

import argparse

import torch

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


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

a_						     =    [
    ['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__ (					_a):
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		SCREAMING_SNAKE_CASE   :      Any				     =       k.replace(_a							,			_a)

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

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      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:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       sd.pop(_a)
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       k.replace("layernorm_embedding"							,			"layer_norm")
		assert new_k not in sd
		SCREAMING_SNAKE_CASE   :      Dict				     =       v


a_						     =    ['START']

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

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


if __name__ == "__main__":
			a_						     =    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'
			)
			a_						     =    parser.parse_args()
			convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


from __future__ import annotations

def  lowerCamelCase__ (					_a							,			_a):
	if b == 0:
		return (1, 0)
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      Tuple				     =       extended_euclid(_a							,			a % b)
	SCREAMING_SNAKE_CASE   :      Dict				     =       a // b
	return (y, x - k * y)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      str				     =       extended_euclid(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      str				     =       na * na
	SCREAMING_SNAKE_CASE   :      int				     =       ra * x * na + ra * y * na
	return (n % m + m) % m

def  lowerCamelCase__ (					_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      List[str]				     =       extended_euclid(_a							,			_a)
	if b < 0:
		SCREAMING_SNAKE_CASE   :      int				     =       (b % n + n) % n
	return b

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       invert_modulo(_a							,			_a), invert_modulo(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       na * na
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ra * x * na + ra * y * na
	return (n % m + m) % m


if __name__ == "__main__":
			from doctest import testmod

			testmod(name='chinese_remainder_theorem', verbose=True)
			testmod(name='chinese_remainder_theorem2', verbose=True)
			testmod(name='invert_modulo', verbose=True)
			testmod(name='extended_euclid', verbose=True)