake_case	,       **snake_case						)
									_UpperCAmelCase							 =		self.convert_tokens_to_ids(snake_case						)
									_UpperCAmelCase							 =		tgt_lang_id
									return inputs






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		{self.convert_ids_to_tokens(snake_case						): i for i in range(self.vocab_size						)}
									vocab.update(self.added_tokens_encoder						)
									return vocab






							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[str]:
									return self.sp_model.encode(snake_case	,       out_type=snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[str]:
									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									_UpperCAmelCase							 =		self.sp_model.PieceToId(snake_case						)

									# 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        lowerCamelCase_   (  self	,       snake_case						)			->							Any:
									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        lowerCamelCase_   (  self	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		''.join(snake_case						).replace(snake_case	,       ' '						).strip()
									return out_string






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None						)			->							Tuple[str]:
									if not os.path.isdir(snake_case						):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
											return
									_UpperCAmelCase							 =		os.path.join(
									    snake_case	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

									return (out_vocab_file,)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = "en_XX"	,       snake_case = None	,       snake_case = "ro_RO"	,       **snake_case	,       )			->							BatchEncoding:
									_UpperCAmelCase							 =		src_lang
									_UpperCAmelCase							 =		tgt_lang
									return super().prepare_seqaseq_batch(snake_case	,       snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self						)			->							str:
									return self.set_src_lang_special_tokens(self.src_lang						)






							def        lowerCamelCase_   (  self						)			->							str:
									return self.set_tgt_lang_special_tokens(self.tgt_lang						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							None:
									_UpperCAmelCase							 =		self.lang_code_to_id[src_lang]
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[self.eos_token_id, self.cur_lang_code]






							def        lowerCamelCase_   (  self	,       snake_case						)			->							None:
									_UpperCAmelCase							 =		self.lang_code_to_id[lang]
									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		[self.eos_token_id, self.cur_lang_code]







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  							=					{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CLIPSegModel''',
					    '''CLIPSegPreTrainedModel''',
					    '''CLIPSegTextModel''',
					    '''CLIPSegVisionModel''',
					    '''CLIPSegForImageSegmentation''',
					]

if TYPE_CHECKING:
					from .configuration_clipseg import (
					    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPSegConfig,
					    CLIPSegTextConfig,
					    CLIPSegVisionConfig,
					)
					from .processing_clipseg import CLIPSegProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clipseg import (
										    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPSegForImageSegmentation,
										    CLIPSegModel,
										    CLIPSegPreTrainedModel,
										    CLIPSegTextModel,
										    CLIPSegVisionModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


