
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)



import sys

_A						      =				(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)



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

    snake_case				  =      1
    for digit in s:
        product *= int(A__	)
    return product



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

    snake_case				  =      -sys.maxsize - 1
    snake_case				  =      n[:13]
    snake_case				  =      13
    while cur_index < len(A__	) - 13:
        if int(n[cur_index]	) >= int(substr[0]	):
            snake_case				  =      substr[1:] + n[cur_index]
            cur_index += 1
        else:
            snake_case				  =      max(A__				,	str_eval(A__	)	)
            snake_case				  =      n[cur_index : cur_index + 13]
            cur_index += 13
    return largest_product


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

