


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







def 						SCREAMING_SNAKE_CASE__(   a__):






				def is_in_circle(a__	,a__) -> bool:
								_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =mean(
				    int(is_in_circle(uniform(-1.0	,1.0)	,uniform(-1.0	,1.0)))
				    for _ in range(__UpperCamelCase))
				# The ratio of the area for circle to square is pi/4.
				_SCREAMING_SNAKE_CASE			  =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 						SCREAMING_SNAKE_CASE__(   a__	,a__	,a__ = 0.0	,a__ = 1.0	,):
				return mean(
				    function_to_integrate(uniform(__UpperCamelCase	,__UpperCamelCase)) for _ in range(__UpperCamelCase)) * (max_value - min_value)







def 						SCREAMING_SNAKE_CASE__(   a__	,a__ = 0.0	,a__ = 1.0):






				def identity_function(a__) -> float:
								return x

				_SCREAMING_SNAKE_CASE			  =area_under_curve_estimator(
				    __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase)
				_SCREAMING_SNAKE_CASE			  =(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 						SCREAMING_SNAKE_CASE__(   a__):






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

				_SCREAMING_SNAKE_CASE			  =area_under_curve_estimator(
				    __UpperCamelCase	,__UpperCamelCase	,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()



# 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 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"naver-clova-ix/donut-base-finetuned-docvqa"
						UpperCAmelCase             =						(
						    "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."
						)
						UpperCAmelCase             =						"document_qa"
						UpperCAmelCase             =						AutoProcessor
						UpperCAmelCase             =						VisionEncoderDecoderModel

						UpperCAmelCase             =						["image", "text"]
						UpperCAmelCase             =						["text"]




						def __init__(       self					:      Any				,    *_a					:      int				,    **_a					:      Dict  )							->     int:


										"""simple docstring"""





										if not is_vision_available():
														raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''  )

										super().__init__(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      "Image"				,    _a					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
										_SCREAMING_SNAKE_CASE			  =task_prompt.replace('''{user_input}'''				,    _a  )
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenizer(
										    _a				,    add_special_tokens=_a				,    return_tensors='''pt'''  ).input_ids
										_SCREAMING_SNAKE_CASE			  =self.pre_processor(_a				,    return_tensors='''pt'''  ).pixel_values

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




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[Any]  )							->     int:


										"""simple docstring"""





										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=_a				,    pad_token_id=self.pre_processor.tokenizer.pad_token_id				,    eos_token_id=self.pre_processor.tokenizer.eos_token_id				,    use_cache=_a				,    num_beams=1				,    bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,    return_dict_in_generate=_a				,    ).sequences




						def 							__UpperCamelCase				(       self					:      Any				,    _a					:      int  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.pre_processor.batch_decode(_a  )[0]
										_SCREAMING_SNAKE_CASE			  =sequence.replace(self.pre_processor.tokenizer.eos_token				,    ''''''  )
										_SCREAMING_SNAKE_CASE			  =sequence.replace(self.pre_processor.tokenizer.pad_token				,    ''''''  )
										_SCREAMING_SNAKE_CASE			  =re.sub(R'''<.*?>'''				,    ''''''				,    _a				,    count=1  ).strip()  # remove first task start token
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenajson(_a  )

										return sequence["answer"]