





'''simple docstring'''






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


__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class  _A (					__lowercase		):
		lowercase__:   Optional[Any]				     =   '''visual_bert'''

		def __init__(   self					:   Union[str, Any]  ,      __magic_name__					:   Any=3_05_22  ,      __magic_name__					:   Union[str, Any]=7_68  ,      __magic_name__					:   Any=5_12  ,      __magic_name__					:   Dict=12  ,      __magic_name__					:   Optional[Any]=12  ,      __magic_name__					:   Optional[Any]=30_72  ,      __magic_name__					:   Tuple="gelu"  ,      __magic_name__					:   Any=0.1  ,      __magic_name__					:   Optional[Any]=0.1  ,      __magic_name__					:   List[str]=5_12  ,      __magic_name__					:   int=2  ,      __magic_name__					:   str=0.02  ,      __magic_name__					:   Dict=1E-12  ,      __magic_name__					:   Any=False  ,      __magic_name__					:   List[str]=True  ,      __magic_name__					:   Union[str, Any]=1  ,      __magic_name__					:   Dict=0  ,      __magic_name__					:   Union[str, Any]=2  ,      **__magic_name__					:   Tuple  ,      )					->    str:


					"""simple docstring"""

					super().__init__(pad_token_id=__magic_name__  ,      bos_token_id=__magic_name__  ,      eos_token_id=__magic_name__  ,      **__magic_name__			)

					__snake_case					:      Dict							      =     vocab_size
					__snake_case					:      Tuple							      =     max_position_embeddings
					__snake_case					:      int							      =     hidden_size
					__snake_case					:      Dict							      =     visual_embedding_dim
					__snake_case					:      Tuple							      =     num_hidden_layers
					__snake_case					:      str							      =     num_attention_heads
					__snake_case					:      str							      =     intermediate_size
					__snake_case					:      int							      =     hidden_act
					__snake_case					:      Tuple							      =     hidden_dropout_prob
					__snake_case					:      int							      =     attention_probs_dropout_prob
					__snake_case					:      List[str]							      =     initializer_range
					__snake_case					:      Optional[Any]							      =     type_vocab_size
					__snake_case					:      Union[str, Any]							      =     layer_norm_eps
					__snake_case					:      Any							      =     bypass_transformer
					__snake_case					:      Dict							      =     special_visual_initialize









'''simple docstring'''






import argparse

import torch

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


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

__UpperCamelCase					    =	[
    ["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						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


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

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

			if k.startswith("""encoder""" ):
						__snake_case					:      Optional[int]							      =     k.replace(""".attn"""    ,	""".self_attn""" )
						__snake_case					:      Tuple							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      List[str]							      =     k.replace("""norm2"""    ,	"""final_layer_norm""" )
			elif k.startswith("""decoder""" ):
						__snake_case					:      List[Any]							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      str							      =     k.replace("""norm2"""    ,	"""encoder_attn_layer_norm""" )
						__snake_case					:      Optional[int]							      =     k.replace("""norm3"""    ,	"""final_layer_norm""" )
			return k







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [
			    """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					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__snake_case					:      Optional[int]							      =     rename_state_dict_key(_lowerCamelCase )
						if new_k not in valid_keys:
									failures.append([k, new_k] )
						else:
									__snake_case					:      str							      =     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__":
	__UpperCamelCase					    =	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"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


