


def 	_lowerCAmelCase					( UpperCamelCase__:  int				,       UpperCamelCase__:  Any    )			-> Optional[Any]:





	"""simple docstring"""







	A             =					0
	A             =					len(UpperCamelCase__    ) - 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

		A             =					left + ((item - sorted_collection[left]) * (right - left)) // (
		    sorted_collection[right] - sorted_collection[left]
		)

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

		A             =					sorted_collection[point]
		if current_item == item:
			return point
		else:
			if point < left:
				A             =					left
				A             =					point
			elif point > right:
				A             =					right
				A             =					point
			else:
				if item < current_item:
					A             =					point - 1
				else:
					A             =					point + 1
	return None





def 	_lowerCAmelCase					( UpperCamelCase__:  List[str]				,       UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  str    )			-> Any:





	"""simple docstring"""







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

	A             =					left + ((item - sorted_collection[left]) * (right - left)) // (
	    sorted_collection[right] - sorted_collection[left]
	)

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

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





def 	_lowerCAmelCase					( UpperCamelCase__:  Any    )			-> int:





	"""simple docstring"""







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


if __name__ == "__main__":
				import sys
				_lowercase							:   Tuple						=						0
				if debug == 1:
								_lowercase							:   Tuple						=						[10, 30, 40, 45, 50, 66, 77, 93]
								try:
												__assert_sorted(collection)
								except ValueError:
												sys.exit("Sequence must be ascending sorted to apply interpolation search")
								_lowercase							:   Any						=						67

				_lowercase							:   str						=						interpolation_search(collection, target)
				if result is not None:
								print(f'''{target} found at positions: {result}''')
				else:
								print("Not found")







import argparse

import torch

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


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["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 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> int:





	"""simple docstring"""







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

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

	if k.startswith("""encoder"""    ):
		A             =					k.replace(""".attn"""				,       """.self_attn"""    )
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """final_layer_norm"""    )
	elif k.startswith("""decoder"""    ):
		A             =					k.replace("""norm1"""				,       """self_attn_layer_norm"""    )
		A             =					k.replace("""norm2"""				,       """encoder_attn_layer_norm"""    )
		A             =					k.replace("""norm3"""				,       """final_layer_norm"""    )
	return k





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

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


if __name__ == "__main__":
				_lowercase							:   str						=						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"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



