
"""simple docstring"""







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowerCamelCase__   					=		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""")
							lowerCamelCase__   					=		parser.parse_args()

							if args.model_type == "roberta":
														lowerCamelCase__   					=		RobertaForMaskedLM.from_pretrained(args.model_name)
														lowerCamelCase__   					=		"""roberta"""
							elif args.model_type == "gpt2":
														lowerCamelCase__   					=		GPTaLMHeadModel.from_pretrained(args.model_name)
														lowerCamelCase__   					=		"""transformer"""

							lowerCamelCase__   					=		model.state_dict()
							lowerCamelCase__   					=		{}

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

    # Transformer Blocks #
							lowerCamelCase__   					=		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"]:
																																			lowerCamelCase__   					=		state_dict[
																																			    f'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowerCamelCase__   					=		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"]:
																																			lowerCamelCase__   					=		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"]:
																					lowerCamelCase__   					=		state_dict[f'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowerCamelCase__   					=		state_dict[f'lm_head.dense.{w}']
																												lowerCamelCase__   					=		state_dict[f'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowerCamelCase__   					=		state_dict[f'{prefix}.ln_f.{w}']
														lowerCamelCase__   					=		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"""






from __future__ import annotations


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    int:
      '''simple docstring'''


      if not nums:
            return 0
      lowercase														=	nums[0]
      lowercase														=	0
      for num in nums[1:]:
            lowercase							,							lowercase														=	(
                max_excluding + num,
                max(lowerCAmelCase__					,		lowerCAmelCase__		),
            )
      return max(lowerCAmelCase__					,		lowerCAmelCase__		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



