

import argparse

import torch

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


logging.set_verbosity_info()
__A			:					int						=			logging.get_logger(__name__)

__A			:					str						=			[
    ['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__			:  Optional[Any]       ):
    if k == "embeddings.weight":
        return "shared.weight"

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

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





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_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:
        SCREAMING_SNAKE_CASE										=   sd.pop(A__       )
        SCREAMING_SNAKE_CASE										=   k.replace("layernorm_embedding"			,  "layer_norm"       )
        assert new_k not in sd
        SCREAMING_SNAKE_CASE										=   v


__A			:					Tuple						=			['START']





@torch.no_grad()
def 	__a     (      A__			:  List[Any]			,  A__			:  Union[str, Any]			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   torch.load(A__			,  map_location="cpu"       )
    SCREAMING_SNAKE_CASE										=   model["model"]
    SCREAMING_SNAKE_CASE										=   BlenderbotConfig.from_json_file(A__       )
    SCREAMING_SNAKE_CASE										=   BlenderbotForConditionalGeneration(A__       )
    SCREAMING_SNAKE_CASE										=   m.model.state_dict().keys()
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        SCREAMING_SNAKE_CASE										=   rename_state_dict_key(A__       )
        if new_k not in valid_keys:
            failures.append([k, new_k]       )
        else:
            SCREAMING_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			:					Optional[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'
    )
    __A			:					Optional[int]						=			parser.parse_args()
    convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


def 	__a     (      A__			:  int = 1000       ):
    SCREAMING_SNAKE_CASE										=   3
    SCREAMING_SNAKE_CASE										=   0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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