


"""simple docstring"""







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


lowerCAmelCase__ 							=  logging.get_logger(__name__)

lowerCAmelCase__ 							=  {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}


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


       a    :							Any  ="gpt_bigcode"
       a    :							Optional[int]  =["past_key_values"]
       a    :							str  ={
           "hidden_size": "n_embd",
           "max_position_embeddings": "n_positions",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

       def __init__(		self						,	snake_case__=50_257						,	snake_case__=1_024						,	snake_case__=768						,	snake_case__=12						,	snake_case__=12						,	snake_case__=None						,	snake_case__="gelu_pytorch_tanh"						,	snake_case__=0.1						,	snake_case__=0.1						,	snake_case__=0.1						,	snake_case__=1e-5						,	snake_case__=0.02						,	snake_case__=True						,	snake_case__=True						,	snake_case__=50_256						,	snake_case__=50_256						,	snake_case__=True						,	snake_case__=True						,	snake_case__=True						,	**snake_case__						,	):






         """simple docstring"""





         lowerCAmelCase	:	Any								=				vocab_size
         lowerCAmelCase	:	List[str]								=				n_positions
         lowerCAmelCase	:	str								=				n_embd
         lowerCAmelCase	:	Union[str, Any]								=				n_layer
         lowerCAmelCase	:	Optional[int]								=				n_head
         lowerCAmelCase	:	List[Any]								=				n_inner
         lowerCAmelCase	:	List[str]								=				activation_function
         lowerCAmelCase	:	Dict								=				resid_pdrop
         lowerCAmelCase	:	Dict								=				embd_pdrop
         lowerCAmelCase	:	List[str]								=				attn_pdrop
         lowerCAmelCase	:	Optional[Any]								=				layer_norm_epsilon
         lowerCAmelCase	:	List[Any]								=				initializer_range
         lowerCAmelCase	:	Optional[Any]								=				scale_attn_weights
         lowerCAmelCase	:	Any								=				use_cache
         lowerCAmelCase	:	Optional[int]								=				attention_softmax_in_fpaa
         lowerCAmelCase	:	List[Any]								=				scale_attention_softmax_in_fpaa
         lowerCAmelCase	:	Dict								=				multi_query

         lowerCAmelCase	:	Union[str, Any]								=				bos_token_id
         lowerCAmelCase	:	Any								=				eos_token_id

         super().__init__(bos_token_id=a__						,	eos_token_id=a__						,	**a__		)









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)



