





'''simple docstring'''






def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				list[int]:



			"""simple docstring"""


			__snake_case					:      Tuple							      =     int(_lowerCamelCase )

			# Initialize Result
			__snake_case					:      Union[str, Any]							      =     []

			# Traverse through all denomination
			for denomination in reversed(_lowerCamelCase ):
						# Find denominations
						while int(_lowerCamelCase ) >= int(_lowerCamelCase ):
									total_value -= int(_lowerCamelCase )
									answer.append(_lowerCamelCase )  # Append the "answers" array

			return answer


# Driver Code
if __name__ == "__main__":
	__UpperCamelCase					    =	[]
	__UpperCamelCase					    =	"0"

	if (
	    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
	    == "y"
	):
		__UpperCamelCase					    =	int(input("Enter the number of denominations you want to add: ").strip())

		for i in range(0, n):
			denominations.append(int(input(f"""Denomination {i}: """).strip()))
		__UpperCamelCase					    =	input("Enter the change you want to make in Indian Currency: ").strip()
	else:
		# All denominations of Indian Currency if user does not enter
		__UpperCamelCase					    =	[1, 2, 5, 10, 20, 50, 100, 500, 2000]
		__UpperCamelCase					    =	input("Enter the change you want to make: ").strip()

	if int(value) == 0 or int(value) < 0:
		print("The total value cannot be zero or negative.")

	else:
		print(f"""Following is minimal change for {value}: """)
		__UpperCamelCase					    =	find_minimum_change(denominations, value)
		# Print result
		for i in range(len(answer)):
			print(answer[i], end=" ")









'''simple docstring'''






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

			if k.startswith("""encoder""" ):
						__snake_case					:      Optional[int]							      =     k.replace(""".attn"""    ,	""".self_attn""" )
						__snake_case					:      Tuple							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      List[str]							      =     k.replace("""norm2"""    ,	"""final_layer_norm""" )
			elif k.startswith("""decoder""" ):
						__snake_case					:      List[Any]							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      str							      =     k.replace("""norm2"""    ,	"""encoder_attn_layer_norm""" )
						__snake_case					:      Optional[int]							      =     k.replace("""norm3"""    ,	"""final_layer_norm""" )
			return k







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [
			    """model.encoder.layernorm_embedding.weight""",
			    """model.encoder.layernorm_embedding.bias""",
			    """model.decoder.layernorm_embedding.weight""",
			    """model.decoder.layernorm_embedding.bias""",
			]
			for k in keys:
						__snake_case					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__snake_case					:      Optional[int]							      =     rename_state_dict_key(_lowerCamelCase )
						if new_k not in valid_keys:
									failures.append([k, new_k] )
						else:
									__snake_case					:      str							      =     v
			if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
						rename_layernorm_keys(_lowerCamelCase )
			m.model.load_state_dict(_lowerCamelCase    ,	strict=_lowerCamelCase )
			m.half()
			m.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
	parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
	parser.add_argument(
	    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


