
"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		[
    ["""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    __lowerCAmelCase    (_UpperCamelCase		):
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		__lowerCAmelCase      :						Optional[Any]						      =				k.replace(_UpperCamelCase	,  _UpperCamelCase		)

	if k.startswith('encoder'		):
		__lowerCAmelCase      :						Optional[Any]						      =				k.replace('.attn'	,  '.self_attn'		)
		__lowerCAmelCase      :						str						      =				k.replace('norm1'	,  'self_attn_layer_norm'		)
		__lowerCAmelCase      :						List[Any]						      =				k.replace('norm2'	,  'final_layer_norm'		)
	elif k.startswith('decoder'		):
		__lowerCAmelCase      :						int						      =				k.replace('norm1'	,  'self_attn_layer_norm'		)
		__lowerCAmelCase      :						List[Any]						      =				k.replace('norm2'	,  'encoder_attn_layer_norm'		)
		__lowerCAmelCase      :						List[Any]						      =				k.replace('norm3'	,  'final_layer_norm'		)
	return k
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Dict						      =				[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		__lowerCAmelCase      :						Union[str, Any]						      =				sd.pop(_UpperCamelCase		)
		__lowerCAmelCase      :						Optional[Any]						      =				k.replace('layernorm_embedding'	,  'layer_norm'		)
		assert new_k not in sd
		__lowerCAmelCase      :						Tuple						      =				v


lowerCamelCase__        =		["""START"""]
@torch.no_grad()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				torch.load(_UpperCamelCase	,  map_location='cpu'		)
	__lowerCAmelCase      :						Optional[Any]						      =				model['model']
	__lowerCAmelCase      :						Optional[int]						      =				BlenderbotConfig.from_json_file(_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				BlenderbotForConditionalGeneration(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				m.model.state_dict().keys()
	__lowerCAmelCase      :						Union[str, Any]						      =				[]
	__lowerCAmelCase      :						Optional[int]						      =				{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		__lowerCAmelCase      :						Optional[Any]						      =				rename_state_dict_key(_UpperCamelCase		)
		if new_k not in valid_keys:
			failures.append([k, new_k]		)
		else:
			__lowerCAmelCase      :						Optional[Any]						      =				v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(_UpperCamelCase		)
	m.model.load_state_dict(_UpperCamelCase	,  strict=_UpperCamelCase		)
	m.half()
	m.save_pretrained(_UpperCamelCase		)


if __name__ == "__main__":
							lowerCamelCase__        =		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"""
							)
							lowerCamelCase__        =		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

"""simple docstring"""







from __future__ import annotations
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				str(_UpperCamelCase		)
	return len(_UpperCamelCase		) == 9 and set(_UpperCamelCase		) == set('123456789'		)
def    __lowerCAmelCase    ():
	for base_num in range(9999	,  4999	,  -1		):
		__lowerCAmelCase      :						Union[str, Any]						      =				10_0002 * base_num
		if is_9_pandigital(_UpperCamelCase		):
			return candidate

	for base_num in range(333	,  99	,  -1		):
		__lowerCAmelCase      :						Dict						      =				100_2003 * base_num
		if is_9_pandigital(_UpperCamelCase		):
			return candidate

	return None


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