





import argparse

import torch

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


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

a__:      Dict									=		[
    ['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__(       UpperCamelCase__			:       str  )->Union[str, Any]:
       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 	UpperCamelCase__(       UpperCamelCase__			:       List[str]  )->Tuple:
       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


a__:      Dict									=		['START']


@torch.no_grad()
def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       int  )->int:
       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__":
       a__:      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'
       )
       a__:      List[Any]									=		parser.parse_args()
       convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)












a__:      Dict									=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

a__:      str									=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[]

       for neighbour in graph[vert]:
              if not visited[neighbour]:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       order.append(UpperCamelCase__  )

       return order


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[vert]

       for neighbour in reversed_graph[vert]:
              if not visited[neighbour]:
                     component += find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       return component


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]]  )->list[list[int]]:
       A__											=						len(UpperCamelCase__  ) * [False]
       A__											=						{vert: [] for vert in range(len(UpperCamelCase__  )  )}

       for vert, neighbours in graph.items():
              for neighbour in neighbours:
                     reversed_graph[neighbour].append(UpperCamelCase__  )

       A__											=						[]
       for i, was_visited in enumerate(UpperCamelCase__  ):
              if not was_visited:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						[]
       A__											=						len(UpperCamelCase__  ) * [False]

       for i in range(len(UpperCamelCase__  )  ):
              A__											=						order[len(UpperCamelCase__  ) - i - 1]
              if not visited[vert]:
                     A__											=						find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     components_list.append(UpperCamelCase__  )

       return components_list





