

'''simple docstring'''


class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      str       	=					set_counts
								UpperCAmelCase_		:      List[Any]       	=					max(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					len(_snake_case)
								UpperCAmelCase_		:      str       	=					[1] * num_sets
								UpperCAmelCase_		:      int       	=					list(range(_snake_case))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				bool:
								UpperCAmelCase_		:      Optional[int]       	=					self.get_parent(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					self.get_parent(_snake_case)

								if src_parent == dst_parent:
									return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
									self.set_counts[dst_parent] += self.set_counts[src_parent]
									UpperCAmelCase_		:      Any       	=					0
									UpperCAmelCase_		:      List[str]       	=					dst_parent
									if self.ranks[dst_parent] == self.ranks[src_parent]:
										self.ranks[dst_parent] += 1
									UpperCAmelCase_		:      Dict       	=					self.set_counts[dst_parent]
								else:
									self.set_counts[src_parent] += self.set_counts[dst_parent]
									UpperCAmelCase_		:      List[Any]       	=					0
									UpperCAmelCase_		:      Optional[Any]       	=					src_parent
									UpperCAmelCase_		:      Tuple       	=					self.set_counts[src_parent]

								UpperCAmelCase_		:      Any       	=					max(self.max_set    ,			_snake_case)
								return True





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								if self.parents[disj_set] == disj_set:
									return disj_set
								UpperCAmelCase_		:      List[str]       	=					self.get_parent(self.parents[disj_set])
								return self.parents[disj_set]









'''simple docstring'''


import argparse

import torch

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


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

lowerCAmelCase__										= [
    ["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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					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__":
						lowerCAmelCase__										= 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"
						)
						lowerCAmelCase__										= parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






