import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
				_lowercase:	int       			=		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:	List[str]       			=		parser.parse_args()

				if args.model_type == "roberta":
								_lowercase:	Union[str, Any]       			=		RobertaForMaskedLM.from_pretrained(args.model_name)
								_lowercase:	Optional[Any]       			=		'''roberta'''
				elif args.model_type == "gpt2":
								_lowercase:	Any       			=		GPTaLMHeadModel.from_pretrained(args.model_name)
								_lowercase:	Optional[int]       			=		'''transformer'''

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

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

    # Transformer Blocks #
				_lowercase:	Optional[Any]       			=		0
				for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
								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:	List[Any]       			=		state_dict[
																				    f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
																				]
												_lowercase:	Optional[Any]       			=		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:	List[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:	str       			=		state_dict[f"""{layer}"""]
								if args.vocab_transform:
												for w in ["weight", "bias"]:
																_lowercase:	Union[str, Any]       			=		state_dict[f"""lm_head.dense.{w}"""]
																_lowercase:	List[str]       			=		state_dict[f"""lm_head.layer_norm.{w}"""]
				elif args.model_type == "gpt2":
								for w in ["weight", "bias"]:
												_lowercase:	List[str]       			=		state_dict[f"""{prefix}.ln_f.{w}"""]
								_lowercase:	Tuple       			=		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)







import functools






def _lowerCamelCase  (		snake_case     , snake_case  ):

							# Validation
							if not isinstance(snake_case     , snake_case  ) or not all(isinstance(snake_case     , snake_case  ) for day in days  ):
														raise ValueError('The parameter days should be a list of integers'  )

							if len(snake_case  ) != 3 or not all(isinstance(snake_case     , snake_case  ) for cost in costs  ):
														raise ValueError('The parameter costs should be a list of three integers'  )

							if len(snake_case  ) == 0:
														return 0

							if min(snake_case  ) <= 0:
														raise ValueError('All days elements should be greater than 0'  )

							if max(snake_case  ) >= 366:
														raise ValueError('All days elements should be less than 366'  )

							_lowerCAmelCase							=							set(snake_case  )

							@functools.cache
							def dynamic_programming(snake_case  ) -> int:
														if index > 365:
																					return 0

														if index not in days_set:
																					return dynamic_programming(index + 1  )

														return min(
														    costs[0] + dynamic_programming(index + 1  )     , costs[1] + dynamic_programming(index + 7  )     , costs[2] + dynamic_programming(index + 30  )     , )

							return dynamic_programming(1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






