

'''simple docstring'''

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      lowerCAmelCase   (					__a      ):
       lowerCAmelCase_				=       ['''image_processor''', '''tokenizer''']
       lowerCAmelCase_				=       '''BridgeTowerImageProcessor'''
       lowerCAmelCase_				=       ('''RobertaTokenizer''', '''RobertaTokenizerFast''')







       def __init__(							self	:       Optional[Any]  ,					__lowercase	:       Any  ,					__lowercase	:       int					):







            """simple docstring"""







            super().__init__(lowerCAmelCase__  ,					lowerCAmelCase__					)







       def __call__(							self	:       List[Any]  ,					__lowercase	:       Union[str, Any]  ,					__lowercase	:       str = None  ,					__lowercase	:       Dict = True  ,					__lowercase	:       Union[str, Any] = False  ,					__lowercase	:       Dict = None  ,					__lowercase	:       Optional[int] = None  ,					__lowercase	:       Optional[int] = 0  ,					__lowercase	:       List[str] = None  ,					__lowercase	:       Optional[int] = None  ,					__lowercase	:       Tuple = None  ,					__lowercase	:       Dict = False  ,					__lowercase	:       Any = False  ,					__lowercase	:       Any = False  ,					__lowercase	:       Optional[int] = False  ,					__lowercase	:       int = True  ,					__lowercase	:       Optional[int] = None  ,					**__lowercase	:       Any  ,					):







            """simple docstring"""







            __lowercase							=self.tokenizer(
                text=lowerCAmelCase__  ,					add_special_tokens=lowerCAmelCase__  ,					padding=lowerCAmelCase__  ,					truncation=lowerCAmelCase__  ,					max_length=lowerCAmelCase__  ,					stride=lowerCAmelCase__  ,					pad_to_multiple_of=lowerCAmelCase__  ,					return_token_type_ids=lowerCAmelCase__  ,					return_attention_mask=lowerCAmelCase__  ,					return_overflowing_tokens=lowerCAmelCase__  ,					return_special_tokens_mask=lowerCAmelCase__  ,					return_offsets_mapping=lowerCAmelCase__  ,					return_length=lowerCAmelCase__  ,					verbose=lowerCAmelCase__  ,					return_tensors=lowerCAmelCase__  ,					**lowerCAmelCase__  ,					)
            # add pixel_values + pixel_mask
            __lowercase							=self.image_processor(
                lowerCAmelCase__  ,					return_tensors=lowerCAmelCase__  ,					do_normalize=lowerCAmelCase__  ,					do_center_crop=lowerCAmelCase__  ,					**lowerCAmelCase__					)
            encoding.update(lowerCAmelCase__					)

            return encoding







       def 	snake_case      (							self	:       List[str]  ,					*__lowercase	:       int  ,					**__lowercase	:       Tuple					):







            """simple docstring"""







            return self.tokenizer.batch_decode(*lowerCAmelCase__  ,					**lowerCAmelCase__					)







       def 	snake_case      (							self	:       List[str]  ,					*__lowercase	:       Dict  ,					**__lowercase	:       List[Any]					):







            """simple docstring"""







            return self.tokenizer.decode(*lowerCAmelCase__  ,					**lowerCAmelCase__					)







       @property
       def 	snake_case      (							self	:       int					):







            """simple docstring"""







            __lowercase							=self.tokenizer.model_input_names
            __lowercase							=self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''dandelin/vilt-b32-finetuned-vqa'''
		__lowercase							:  str						   =		(
		    '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
		    '''image containing the information, as well as a `question` which should be the question in English. It '''
		    '''returns a text that is the answer to the question.'''
		)
		__lowercase							:  Optional[int]						   =		'''image_qa'''
		__lowercase							:  Union[str, Any]						   =		AutoProcessor
		__lowercase							:  Optional[Any]						   =		AutoModelForVisualQuestionAnswering

		__lowercase							:  List[str]						   =		['''image''', '''text''']
		__lowercase							:  Optional[Any]						   =		['''text''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""vision"""])
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				return self.pre_processor(lowerCAmelCase__							,  lowerCAmelCase__							,  return_tensors="""pt""")




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				with torch.no_grad():
						return self.model(**lowerCAmelCase__).logits




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = outputs.argmax(-1).item()
				return self.model.config.idalabel[idx]




