

# 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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   "naver-clova-ix/donut-base-finetuned-docvqa"
					__magic_name__        =   (
					    "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."
					)
					__magic_name__        =   "document_qa"
					__magic_name__        =   AutoProcessor
					__magic_name__        =   VisionEncoderDecoderModel

					__magic_name__        =   ["image", "text"]
					__magic_name__        =   ["text"]





					def __init__(				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						if not is_vision_available():
							raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."						)

						super().__init__(*lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Tuple									= "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
						UpperCAmelCase__:	int									= task_prompt.replace("{user_input}"				,      lowerCamelCase__						)
						UpperCAmelCase__:	Union[str, Any]									= self.pre_processor.tokenizer(
						    lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      return_tensors="pt"						).input_ids
						UpperCAmelCase__:	Dict									= self.pre_processor(lowerCamelCase__				,      return_tensors="pt"						).pixel_values

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





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

						return sequence["answer"]


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()