



"""simple docstring"""






import math






def      snake_case__   (		__lowerCamelCase				:    list							,		__lowerCamelCase				:    int = 0							,		__lowerCamelCase				:    int = 0	):
			"""simple docstring"""
			lowerCamelCase__		:		str			=end or len(__lowerCamelCase	)
			for i in range(__lowerCamelCase							,		__lowerCamelCase	):
						lowerCamelCase__		:		List[str]			=i
						lowerCamelCase__		:		Optional[int]			=array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									lowerCamelCase__		:		List[str]			=array[temp_index - 1]
									temp_index -= 1
						lowerCamelCase__		:		List[str]			=temp_index_value
			return array






def      snake_case__   (		__lowerCamelCase				:    list							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):  # Max Heap
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=index
			lowerCamelCase__		:		str			=2 * index + 1  # Left Node
			lowerCamelCase__		:		Union[str, Any]			=2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						lowerCamelCase__		:		Tuple			=left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						lowerCamelCase__		:		str			=right_index

			if largest != index:
						lowerCamelCase__	,   lowerCamelCase__		:		Optional[Any]			=array[largest], array[index]
						heapify(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    list	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=len(__lowerCamelCase	)

			for i in range(n // 2							,		-1							,		-1	):
						heapify(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			for i in range(n - 1							,		0							,		-1	):
						lowerCamelCase__	,   lowerCamelCase__		:		Optional[Any]			=array[0], array[i]
						heapify(__lowerCamelCase							,		0							,		__lowerCamelCase	)

			return array






def      snake_case__   (		__lowerCamelCase				:    list							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			if (array[first_index] > array[middle_index]) != (
			    array[first_index] > array[last_index]
			):
						return array[first_index]
			elif (array[middle_index] > array[first_index]) != (
			    array[middle_index] > array[last_index]
			):
						return array[middle_index]
			else:
						return array[last_index]






def      snake_case__   (		__lowerCamelCase				:    list							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=low
			lowerCamelCase__		:		Union[str, Any]			=high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						lowerCamelCase__	,   lowerCamelCase__		:		str			=array[j], array[i]
						i += 1






def      snake_case__   (		__lowerCamelCase				:    list	):
			"""simple docstring"""
			if len(__lowerCamelCase	) == 0:
						return array
			lowerCamelCase__		:		str			=2 * math.ceil(math.loga(len(__lowerCamelCase	)	)	)
			lowerCamelCase__		:		List[Any]			=16
			return intro_sort(__lowerCamelCase							,		0							,		len(__lowerCamelCase	)							,		__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    list							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(__lowerCamelCase	)
						max_depth -= 1
						lowerCamelCase__		:		Any			=median_of_a(__lowerCamelCase							,		__lowerCamelCase							,		start + ((end - start) // 2) + 1							,		end - 1	)
						lowerCamelCase__		:		Optional[Any]			=partition(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
						intro_sort(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
						lowerCamelCase__		:		Optional[Any]			=p
			return insertion_sort(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_lowercase : Union[str, Any]      						=						input("Enter numbers separated by a comma : ").strip()
		_lowercase : Dict      						=						[float(item) for item in user_input.split(",")]
		print(sort(unsorted))









"""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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					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:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=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
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, 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:
											lowerCamelCase__		:		Optional[Any]			=None

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

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




