


"""simple docstring"""
import argparse

import torch

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


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Tuple						=      [
    ['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			( _SCREAMING_SNAKE_CASE			)      ->			Any:
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														snake_case_          =						k.replace(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

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







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						[
							    """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_          =						sd.pop(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						k.replace("""layernorm_embedding"""			,	"""layer_norm"""			)
														assert new_k not in sd
														snake_case_          =						v


__SCREAMING_SNAKE_CASE							:      List[Any]						=      ['START']







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_          =						torch.load(_SCREAMING_SNAKE_CASE			,	map_location="""cpu"""			)
							snake_case_          =						model["""model"""]
							snake_case_          =						BlenderbotConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						BlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						m.model.state_dict().keys()
							snake_case_          =						[]
							snake_case_          =						{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

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


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







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			int:
							assert column_title.isupper()
							snake_case_          =						0
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			) - 1
							snake_case_          =						0

							while index >= 0:
														snake_case_          =						(ord(column_title[index]			) - 64) * pow(26			,	_SCREAMING_SNAKE_CASE			)
														answer += value
														power += 1
														index -= 1

							return answer


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

				testmod()



