


import argparse

import torch

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


logging.set_verbosity_info()
__UpperCamelCase				:  List[str]      					=							logging.get_logger(__name__)

__UpperCamelCase				:  int      					=							[
    ['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:
      UpperCAmelCase_							:      Union[str, Any]						=   k.replace(__lowercase			,       __lowercase       )

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


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

      UpperCAmelCase_							:      str						=   rename_state_dict_key(__lowercase       )
      if new_k not in valid_keys:
         failures.append([k, new_k]       )
      else:
         UpperCAmelCase_							:      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				:  str      					=							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				:  Optional[int]      					=							parser.parse_args()
       convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase				:  str      					=							logging.get_logger(__name__)

__UpperCamelCase				:  Any      					=							{
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     int			= 'falcon'
    A_  :     int			= ['past_key_values']





    def __init__(	self				:   Optional[Any]       ,   __snake_case				:   Tuple=65_024       ,   __snake_case				:   List[str]=4_544       ,   __snake_case				:   Optional[Any]=32       ,   __snake_case				:   Any=71       ,   __snake_case				:   str=1E-5       ,   __snake_case				:   List[str]=0.02       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   Dict=0.0       ,   __snake_case				:   Optional[Any]=0.0       ,   __snake_case				:   Any=None       ,   __snake_case				:   List[Any]=False       ,   __snake_case				:   Dict=False       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   Optional[Any]=False       ,   __snake_case				:   Dict=11       ,   __snake_case				:   List[str]=11       ,   **__snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   vocab_size
       # Backward compatibility with n_embed kwarg
       UpperCAmelCase_							:      Union[str, Any]						=   kwargs.pop('''n_embed'''       ,   __snake_case	)
       UpperCAmelCase_							:      str						=   hidden_size if n_embed is None else n_embed
       UpperCAmelCase_							:      Tuple						=   num_hidden_layers
       UpperCAmelCase_							:      Tuple						=   num_attention_heads
       UpperCAmelCase_							:      Optional[int]						=   layer_norm_epsilon
       UpperCAmelCase_							:      int						=   initializer_range
       UpperCAmelCase_							:      Optional[int]						=   use_cache
       UpperCAmelCase_							:      List[Any]						=   hidden_dropout
       UpperCAmelCase_							:      Any						=   attention_dropout

       UpperCAmelCase_							:      Tuple						=   bos_token_id
       UpperCAmelCase_							:      List[Any]						=   eos_token_id
       UpperCAmelCase_							:      Any						=   num_attention_heads if num_kv_heads is None else num_kv_heads
       UpperCAmelCase_							:      Optional[int]						=   alibi
       UpperCAmelCase_							:      Dict						=   new_decoder_architecture
       UpperCAmelCase_							:      List[Any]						=   multi_query  # Ignored when new_decoder_architecture is True
       UpperCAmelCase_							:      Tuple						=   parallel_attn
       UpperCAmelCase_							:      List[Any]						=   bias

       super().__init__(bos_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   **__snake_case	)





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       return self.hidden_size // self.num_attention_heads





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       return not self.alibi