

import argparse

import torch

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


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

a__												=						[
    ['''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   __UpperCAmelCase					(      __a :     Any      )     ->	Union[str, Any]:

					"""simple docstring"""

					if k == "embeddings.weight":
										return "shared.weight"

					for parlai_name, hf_name in PATTERNS:
										_a      :       List[Any]   =						k.replace(__a						,__a      )

					if k.startswith('''encoder'''      ):
										_a      :       Dict   =						k.replace('''.attn'''						,'''.self_attn'''      )
										_a      :       Optional[int]   =						k.replace('''norm1'''						,'''self_attn_layer_norm'''      )
										_a      :       Union[str, Any]   =						k.replace('''norm2'''						,'''final_layer_norm'''      )
					elif k.startswith('''decoder'''      ):
										_a      :       int   =						k.replace('''norm1'''						,'''self_attn_layer_norm'''      )
										_a      :       Any   =						k.replace('''norm2'''						,'''encoder_attn_layer_norm'''      )
										_a      :       List[Any]   =						k.replace('''norm3'''						,'''final_layer_norm'''      )
					return k



def   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						[
					    '''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      :       List[str]   =						sd.pop(__a      )
										_a      :       Any   =						k.replace('''layernorm_embedding'''						,'''layer_norm'''      )
										assert new_k not in sd
										_a      :       str   =						v


a__												=						['''START''']



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     str						,__a :     Union[str, Any]      )     ->	str:

					"""simple docstring"""

					_a      :       Optional[Any]   =						torch.load(__a						,map_location='''cpu'''      )
					_a      :       Optional[int]   =						model['''model''']
					_a      :       Tuple   =						BlenderbotConfig.from_json_file(__a      )
					_a      :       Tuple   =						BlenderbotForConditionalGeneration(__a      )
					_a      :       Union[str, Any]   =						m.model.state_dict().keys()
					_a      :       str   =						[]
					_a      :       Optional[int]   =						{}
					for k, v in sd.items():
										if k in IGNORE_KEYS:
															continue

										_a      :       str   =						rename_state_dict_key(__a      )
										if new_k not in valid_keys:
															failures.append([k, new_k]      )
										else:
															_a      :       int   =						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__":
			a__												=						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'''
			)
			a__												=						parser.parse_args()
			convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)








"""simple docstring"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str		)							->					None:
									snake_case   :			Optional[int]						    = self._trie
									for char in text:
															if char not in trie:
																					snake_case   :			str						    = {}
															snake_case   :			Optional[Any]						    = trie[char]
									snake_case   :			str						    = True



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



