






"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_     :				Tuple         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Any         =				[
    ["""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    (__a							)				-> Union[str, Any]:
    """simple docstring"""






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

    for parlai_name, hf_name in PATTERNS:
        SCREAMING_SNAKE_CASE_   :						Any							=	k.replace(__a					,       __a							)

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



def       _A    (__a							)				-> List[Any]:
    """simple docstring"""






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


UpperCAmelCase_     :				str         =				["""START"""]



@torch.no_grad()
def       _A    (__a					,       __a					,       __a							)				-> Optional[Any]:
    """simple docstring"""






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

        SCREAMING_SNAKE_CASE_   :						Tuple							=	rename_state_dict_key(__a							)
        if new_k not in valid_keys:
            failures.append([k, new_k]							)
        else:
            SCREAMING_SNAKE_CASE_   :						str							=	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__":
  UpperCAmelCase_     :				int         =				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)










"""simple docstring"""






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	9, 14  # noqa: F841
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    SCREAMING_SNAKE_CASE_   :						Dict							=	defaultdict(__a							)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]							)
        adjancency[nodea].append([nodea, cost]							)

    SCREAMING_SNAKE_CASE_   :						Any							=	mst(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        SCREAMING_SNAKE_CASE_   :						Dict							=	tuple(answer[:2]							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tuple(edge[::-1]							)
        assert edge in result or reverse in result


