
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 		lowercase_    (     A__	)							->							str:
    """simple docstring"""

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

    for parlai_name, hf_name in PATTERNS:
        snake_case				  =      k.replace(A__				,	A__	)

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



def 		lowercase_    (     A__	)							->							Optional[int]:
    """simple docstring"""

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


_A						      =				["START"]



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__	)							->							Union[str, Any]:
    """simple docstring"""

    snake_case				  =      torch.load(A__				,	map_location="cpu"	)
    snake_case				  =      model["model"]
    snake_case				  =      BlenderbotConfig.from_json_file(A__	)
    snake_case				  =      BlenderbotForConditionalGeneration(A__	)
    snake_case				  =      m.model.state_dict().keys()
    snake_case				  =      []
    snake_case				  =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        snake_case				  =      rename_state_dict_key(A__	)
        if new_k not in valid_keys:
            failures.append([k, new_k]	)
        else:
            snake_case				  =      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)



def 		lowercase_    (     A__ = 1000	)							->							int:
    """simple docstring"""

    snake_case				  =      -1
    snake_case				  =      0
    for a in range(1				,	n // 3	):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        snake_case				  =      (n * n - 2 * a * n) // (2 * n - 2 * a)
        snake_case				  =      n - a - b
        if c * c == (a * a + b * b):
            snake_case				  =      a * b * c
            if candidate >= product:
                snake_case				  =      candidate
    return product


if __name__ == "__main__":
   print(f"{solution() = }")

