"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tempfile.mkdtemp()

										snake_case								=			[
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case								=			os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']    )
										with open(self.vocab_file		,    'w'		,    encoding='utf-8'    ) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

										snake_case								=			{
										    'do_resize': True,
										    'size': 20,
										    'do_center_crop': True,
										    'crop_size': 18,
										    'do_normalize': True,
										    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
										    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
										}
										snake_case								=			os.path.join(self.tmpdirname		,    lowerCAmelCase    )
										with open(self.image_processor_file		,    'w'		,    encoding='utf-8'    ) as fp:
																json.dump(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return BertTokenizer.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return BertTokenizerFast.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return EfficientNetImageProcessor.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										shutil.rmtree(self.tmpdirname    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[np.random.randint(2_55		,    size=(3, 30, 4_00)		,    dtype=np.uinta    )]
										snake_case								=			[Image.fromarray(np.moveaxis(lowerCAmelCase		,    0		,    -1    )    ) for x in image_inputs]
										return image_inputs

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_tokenizer()
										snake_case								=			self.get_rust_tokenizer()
										snake_case								=			self.get_image_processor()

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )
										processor_slow.save_pretrained(self.tmpdirname    )
										snake_case								=			AlignProcessor.from_pretrained(self.tmpdirname		,    use_fast=lowerCAmelCase    )

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )
										processor_fast.save_pretrained(self.tmpdirname    )
										snake_case								=			AlignProcessor.from_pretrained(self.tmpdirname    )

										self.assertEqual(processor_slow.tokenizer.get_vocab()		,    tokenizer_slow.get_vocab()    )
										self.assertEqual(processor_fast.tokenizer.get_vocab()		,    tokenizer_fast.get_vocab()    )
										self.assertEqual(tokenizer_slow.get_vocab()		,    tokenizer_fast.get_vocab()    )
										self.assertIsInstance(processor_slow.tokenizer		,    lowerCAmelCase    )
										self.assertIsInstance(processor_fast.tokenizer		,    lowerCAmelCase    )

										self.assertEqual(processor_slow.image_processor.to_json_string()		,    image_processor.to_json_string()    )
										self.assertEqual(processor_fast.image_processor.to_json_string()		,    image_processor.to_json_string()    )
										self.assertIsInstance(processor_slow.image_processor		,    lowerCAmelCase    )
										self.assertIsInstance(processor_fast.image_processor		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			AlignProcessor(tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()    )
										processor.save_pretrained(self.tmpdirname    )

										snake_case								=			self.get_tokenizer(bos_token='(BOS)'		,    eos_token='(EOS)'    )
										snake_case								=			self.get_image_processor(do_normalize=lowerCAmelCase		,    padding_value=1.0    )

										snake_case								=			AlignProcessor.from_pretrained(
										    self.tmpdirname		,    bos_token='(BOS)'		,    eos_token='(EOS)'		,    do_normalize=lowerCAmelCase		,    padding_value=1.0    )

										self.assertEqual(processor.tokenizer.get_vocab()		,    tokenizer_add_kwargs.get_vocab()    )
										self.assertIsInstance(processor.tokenizer		,    lowerCAmelCase    )

										self.assertEqual(processor.image_processor.to_json_string()		,    image_processor_add_kwargs.to_json_string()    )
										self.assertIsInstance(processor.image_processor		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			self.prepare_image_inputs()

										snake_case								=			image_processor(lowerCAmelCase		,    return_tensors='np'    )
										snake_case								=			processor(images=lowerCAmelCase		,    return_tensors='np'    )

										for key in input_image_proc.keys():
																self.assertAlmostEqual(input_image_proc[key].sum()		,    input_processor[key].sum()		,    delta=1E-2    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			'lower newer'

										snake_case								=			processor(text=lowerCAmelCase    )

										snake_case								=			tokenizer(lowerCAmelCase		,    padding='max_length'		,    max_length=64    )

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]		,    encoded_processor[key]    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			'lower newer'
										snake_case								=			self.prepare_image_inputs()

										snake_case								=			processor(text=lowerCAmelCase		,    images=lowerCAmelCase    )

										self.assertListEqual(list(inputs.keys()    )		,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']    )

										# test if it raises when no input is passed
										with pytest.raises(lowerCAmelCase    ):
																processor()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										snake_case								=			processor.batch_decode(lowerCAmelCase    )
										snake_case								=			tokenizer.batch_decode(lowerCAmelCase    )

										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			AlignProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			'lower newer'
										snake_case								=			self.prepare_image_inputs()

										snake_case								=			processor(text=lowerCAmelCase		,    images=lowerCAmelCase    )

										self.assertListEqual(list(inputs.keys()    )		,    processor.model_input_names    )



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





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				Dict:





						"""simple docstring"""






						def is_in_circle(_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					) -> bool:
												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
						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.
						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    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				Callable[[float], float] ,			_UpperCamelCase       :				float = 0.0 ,			_UpperCamelCase       :				float = 1.0 ,			)					->				float:





						"""simple docstring"""






						return mean(
						    function_to_integrate(uniform(_UpperCamelCase ,			_UpperCamelCase					)					) for _ in range(_UpperCamelCase					)					) * (max_value - min_value)





def    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				float = 0.0 ,			_UpperCamelCase       :				float = 1.0					)					->				None:





						"""simple docstring"""






						def identity_function(_UpperCamelCase       :				float					) -> float:
												return x

						snake_case								=			area_under_curve_estimator(
						    _UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)
						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    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				None:





						"""simple docstring"""






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

						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()


