

'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__											= logging.get_logger(__name__)

lowerCAmelCase__											= {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Union[str, Any]=   "mgp-str"


							def __init__(			self    ,			_snake_case=[32, 128]    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=27    ,			_snake_case=38    ,			_snake_case=5_0257    ,			_snake_case=3_0522    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=12    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=1e-5    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=False    ,			_snake_case=0.02    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Any       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Union[str, Any]       	=					max_token_length
								UpperCAmelCase_		:      Tuple       	=					num_character_labels
								UpperCAmelCase_		:      Any       	=					num_bpe_labels
								UpperCAmelCase_		:      Dict       	=					num_wordpiece_labels
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_size
								UpperCAmelCase_		:      Optional[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Any       	=					num_attention_heads
								UpperCAmelCase_		:      Tuple       	=					mlp_ratio
								UpperCAmelCase_		:      List[Any]       	=					distilled
								UpperCAmelCase_		:      Union[str, Any]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[Any]       	=					drop_rate
								UpperCAmelCase_		:      Optional[int]       	=					qkv_bias
								UpperCAmelCase_		:      Tuple       	=					attn_drop_rate
								UpperCAmelCase_		:      Union[str, Any]       	=					drop_path_rate
								UpperCAmelCase_		:      Dict       	=					output_aa_attentions
								UpperCAmelCase_		:      Tuple       	=					initializer_range









'''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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					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)






