
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__	)							->							Dict:
    """simple docstring"""

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

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

    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__	)							->							str:
    """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(_lowerCamelCase	)
        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__	)							->							Any:
    """simple docstring"""

    snake_case				  =      torch.load(_lowerCamelCase				,	map_location="cpu"	)
    snake_case				  =      model['model']
    snake_case				  =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    snake_case				  =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    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(_lowerCamelCase	)
        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(_lowerCamelCase	)
    m.model.load_state_dict(_lowerCamelCase				,	strict=_lowerCamelCase	)
    m.half()
    m.save_pretrained(_lowerCamelCase	)


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)



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



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

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

