





'''simple docstring'''






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class  _A (					tf.keras.layers.Layer		):

		def __init__(   self					:   Dict  ,      __magic_name__					:   Dict[str, int]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   int = None  ,      __magic_name__					:   int = None			)					->    Optional[Any]:


					"""simple docstring"""

					super().__init__()
					__snake_case					:      str							      =     pad_token_id
					__snake_case					:      Tuple							      =     max_length
					__snake_case					:      Dict							      =     vocab
					__snake_case					:      Optional[Any]							      =     merges
					__snake_case					:      Optional[Any]							      =     BytePairTokenizer(__magic_name__  ,      __magic_name__  ,      sequence_length=__magic_name__			)

		@classmethod
		def 	lowercase__     (   cls					:   int  ,      __magic_name__					:   GPTaTokenizer  ,      *__magic_name__					:   int  ,      **__magic_name__					:   int			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [""" """.join(__magic_name__			) for m in tokenizer.bpe_ranks.keys()]
					__snake_case					:      List[str]							      =     tokenizer.get_vocab()
					return cls(__magic_name__  ,      __magic_name__  ,      *__magic_name__  ,      **__magic_name__			)

		@classmethod
		def 	lowercase__     (   cls					:   int  ,      __magic_name__					:   Union[str, os.PathLike]  ,      *__magic_name__					:   Dict  ,      **__magic_name__					:   str			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     GPTaTokenizer.from_pretrained(__magic_name__  ,      *__magic_name__  ,      **__magic_name__			)
					return cls.from_tokenizer(__magic_name__  ,      *__magic_name__  ,      **__magic_name__			)

		@classmethod
		def 	lowercase__     (   cls					:   Any  ,      __magic_name__					:   Dict			)					->    List[Any]:


					"""simple docstring"""

					return cls(**__magic_name__			)

		def 	lowercase__     (   self					:   Optional[Any]			)					->    str:


					"""simple docstring"""

					return {
					    "vocab": self.vocab,
					    "merges": self.merges,
					    "max_length": self.max_length,
					    "pad_token_id": self.pad_token_id,
					}

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   int = None			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tf_tokenizer(__magic_name__			)
					__snake_case					:      Any							      =     tf.ones_like(__magic_name__			)

					if self.pad_token_id is not None:
								# pad the tokens up to max length
								__snake_case					:      Dict							      =     max_length if max_length is not None else self.max_length

								if max_length is not None:
											__snake_case      ,     __snake_case					:      Any							      =     pad_model_inputs(
											    __magic_name__  ,      max_seq_length=__magic_name__  ,      pad_value=self.pad_token_id			)

					return {"attention_mask": attention_mask, "input_ids": input_ids}









'''simple docstring'''






import argparse

import torch

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


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

__UpperCamelCase					    =	[
    ["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 ) ->				Union[str, Any]:



			"""simple docstring"""


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

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

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







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


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


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


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

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


