

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_							:     Union[str, Any]     =	{
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     Tuple     =	["ImageGPTFeatureExtractor"]
						A_							:     str     =	["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     List[str]     =	[
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						A_							:     str     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if start == end:
						return start if lst[start] > item else start + 1
			if start > end:
						return start

			SCREAMING_SNAKE_CASE__									= (start + end) // 2
			if lst[mid] < item:
						return binary_search(snake_case__						,			snake_case__						,			mid + 1						,			snake_case__      )
			elif lst[mid] > item:
						return binary_search(snake_case__						,			snake_case__						,			snake_case__						,			mid - 1      )
			else:
						return mid

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )

			for index in range(1						,			snake_case__      ):
						SCREAMING_SNAKE_CASE__									= lst[index]
						SCREAMING_SNAKE_CASE__									= binary_search(snake_case__						,			snake_case__						,			0						,			index - 1      )
						SCREAMING_SNAKE_CASE__									= lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

			return lst

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if not left:
						return right

			if not right:
						return left

			if left[0] < right[0]:
						return [left[0], *merge(left[1:]						,			snake_case__      )]

			return [right[0], *merge(snake_case__						,			right[1:]      )]

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= [], []
			SCREAMING_SNAKE_CASE__									= [lst[0]]
			SCREAMING_SNAKE_CASE__									= []
			SCREAMING_SNAKE_CASE__									= 1
			while i < length:
						if lst[i] < lst[i - 1]:
									runs.append(snake_case__      )
									SCREAMING_SNAKE_CASE__									= [lst[i]]
						else:
									new_run.append(lst[i]      )
						i += 1
			runs.append(snake_case__      )

			for run in runs:
						sorted_runs.append(insertion_sort(snake_case__      )      )
			for run in sorted_runs:
						SCREAMING_SNAKE_CASE__									= merge(snake_case__						,			snake_case__      )

			return sorted_array

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
			SCREAMING_SNAKE_CASE__									= tim_sort(snake_case__      )
			print(snake_case__      )


if __name__ == "__main__":
						main()


