

import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class 	A_		(	snake_case__    ):
       _UpperCAmelCase   :  Optional[int]										=				["""image_processor""", """tokenizer"""]
       _UpperCAmelCase   :  List[Any]										=				"""BlipImageProcessor"""
       _UpperCAmelCase   :  Optional[Any]										=				"""AutoTokenizer"""
       def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[str]):
        super().__init__(_A       ,_A)

        # add QFormer tokenizer
        __lowerCamelCase    :    List[str] 	=			qformer_tokenizer
       def __call__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = True       ,SCREAMING_SNAKE_CASE__		:      Tuple = False       ,SCREAMING_SNAKE_CASE__		:      List[Any] = None       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = 0       ,SCREAMING_SNAKE_CASE__		:      Optional[int] = None       ,SCREAMING_SNAKE_CASE__		:      Dict = None       ,SCREAMING_SNAKE_CASE__		:      str = False       ,SCREAMING_SNAKE_CASE__		:      List[str] = False       ,SCREAMING_SNAKE_CASE__		:      List[str] = False       ,SCREAMING_SNAKE_CASE__		:      Tuple = False       ,SCREAMING_SNAKE_CASE__		:      str = False       ,SCREAMING_SNAKE_CASE__		:      int = True       ,SCREAMING_SNAKE_CASE__		:      Optional[int] = None       ,**SCREAMING_SNAKE_CASE__		:      str       ,):



        if images is None and text is None:
         raise ValueError('You have to specify at least images or text.')

        __lowerCamelCase    :    Optional[int] 	=			BatchFeature()

        if text is not None:
         __lowerCamelCase    :    Dict 	=			self.tokenizer(
             text=_A       ,add_special_tokens=_A       ,padding=_A       ,truncation=_A       ,max_length=_A       ,stride=_A       ,pad_to_multiple_of=_A       ,return_attention_mask=_A       ,return_overflowing_tokens=_A       ,return_special_tokens_mask=_A       ,return_offsets_mapping=_A       ,return_token_type_ids=_A       ,return_length=_A       ,verbose=_A       ,return_tensors=_A       ,**_A       ,)
         encoding.update(_A)
         __lowerCamelCase    :    Optional[Any] 	=			self.qformer_tokenizer(
             text=_A       ,add_special_tokens=_A       ,padding=_A       ,truncation=_A       ,max_length=_A       ,stride=_A       ,pad_to_multiple_of=_A       ,return_attention_mask=_A       ,return_overflowing_tokens=_A       ,return_special_tokens_mask=_A       ,return_offsets_mapping=_A       ,return_token_type_ids=_A       ,return_length=_A       ,verbose=_A       ,return_tensors=_A       ,**_A       ,)
         __lowerCamelCase    :    Dict 	=			qformer_text_encoding.pop('input_ids')
         __lowerCamelCase    :    Optional[int] 	=			qformer_text_encoding.pop('attention_mask')

        if images is not None:
         __lowerCamelCase    :    int 	=			self.image_processor(_A       ,return_tensors=_A)
         encoding.update(_A)

        return encoding
       def    lowerCAmelCase					(		self		:      Any       ,*SCREAMING_SNAKE_CASE__		:      int       ,**SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
        return self.tokenizer.batch_decode(*_A       ,**_A)
       def    lowerCAmelCase					(		self		:      Union[str, Any]       ,*SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,**SCREAMING_SNAKE_CASE__		:      Tuple):
        return self.tokenizer.decode(*_A       ,**_A)
       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def    lowerCAmelCase					(		self		:      int):
        __lowerCamelCase    :    Dict 	=			self.tokenizer.model_input_names
        __lowerCamelCase    :    Tuple 	=			self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
       def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      Any):



        if os.path.isfile(_A):
         raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file")
        os.makedirs(_A       ,exist_ok=_A)
        __lowerCamelCase    :    int 	=			os.path.join(_A       ,'qformer_tokenizer')
        self.qformer_tokenizer.save_pretrained(_A)
        return super().save_pretrained(_A       ,**_A)


       @classmethod
       def    lowerCAmelCase					(		cls		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,**SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
        __lowerCamelCase    :    Optional[int] 	=			AutoTokenizer.from_pretrained(_A       ,subfolder='qformer_tokenizer')
        __lowerCamelCase    :    Union[str, Any] 	=			cls._get_arguments_from_pretrained(_A       ,**_A)
        args.append(_A)
        return cls(*_A)







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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:

	# A local function to see if a dot lands in the circle.
	def is_in_circle(lowerCamelCase__     ,			lowerCamelCase__      ) -> bool:
		__lowerCamelCase    :    Dict 	=			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
	__lowerCamelCase    :    Optional[int] 	=			mean(
	    int(is_in_circle(uniform(-1.0     ,			1.0      )     ,			uniform(-1.0     ,			1.0      )      )      )
	    for _ in range(lowerCamelCase__      )      )
	# The ratio of the area for circle to square is pi/4.
	__lowerCamelCase    :    Any 	=			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__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0     ,			)  ->					float:
	return mean(
	    function_to_integrate(uniform(lowerCamelCase__     ,			lowerCamelCase__      )      ) for _ in range(lowerCamelCase__      )      ) * (max_value - min_value)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0      )  ->					None:

	def identity_function(lowerCamelCase__      ) -> float:
		return x

	__lowerCamelCase    :    str 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			(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__	(  lowerCamelCase__      )  ->					None:

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

	__lowerCamelCase    :    Any 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			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()




