


"""simple docstring"""







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


lowerCAmelCase__ 							=  logging.get_logger(__name__)

lowerCAmelCase__ 							=  {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}


class  SCREAMING_SNAKE_CASE__							(						__snake_case							):
       """simple docstring"""


       a    :							Any  ="donut-swin"

       a    :							Any  ={
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }

       def __init__(		self						,	snake_case__=224						,	snake_case__=4						,	snake_case__=3						,	snake_case__=96						,	snake_case__=[2, 2, 6, 2]						,	snake_case__=[3, 6, 12, 24]						,	snake_case__=7						,	snake_case__=4.0						,	snake_case__=True						,	snake_case__=0.0						,	snake_case__=0.0						,	snake_case__=0.1						,	snake_case__="gelu"						,	snake_case__=False						,	snake_case__=0.02						,	snake_case__=1e-5						,	**snake_case__						,	):






         """simple docstring"""





         super().__init__(**a__		)

         lowerCAmelCase	:	int								=				image_size
         lowerCAmelCase	:	Any								=				patch_size
         lowerCAmelCase	:	Any								=				num_channels
         lowerCAmelCase	:	int								=				embed_dim
         lowerCAmelCase	:	Optional[int]								=				depths
         lowerCAmelCase	:	Dict								=				len(a__		)
         lowerCAmelCase	:	List[Any]								=				num_heads
         lowerCAmelCase	:	Dict								=				window_size
         lowerCAmelCase	:	Optional[Any]								=				mlp_ratio
         lowerCAmelCase	:	List[str]								=				qkv_bias
         lowerCAmelCase	:	int								=				hidden_dropout_prob
         lowerCAmelCase	:	int								=				attention_probs_dropout_prob
         lowerCAmelCase	:	Tuple								=				drop_path_rate
         lowerCAmelCase	:	Optional[Any]								=				hidden_act
         lowerCAmelCase	:	Optional[int]								=				use_absolute_embeddings
         lowerCAmelCase	:	str								=				layer_norm_eps
         lowerCAmelCase	:	Union[str, Any]								=				initializer_range
         # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         lowerCAmelCase	:	Any								=				int(embed_dim * 2 ** (len(a__		) - 1)		)









import argparse

import torch

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


logging.set_verbosity_info()
_lowercase							:   List[str]						=						logging.get_logger(__name__)

_lowercase							:   int						=						[
    ["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__:  str    )			-> int:





	"""simple docstring"""







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

	for parlai_name, hf_name in PATTERNS:
		A             =					k.replace(UpperCamelCase__				,       UpperCamelCase__    )

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





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple    )			-> str:





	"""simple docstring"""







	A             =					[
	    """model.encoder.layernorm_embedding.weight""",
	    """model.encoder.layernorm_embedding.bias""",
	    """model.decoder.layernorm_embedding.weight""",
	    """model.decoder.layernorm_embedding.bias""",
	]
	for k in keys:
		A             =					sd.pop(UpperCamelCase__    )
		A             =					k.replace("""layernorm_embedding"""				,       """layer_norm"""    )
		assert new_k not in sd
		A             =					v


_lowercase							:   List[Any]						=						["START"]





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[int]    )			-> int:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					model["""model"""]
	A             =					BlenderbotConfig.from_json_file(UpperCamelCase__    )
	A             =					BlenderbotForConditionalGeneration(UpperCamelCase__    )
	A             =					m.model.state_dict().keys()
	A             =					[]
	A             =					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		A             =					rename_state_dict_key(UpperCamelCase__    )
		if new_k not in valid_keys:
			failures.append([k, new_k]    )
		else:
			A             =					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__":
				_lowercase							:   str						=						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"
				)
				_lowercase							:   List[str]						=						parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



