






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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						'▁'

__UpperCAmelCase								=						{'vocab_file': 'sentencepiece.bpe.model'}

__UpperCAmelCase								=						{
    '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'
        ),
    }
}

__UpperCAmelCase								=						{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
__UpperCAmelCase								=						['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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  VOCAB_FILES_NAMES
		_snake_case    :    str       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case    :    str       						=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case    :    Union[str, Any]       						=  ['''input_ids''', '''attention_mask''']

		_snake_case    :    List[int]       						=  []
		_snake_case    :    List[int]       						=  []



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

							UpperCAmelCase_    :      Union[str, Any]            =						{} if sp_model_kwargs is None else sp_model_kwargs

							UpperCAmelCase_    :      str            =						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=_UpperCamelCase				,		tgt_lang=_UpperCamelCase				,		eos_token=_UpperCamelCase				,		unk_token=_UpperCamelCase				,		sep_token=_UpperCamelCase				,		cls_token=_UpperCamelCase				,		pad_token=_UpperCamelCase				,		mask_token=_UpperCamelCase				,		sp_model_kwargs=self.sp_model_kwargs				,		**_UpperCamelCase				,		)

							UpperCAmelCase_    :      Dict            =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(_UpperCamelCase   )   )
							UpperCAmelCase_    :      str            =						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_    :      Optional[int]            =						{'<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_    :      Union[str, Any]            =						1

							UpperCAmelCase_    :      Union[str, Any]            =						len(self.sp_model   )
							UpperCAmelCase_    :      Optional[Any]            =						{
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCamelCase   )
							}
							UpperCAmelCase_    :      int            =						{v: k for k, v in self.lang_code_to_id.items()}
							UpperCAmelCase_    :      Optional[int]            =						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_    :      Optional[Any]            =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}

							UpperCAmelCase_    :      Optional[int]            =						src_lang if src_lang is not None else 'en_XX'
							UpperCAmelCase_    :      Tuple            =						self.lang_code_to_id[self._src_lang]
							UpperCAmelCase_    :      Any            =						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   )	->							str:
							return self._src_lang



		@src_lang.setter
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      Optional[Any]            =						new_src_lang
							self.set_src_lang_special_tokens(self._src_lang   )



		def __getstate__(							self   )	->							Dict:
							UpperCAmelCase_    :      List[str]            =						self.__dict__.copy()
							UpperCAmelCase_    :      str            =						None
							return state



		def __setstate__(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      Optional[int]            =						d

							# for backward compatibility
							if not hasattr(self				,		'sp_model_kwargs'   ):
												UpperCAmelCase_    :      List[str]            =						{}

							UpperCAmelCase_    :      Optional[int]            =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      List[str]            =						{self.convert_ids_to_tokens(_UpperCamelCase   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab



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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							int:
							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_    :      int            =						self.sp_model.PieceToId(_UpperCamelCase   )

							# 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				,		_UpperCamelCase   )	->							str:
							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				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						[]
							UpperCAmelCase_    :      List[Any]            =						''
							UpperCAmelCase_    :      Optional[Any]            =						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(_UpperCamelCase   ) + token
																	UpperCAmelCase_    :      Optional[Any]            =						True
																	UpperCAmelCase_    :      Union[str, Any]            =						[]
												else:
																	current_sub_tokens.append(_UpperCamelCase   )
																	UpperCAmelCase_    :      Optional[int]            =						False
							out_string += self.sp_model.decode(_UpperCamelCase   )
							return out_string.strip()



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

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

							return (out_vocab_file,)



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

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

							UpperCAmelCase_    :      List[str]            =						[1] * len(self.prefix_tokens   )
							UpperCAmelCase_    :      Optional[int]            =						[1] * len(self.suffix_tokens   )
							if token_ids_a is None:
												return prefix_ones + ([0] * len(_UpperCamelCase   )) + suffix_ones
							return prefix_ones + ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = 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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							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_    :      str            =						src_lang
							UpperCAmelCase_    :      Any            =						self(_UpperCamelCase				,		add_special_tokens=_UpperCamelCase				,		return_tensors=_UpperCamelCase				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						self.convert_tokens_to_ids(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						tgt_lang_id
							return inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = "en_XX"				,		_UpperCamelCase = None				,		_UpperCamelCase = "ro_RO"				,		**_UpperCamelCase				,		)	->							BatchEncoding:
							UpperCAmelCase_    :      Optional[int]            =						src_lang
							UpperCAmelCase_    :      Optional[Any]            =						tgt_lang
							return super().prepare_seqaseq_batch(_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )



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



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							return self.set_tgt_lang_special_tokens(self.tgt_lang   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      str            =						self.lang_code_to_id[src_lang]
							UpperCAmelCase_    :      Union[str, Any]            =						[self.cur_lang_code_id]
							UpperCAmelCase_    :      Union[str, Any]            =						[self.eos_token_id]



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      int            =						self.lang_code_to_id[tgt_lang]
							UpperCAmelCase_    :      str            =						[self.cur_lang_code_id]
							UpperCAmelCase_    :      List[str]            =						[self.eos_token_id]














from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__UpperCAmelCase								=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case			)
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							int:
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )
							self.check_model_type(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						{}, {}
							if padding is not None:
												UpperCAmelCase_    :      List[str]            =						padding
							if truncation is not None:
												UpperCAmelCase_    :      Tuple            =						truncation
							if top_k is not None:
												UpperCAmelCase_    :      Dict            =						top_k
							return preprocess_params, {}, postprocess_params



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							if isinstance(_UpperCamelCase				,		(Image.Image, str)   ) and isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						{'image': image, 'question': question}
							else:
												UpperCAmelCase_    :      List[str]            =						image
							UpperCAmelCase_    :      Optional[Any]            =						super().__call__(_UpperCamelCase				,		**_UpperCamelCase   )
							return results



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=False   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						load_image(inputs['image']   )
							UpperCAmelCase_    :      Dict            =						self.tokenizer(
							    inputs['question']				,		return_tensors=self.framework				,		padding=_UpperCamelCase				,		truncation=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.image_processor(images=_UpperCamelCase				,		return_tensors=self.framework   )
							model_inputs.update(_UpperCamelCase   )
							return model_inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							UpperCAmelCase_    :      Any            =						self.model(**_UpperCamelCase   )
							return model_outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=5   )	->							str:
							if top_k > self.model.config.num_labels:
												UpperCAmelCase_    :      Union[str, Any]            =						self.model.config.num_labels

							if self.framework == "pt":
												UpperCAmelCase_    :      List[str]            =						model_outputs.logits.sigmoid()[0]
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						probs.topk(_UpperCamelCase   )
							else:
												raise ValueError(f"Unsupported framework: {self.framework}"   )

							UpperCAmelCase_    :      Optional[Any]            =						scores.tolist()
							UpperCAmelCase_    :      Tuple            =						ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase				,		_UpperCamelCase   )]






