





"""simple docstring"""




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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[Any]         =["""image_processor""", """tokenizer"""]
							lowerCamelCase     :      int         ="""BlipImageProcessor"""
							lowerCamelCase     :      List[Any]         =("""BertTokenizer""", """BertTokenizerFast""")






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	int			=       False
								super().__init__(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	Tuple			=       self.image_processor






							def __call__(     self   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = 0   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				BatchEncoding:
								if images is None and text is None:
									raise ValueError("You have to specify either images or text."   )

								# Get only text
								if images is None:
									a   :	int			=       self.tokenizer
									a   :	int			=       self.tokenizer(
									    text=lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      stride=lowerCAmelCase__   ,      pad_to_multiple_of=lowerCAmelCase__   ,      return_attention_mask=lowerCAmelCase__   ,      return_overflowing_tokens=lowerCAmelCase__   ,      return_special_tokens_mask=lowerCAmelCase__   ,      return_offsets_mapping=lowerCAmelCase__   ,      return_token_type_ids=lowerCAmelCase__   ,      return_length=lowerCAmelCase__   ,      verbose=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )
									return text_encoding

								# add pixel_values
								a   :	Union[str, Any]			=       self.image_processor(lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   )

								if text is not None:
									a   :	List[Any]			=       self.tokenizer(
									    text=lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      stride=lowerCAmelCase__   ,      pad_to_multiple_of=lowerCAmelCase__   ,      return_attention_mask=lowerCAmelCase__   ,      return_overflowing_tokens=lowerCAmelCase__   ,      return_special_tokens_mask=lowerCAmelCase__   ,      return_offsets_mapping=lowerCAmelCase__   ,      return_token_type_ids=lowerCAmelCase__   ,      return_length=lowerCAmelCase__   ,      verbose=lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )
								else:
									a   :	List[str]			=       None

								if text_encoding is not None:
									encoding_image_processor.update(lowerCAmelCase__   )

								return encoding_image_processor






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				str:
								return self.tokenizer.batch_decode(*lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				List[str]:
								return self.tokenizer.decode(*lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@property
							def 					__a     (     self   )			->				Any:
								a   :	Dict			=       self.tokenizer.model_input_names
								a   :	Union[str, Any]			=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


