





import argparse

import torch

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


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

_lowerCamelCase 					=      [
    ['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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		if k == "embeddings.weight":
				return "shared.weight"

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

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






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


_lowerCamelCase 					=      ['START']






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

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





'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


