




"""simple docstring"""
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

a_				:    Tuple										=     logging.get_logger(__name__)

@add_end_docstrings(lowercase__				)
class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	*__a						,	**__a     ):
								super().__init__(*__a						,	**__a     )
								self.check_model_type(__a     )
					def     snake_case_				(    self						,	__a=None						,	__a=None						,	__a=None						,	**__a     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				{}, {}
								if padding is not None:
											__lowerCamelCase					:	List[str]	       =				padding
								if truncation is not None:
											__lowerCamelCase					:	str	       =				truncation
								if top_k is not None:
											__lowerCamelCase					:	str	       =				top_k
								return preprocess_params, {}, postprocess_params
					def __call__(    self						,	__a						,	__a = None						,	**__a     ):
								if isinstance(__a						,	(Image.Image, str)     ) and isinstance(__a						,	__a     ):
											__lowerCamelCase					:	Optional[Any]	       =				{'image': image, 'question': question}
								else:
											__lowerCamelCase					:	int	       =				image
								__lowerCamelCase					:	int	       =				super().__call__(__a						,	**__a     )
								return results
					def     snake_case_				(    self						,	__a						,	__a=False						,	__a=False     ):
								__lowerCamelCase					:	Optional[Any]	       =				load_image(inputs['image']     )
								__lowerCamelCase					:	str	       =				self.tokenizer(
								    inputs['question']						,	return_tensors=self.framework						,	padding=__a						,	truncation=__a     )
								__lowerCamelCase					:	str	       =				self.image_processor(images=__a						,	return_tensors=self.framework     )
								model_inputs.update(__a     )
								return model_inputs
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	str	       =				self.model(**__a     )
								return model_outputs





					def     snake_case_				(    self						,	__a						,	__a=5     ):
								if top_k > self.model.config.num_labels:
											__lowerCamelCase					:	Union[str, Any]	       =				self.model.config.num_labels

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

								__lowerCamelCase					:	Dict	       =				scores.tolist()
								__lowerCamelCase					:	Tuple	       =				ids.tolist()
								return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__a						,	__a     )]









"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_barthez import BarthezTokenizer
else:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


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

								super().__init__(
								    __a						,	tokenizer_file=__a						,	bos_token=__a						,	eos_token=__a						,	unk_token=__a						,	sep_token=__a						,	cls_token=__a						,	pad_token=__a						,	mask_token=__a						,	**__a						,	)

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	int	       =				[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     snake_case_				(    self						,	__a						,	__a = None     ):
								if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'     )

								if not os.path.isdir(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				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     ):
											copyfile(self.vocab_file						,	__a     )

								return (out_vocab_file,)



