


import argparse

import torch

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


logging.set_verbosity_info()
_UpperCAmelCase			:  str     =logging.get_logger(__name__)

_UpperCAmelCase			:  List[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 			lowerCAmelCase (		lowerCAmelCase_ )-> Any:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								lowerCAmelCase_ :      List[str]    				=				k.replace(lowerCAmelCase__ ,   lowerCAmelCase__ )

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



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Tuple    				=				[
				    '''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_ :      List[Any]    				=				sd.pop(lowerCAmelCase__ )
								lowerCAmelCase_ :      List[Any]    				=				k.replace('''layernorm_embedding''' ,   '''layer_norm''' )
								assert new_k not in sd
								lowerCAmelCase_ :      Dict    				=				v


_UpperCAmelCase			:  List[str]     =["""START"""]



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

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


if __name__ == "__main__":
						_UpperCAmelCase			:  Dict     =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			:  Any     =parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



from __future__ import annotations



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> tuple:
				if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
								raise ValueError('''You cannot supply more or less than 2 values''' )
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
				elif hole_conc < 0:
								raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
				elif intrinsic_conc < 0:
								raise ValueError(
								    '''Intrinsic concentration cannot be negative in a semiconductor''' )
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    intrinsic_conc**2 / hole_conc,
								)
				elif hole_conc == 0:
								return (
								    "hole_conc",
								    intrinsic_conc**2 / electron_conc,
								)
				elif intrinsic_conc == 0:
								return (
								    "intrinsic_conc",
								    (electron_conc * hole_conc) ** 0.5,
								)
				else:
								return (-1, -1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()