


"""simple docstring"""



import argparse

import torch

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


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

__snake_case         =					[
    ["""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		(		lowercase						:						List[str]				)			->		Optional[Any]:


    """simple docstring"""





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

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

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







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]				)			->		int:


    """simple docstring"""





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


__snake_case         =					["""START"""]







@torch.no_grad()
def 						__lowerCAmelCase		(		lowercase						:						Tuple		,							lowercase						:						Tuple		,							lowercase						:						List[str]				)			->		int:


    """simple docstring"""





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

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


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









"""simple docstring"""



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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''markuplm'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=256		,			UpperCamelCase__=1024		,			UpperCamelCase__=216		,			UpperCamelCase__=1001		,			UpperCamelCase__=32		,			UpperCamelCase__=50		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Any:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   List[str]         =					vocab_size
       snake_case					:   Optional[int]         =					hidden_size
       snake_case					:   List[str]         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Tuple         =					intermediate_size
       snake_case					:   int         =					hidden_dropout_prob
       snake_case					:   Tuple         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[Any]         =					type_vocab_size
       snake_case					:   Any         =					initializer_range
       snake_case					:   Union[str, Any]         =					layer_norm_eps
       snake_case					:   List[str]         =					position_embedding_type
       snake_case					:   Optional[Any]         =					use_cache
       snake_case					:   str         =					classifier_dropout
       # additional properties
       snake_case					:   Optional[int]         =					max_depth
       snake_case					:   Optional[int]         =					max_xpath_tag_unit_embeddings
       snake_case					:   str         =					max_xpath_subs_unit_embeddings
       snake_case					:   int         =					tag_pad_id
       snake_case					:   Dict         =					subs_pad_id
       snake_case					:   List[str]         =					xpath_unit_hidden_size





