



"""simple docstring"""


import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """tokenizer"""]
			UpperCamelCase__			      =	"""FlavaImageProcessor"""
			UpperCamelCase__			      =	("""BertTokenizer""", """BertTokenizerFast""")



			def __init__(		self     :    Dict				,   __UpperCamelCase     :    Any=None				,   __UpperCamelCase     :    Dict=None				,   **__UpperCamelCase     :    Any   )->str:
							_UpperCAmelCase					=    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,   __UpperCamelCase				,   )
											_UpperCAmelCase					=    kwargs.pop('''feature_extractor'''   )

							_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''   )
							if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''   )

							super().__init__(__UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    self.image_processor



			def __call__(		self     :    List[str]				,   __UpperCamelCase     :    Optional[ImageInput] = None				,   __UpperCamelCase     :    Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Union[bool, str, PaddingStrategy] = False				,   __UpperCamelCase     :    Union[bool, str, TruncationStrategy] = False				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Optional[Union[str, TensorType]] = None				,   **__UpperCamelCase     :    Optional[int]				,   )->Optional[int]:

							if text is None and images is None:
											raise ValueError('''You have to specify either text or images. Both cannot be none.'''   )

							if text is not None:
											_UpperCAmelCase					=    self.tokenizer(
											    text=__UpperCamelCase				,   add_special_tokens=__UpperCamelCase				,   padding=__UpperCamelCase				,   truncation=__UpperCamelCase				,   max_length=__UpperCamelCase				,   stride=__UpperCamelCase				,   pad_to_multiple_of=__UpperCamelCase				,   return_token_type_ids=__UpperCamelCase				,   return_attention_mask=__UpperCamelCase				,   return_overflowing_tokens=__UpperCamelCase				,   return_special_tokens_mask=__UpperCamelCase				,   return_offsets_mapping=__UpperCamelCase				,   return_length=__UpperCamelCase				,   verbose=__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase				,   )
							if images is not None:
											_UpperCAmelCase					=    self.image_processor(
											    __UpperCamelCase				,   return_image_mask=__UpperCamelCase				,   return_codebook_pixels=__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase				,   )

							if text is not None and images is not None:
											encoding.update(__UpperCamelCase   )
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**__UpperCamelCase   )				,   tensor_type=__UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   *__UpperCamelCase     :    Optional[int]				,   **__UpperCamelCase     :    Union[str, Any]   )->Optional[int]:
							return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]				,   *__UpperCamelCase     :    Any				,   **__UpperCamelCase     :    Optional[Any]   )->Any:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			@property
			def  lowercase__						(		self     :    str   )->Any:
							_UpperCAmelCase					=    self.tokenizer.model_input_names
							_UpperCAmelCase					=    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )



			@property
			def  lowercase__						(		self     :    int   )->Optional[Any]:
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor_class



			@property
			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor








"""simple docstring"""


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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def is_in_circle(_SCREAMING_SNAKE_CASE   :						float					,     _SCREAMING_SNAKE_CASE   :						float				) -> bool:
								_UpperCAmelCase					=    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					=    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					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Callable[[float], float]					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0					,     ):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0				):






				'''simple docstring'''



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

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    (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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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

				_UpperCAmelCase					=    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()



