






# 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       (			_a      ):
	snake_case__							:     Union[str, Any]     	=  """naver-clova-ix/donut-base-finetuned-docvqa"""
	snake_case__							:     int     	=  (
	    """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."""
	)
	snake_case__							:     Any     	=  """document_qa"""
	snake_case__							:     List[str]     	=  AutoProcessor
	snake_case__							:     Optional[Any]     	=  VisionEncoderDecoderModel

	snake_case__							:     Optional[Any]     	=  ["""image""", """text"""]
	snake_case__							:     Tuple     	=  ["""text"""]





	def __init__(		self				:					Optional[int] ,  *__lowerCamelCase				:					Tuple ,  **__lowerCamelCase				:					Optional[int]	):
		if not is_vision_available():
			raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""	)

		super().__init__(*__lowerCamelCase ,  **__lowerCamelCase	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					"Image" ,  __lowerCamelCase				:					str	):
		UpperCamelCase     :int          =	"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
		UpperCamelCase     :List[Any]          =	task_prompt.replace("""{user_input}""" ,  __lowerCamelCase	)
		UpperCamelCase     :Tuple          =	self.pre_processor.tokenizer(
		    __lowerCamelCase ,  add_special_tokens=__lowerCamelCase ,  return_tensors="""pt"""	).input_ids
		UpperCamelCase     :List[str]          =	self.pre_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values

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





	def 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					List[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 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					str	):
		UpperCamelCase     :Optional[int]          =	self.pre_processor.batch_decode(__lowerCamelCase	)[0]
		UpperCamelCase     :Any          =	sequence.replace(self.pre_processor.tokenizer.eos_token ,  """"""	)
		UpperCamelCase     :List[Any]          =	sequence.replace(self.pre_processor.tokenizer.pad_token ,  """"""	)
		UpperCamelCase     :Optional[int]          =	re.sub(R"""<.*?>""" ,  """""" ,  __lowerCamelCase ,  count=1	).strip()  # remove first task start token
		UpperCamelCase     :str          =	self.pre_processor.tokenajson(__lowerCamelCase	)

		return sequence["answer"]






"""simple docstring"""




from __future__ import annotations



def     a__ (				snake_case__       , snake_case__				)						->					bool:
			if len(snake_case__				) == 0:
						return False
			lowerCamelCase			      =					len(snake_case__				) // 2
			if a_list[midpoint] == item:
						return True
			if item < a_list[midpoint]:
						return binary_search(a_list[:midpoint]       , snake_case__				)
			else:
						return binary_search(a_list[midpoint + 1 :]       , snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[Any]     							=							input("""Enter numbers separated by comma:\n""").strip()
					lowerCAmelCase							:			Optional[Any]     							=							[int(item.strip()) for item in user_input.split(""",""")]
					lowerCAmelCase							:			Optional[int]     							=							int(input("""Enter the number to be found in the list:\n""").strip())
					lowerCAmelCase							:			Union[str, Any]     							=							"""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")



