



def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					0
				lowerCAmelCase__					:    Tuple												=					len(_A) - 1

				while left <= right:
								# avoid divided by 0 during interpolation
								if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																return left
												else:
																return None

								lowerCAmelCase__					:    Tuple												=					left + ((item - sorted_collection[left]) * (right - left)) // (
								    sorted_collection[right] - sorted_collection[left]
								)

								# out of range check
								if point < 0 or point >= len(_A):
												return None

								lowerCAmelCase__					:    List[str]												=					sorted_collection[point]
								if current_item == item:
												return point
								else:
												if point < left:
																lowerCAmelCase__					:    Union[str, Any]												=					left
																lowerCAmelCase__					:    Any												=					point
												elif point > right:
																lowerCAmelCase__					:    Optional[Any]												=					right
																lowerCAmelCase__					:    List[Any]												=					point
												else:
																if item < current_item:
																				lowerCAmelCase__					:    Any												=					point - 1
																else:
																				lowerCAmelCase__					:    Tuple												=					point + 1
				return None






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		str	,lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				if sorted_collection[left] == sorted_collection[right]:
								if sorted_collection[left] == item:
												return left
								else:
												return None

				lowerCAmelCase__					:    Tuple												=					left + ((item - sorted_collection[left]) * (right - left)) // (
				    sorted_collection[right] - sorted_collection[left]
				)

				# out of range check
				if point < 0 or point >= len(_A):
								return None

				if sorted_collection[point] == item:
								return point
				elif point < left:
								return interpolation_search_by_recursion(_A	,_A	,_A	,_A)
				elif point > right:
								return interpolation_search_by_recursion(_A	,_A	,_A	,_A)
				else:
								if sorted_collection[point] > item:
												return interpolation_search_by_recursion(
												    _A	,_A	,_A	,point - 1)
								else:
												return interpolation_search_by_recursion(
												    _A	,_A	,point + 1	,_A)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				if collection != sorted(_A):
								raise ValueError('''Collection must be ascending sorted''')
				return True


if __name__ == "__main__":
							import sys
							__snake_case    :		Any  =0
							if debug == 1:
														__snake_case    :		Dict  =[1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
														try:
																					__assert_sorted(collection)
														except ValueError:
																					sys.exit('Sequence must be ascending sorted to apply interpolation search')
														__snake_case    :		List[Any]  =6_7

							__snake_case    :		List[Any]  =interpolation_search(collection, target)
							if result is not None:
														print(f"""{target} found at positions: {result}""")
							else:
														print('Not found')






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

