




import argparse

import torch

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


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

lowercase__  		=		[
    ['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   _snake_case    (       lowercase__	):
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_lowerCamelCase  :    Tuple					=					k.replace(_A							,					_A	)

				if k.startswith('encoder'	):
								_lowerCamelCase  :    Tuple					=					k.replace('.attn'							,					'.self_attn'	)
								_lowerCamelCase  :    int					=					k.replace('norm1'							,					'self_attn_layer_norm'	)
								_lowerCamelCase  :    int					=					k.replace('norm2'							,					'final_layer_norm'	)
				elif k.startswith('decoder'	):
								_lowerCamelCase  :    Dict					=					k.replace('norm1'							,					'self_attn_layer_norm'	)
								_lowerCamelCase  :    str					=					k.replace('norm2'							,					'encoder_attn_layer_norm'	)
								_lowerCamelCase  :    int					=					k.replace('norm3'							,					'final_layer_norm'	)
				return k







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    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:
								_lowerCamelCase  :    str					=					sd.pop(_A	)
								_lowerCamelCase  :    int					=					k.replace('layernorm_embedding'							,					'layer_norm'	)
								assert new_k not in sd
								_lowerCamelCase  :    str					=					v


lowercase__  		=		['START']







@torch.no_grad()
def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Any					=					torch.load(_A							,					map_location='cpu'	)
				_lowerCamelCase  :    Dict					=					model['model']
				_lowerCamelCase  :    str					=					BlenderbotConfig.from_json_file(_A	)
				_lowerCamelCase  :    Union[str, Any]					=					BlenderbotForConditionalGeneration(_A	)
				_lowerCamelCase  :    List[Any]					=					m.model.state_dict().keys()
				_lowerCamelCase  :    Optional[Any]					=					[]
				_lowerCamelCase  :    Any					=					{}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_lowerCamelCase  :    Union[str, Any]					=					rename_state_dict_key(_A	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_lowerCamelCase  :    Optional[Any]					=					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__":
			lowercase__  		=		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__  		=		parser.parse_args()
			convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





"""simple docstring"""






from math import pow, sqrt







def   _snake_case    (       *lowercase__	):
				_lowerCamelCase  :    Dict					=					len(lowercase__	) > 0 and all(value > 0.0 for value in values	)
				return result







def   _snake_case    (       lowercase__							,					lowercase__	):
				return (
				    round(sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__	)
				    else ValueError('Input Error: Molar mass values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(molar_mass / pow(effusion_rate_a / effusion_rate_a							,					2	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(pow(effusion_rate_a / effusion_rate_a							,					2	) / molar_mass							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)