import argparse

import torch

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


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

__UpperCamelCase		:       str									=     [
    ['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	(    _lowercase						):
						if k == "embeddings.weight":
												return "shared.weight"

						for parlai_name, hf_name in PATTERNS:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							k.replace(_lowercase		,  _lowercase						)

						if k.startswith('''encoder'''						):
												SCREAMING_SNAKE_CASE							:    Dict							=							k.replace('''.attn'''		,  '''.self_attn'''						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							k.replace('''norm1'''		,  '''self_attn_layer_norm'''						)
												SCREAMING_SNAKE_CASE							:    str							=							k.replace('''norm2'''		,  '''final_layer_norm'''						)
						elif k.startswith('''decoder'''						):
												SCREAMING_SNAKE_CASE							:    List[str]							=							k.replace('''norm1'''		,  '''self_attn_layer_norm'''						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							k.replace('''norm2'''		,  '''encoder_attn_layer_norm'''						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							k.replace('''norm3'''		,  '''final_layer_norm'''						)
						return k
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[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							:    Union[str, Any]							=							sd.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							k.replace('''layernorm_embedding'''		,  '''layer_norm'''						)
												assert new_k not in sd
												SCREAMING_SNAKE_CASE							:    Tuple							=							v


__UpperCamelCase		:       Any									=     ['START']
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							torch.load(_lowercase		,  map_location='''cpu'''						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							model['''model''']
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							BlenderbotConfig.from_json_file(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							BlenderbotForConditionalGeneration(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							m.model.state_dict().keys()
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							{}
						for k, v in sd.items():
												if k in IGNORE_KEYS:
																		continue

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


if __name__ == "__main__":
					__UpperCamelCase		:       Optional[int]									=     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		:       Union[str, Any]									=     parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float(moles / volume						) * nfactor						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (volume)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (pressure)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((pressure * volume) / (0.0821 * moles)						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





