


'''simple docstring'''
__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__SCREAMING_SNAKE_CASE					:				Tuple									=		{value: key for key, value in encode_dict.items()}







def       a_				(	UpperCamelCase_ ):
							A_			    =		""
							for letter in word.lower():
														if letter.isalpha() or letter == " ":
																					encoded += encode_dict[letter]
														else:
																					raise Exception("encode() accepts only letters of the alphabet and spaces" )
							return encoded







def       a_				(	UpperCamelCase_ ):
							if set(UpperCamelCase_ ) - {"A", "B", " "} != set():
														raise Exception("decode() accepts only 'A', 'B' and spaces" )
							A_			    =		""
							for word in coded.split():
														while len(UpperCamelCase_ ) != 0:
																					decoded += decode_dict[word[:5]]
																					A_			    =		word[5:]
														decoded += " "
							return decoded.strip()


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

							testmod()









'''simple docstring'''
import argparse

import torch

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


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE					:				List[Any]									=		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_				(	UpperCamelCase_ ):
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														A_			    =		k.replace(UpperCamelCase_						,  UpperCamelCase_ )

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







def       a_				(	UpperCamelCase_ ):
							A_			    =		[
							    "model.encoder.layernorm_embedding.weight",
							    "model.encoder.layernorm_embedding.bias",
							    "model.decoder.layernorm_embedding.weight",
							    "model.decoder.layernorm_embedding.bias",
							]
							for k in keys:
														A_			    =		sd.pop(UpperCamelCase_ )
														A_			    =		k.replace("layernorm_embedding"						,  "layer_norm" )
														assert new_k not in sd
														A_			    =		v


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		['''START''']







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCamelCase_						,  map_location="cpu" )
							A_			    =		model["model"]
							A_			    =		BlenderbotConfig.from_json_file(UpperCamelCase_ )
							A_			    =		BlenderbotForConditionalGeneration(UpperCamelCase_ )
							A_			    =		m.model.state_dict().keys()
							A_			    =		[]
							A_			    =		{}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

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


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





