



'''simple docstring'''




import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase							:Tuple       = logging.get_logger(__name__)

lowerCamelCase							:Any       = [
    ['''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__   ):
			'''simple docstring'''





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

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

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



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						str     =					[
			    """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_     :						Optional[int]     =					sd.pop(lowerCamelCase__   )
						A_     :						str     =					k.replace("""layernorm_embedding"""						,				"""layer_norm"""   )
						assert new_k not in sd
						A_     :						List[str]     =					v


lowerCamelCase							:List[Any]       = ['''START''']



@torch.no_grad()
def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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

						A_     :						Dict     =					rename_state_dict_key(lowerCamelCase__   )
						if new_k not in valid_keys:
									failures.append([k, new_k]   )
						else:
									A_     :						Tuple     =					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__":
						lowerCamelCase							:List[Any]       = 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							:Any       = parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




'''simple docstring'''




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:Dict       = {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:str       = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						lowerCamelCase							:Tuple       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)