
'''simple docstring'''







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
					lowercase    :							List[Any]      =					argparse.ArgumentParser(
					    description=(
					        "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
					        " Distillation"
					    )
					)
					parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
					parser.add_argument("--model_name", default="roberta-large", type=str)
					parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
					parser.add_argument("--vocab_transform", action="store_true")
					lowercase    :							Any      =					parser.parse_args()

					if args.model_type == "roberta":
										lowercase    :							Optional[int]      =					RobertaForMaskedLM.from_pretrained(args.model_name)
										lowercase    :							List[Any]      =					"roberta"
					elif args.model_type == "gpt2":
										lowercase    :							Dict      =					GPTaLMHeadModel.from_pretrained(args.model_name)
										lowercase    :							Any      =					"transformer"

					lowercase    :							str      =					model.state_dict()
					lowercase    :							Dict      =					{}

					# Embeddings #
					if args.model_type == "gpt2":
										for param_name in ["wte.weight", "wpe.weight"]:
															lowercase    :							str      =					state_dict[F'''{prefix}.{param_name}''']
					else:
										for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
															lowercase    :							List[str]      =					F'''{prefix}.embeddings.{w}.weight'''
															lowercase    :							Union[str, Any]      =					state_dict[param_name]
										for w in ["weight", "bias"]:
															lowercase    :							Union[str, Any]      =					F'''{prefix}.embeddings.LayerNorm.{w}'''
															lowercase    :							List[str]      =					state_dict[param_name]

    # Transformer Blocks #
					lowercase    :							Union[str, Any]      =					0
					for teacher_idx in [0, 2, 4, 7, 9, 11]:
										if args.model_type == "gpt2":
															for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																				for w in ["weight", "bias"]:
																									lowercase    :							Union[str, Any]      =					state_dict[
																									    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																									]
															lowercase    :							str      =					state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
										else:
															for layer in [
															    "attention.self.query",
															    "attention.self.key",
															    "attention.self.value",
															    "attention.output.dense",
															    "attention.output.LayerNorm",
															    "intermediate.dense",
															    "output.dense",
															    "output.LayerNorm",
															]:
																				for w in ["weight", "bias"]:
																									lowercase    :							Union[str, Any]      =					state_dict[
																									    F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																									]
										std_idx += 1

					# Language Modeling Head ###s
					if args.model_type == "roberta":
										for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
															lowercase    :							Dict      =					state_dict[F'''{layer}''']
										if args.vocab_transform:
															for w in ["weight", "bias"]:
																				lowercase    :							List[Any]      =					state_dict[F'''lm_head.dense.{w}''']
																				lowercase    :							Dict      =					state_dict[F'''lm_head.layer_norm.{w}''']
					elif args.model_type == "gpt2":
										for w in ["weight", "bias"]:
															lowercase    :							Optional[Any]      =					state_dict[F'''{prefix}.ln_f.{w}''']
										lowercase    :							Optional[int]      =					state_dict["lm_head.weight"]

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)









"""simple docstring"""


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     list[int]  ):

     '''simple docstring'''
     __A									= len(_lowerCamelCase  )
     __A									= [0] * len_array

     if len_array > 0:
       __A									= array[0]

     for i in range(1,       _lowerCamelCase  ):
       __A									= self.prefix_sum[i - 1] + array[i]


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     if start == 0:
       return self.prefix_sum[end]

     return self.prefix_sum[end] - self.prefix_sum[start - 1]






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int  ):

     '''simple docstring'''
     __A									= {0}
     for sum_item in self.prefix_sum:
       if sum_item - target_sum in sums:
         return True

       sums.add(_lowerCamelCase  )

     return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()




