



"""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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
		from PIL import Image

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							'naver-clova-ix/donut-base-finetuned-docvqa'
					_a         =							(
					    'This is a tool that answers a question about an document (pdf). It takes an input named `document` which '
					    'should be the document containing the information, as well as a `question` that is the question about the '
					    'document. It returns a text that contains the answer to the question.'
					)
					_a         =							'document_qa'
					_a         =							AutoProcessor
					_a         =							VisionEncoderDecoderModel

					_a         =							['image', 'text']
					_a         =							['text']



					def __init__( self							:						Union[str, Any],						*lowerCamelCase							:						int,						**lowerCamelCase							:						List[str]  )->      Optional[int]:
								if not is_vision_available():
											raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''  )

								super().__init__(*lowerCamelCase,						**lowerCamelCase  )



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						"Image",						lowerCamelCase							:						str  )->      Optional[Any]:
								lowerCamelCase__	:	List[Any]				='''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
								lowerCamelCase__	:	Dict				=task_prompt.replace('''{user_input}''',						lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=self.pre_processor.tokenizer(
								    lowerCamelCase,						add_special_tokens=lowerCamelCase,						return_tensors='''pt'''  ).input_ids
								lowerCamelCase__	:	Union[str, Any]				=self.pre_processor(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values

								return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Tuple  )->      Any:
								return self.model.generate(
								    inputs['''pixel_values'''].to(self.device  ),						decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device  ),						max_length=self.model.decoder.config.max_position_embeddings,						early_stopping=lowerCamelCase,						pad_token_id=self.pre_processor.tokenizer.pad_token_id,						eos_token_id=self.pre_processor.tokenizer.eos_token_id,						use_cache=lowerCamelCase,						num_beams=1,						bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]],						return_dict_in_generate=lowerCamelCase,						).sequences




					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Tuple  )->      str:
								lowerCamelCase__	:	Dict				=self.pre_processor.batch_decode(lowerCamelCase  )[0]
								lowerCamelCase__	:	Optional[int]				=sequence.replace(self.pre_processor.tokenizer.eos_token,						''''''  )
								lowerCamelCase__	:	Dict				=sequence.replace(self.pre_processor.tokenizer.pad_token,						''''''  )
								lowerCamelCase__	:	Optional[int]				=re.sub(r'''<.*?>''',						'''''',						lowerCamelCase,						count=1  ).strip()  # remove first task start token
								lowerCamelCase__	:	int				=self.pre_processor.tokenajson(lowerCamelCase  )

								return sequence["answer"]









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""

			# 1. Validate that path exists between current and next vertices
			if graph[path[curr_ind - 1]][next_ver] == 0:
						return False

			# 2. Validate that next vertex is not already in path
			return not any(vertex == next_ver for vertex in path			)






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					list[int]							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			# Base Case
			if curr_ind == len(__lowerCamelCase			):
						# return whether path exists between current and starting vertices
						return graph[path[curr_ind - 1]][path[0]] == 1

			# Recursive Step
			for next_ver in range(0							,	len(__lowerCamelCase			)			):
						if valid_connection(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			):
									# Insert current vertex  into path as next transition
									lowerCamelCase__	:	Tuple				=next_ver
									# Validate created path
									if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	curr_ind + 1			):
												return True
									# Backtrack
									lowerCamelCase__	:	int				=-1
			return False






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int = 0			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=[-1] * (len(__lowerCamelCase			) + 1)
			# initialize start and end of path with starting index
			lowerCamelCase__	:	Union[str, Any]				=start_index
			# evaluate and if we find answer return path either return empty array
			return path if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	1			) else []




