



'''simple docstring'''




import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase_							:int       = logging.get_logger(__name__)

lowerCamelCase_							:List[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_     :						Optional[int]     =					k.replace(snake_case__						,				snake_case__   )

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



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





			A_     :						int     =					[
			    """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[Any]     =					sd.pop(snake_case__   )
						A_     :						Any     =					k.replace("""layernorm_embedding"""						,				"""layer_norm"""   )
						assert new_k not in sd
						A_     :						Dict     =					v


lowerCamelCase_							:Dict       = ['''START''']



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





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

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


if __name__ == "__main__":
						lowerCamelCase_							: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_							:Optional[int]       = 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      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				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)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				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	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)