

'''simple docstring'''





from math import factorial





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		List[Any]		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Any								=real
										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																lowercase   :       int								=[1] * rank
										else:
																lowercase   :       Optional[int]								=rank







				def __repr__(					self    :		List[Any]      ):
										'''simple docstring'''
										return (
										    F'''{self.real}+'''
										    F'''{"+".join(str(UpperCAmelCase__      )+"E"+str(n+1      )for n,dual in enumerate(self.duals      )      )}'''
										)







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.duals.copy()
										while cur[-1] == 0:
																cur.pop(-1      )
										return Dual(self.real		,				UpperCAmelCase__      )







				def __add__(					self    :		Any		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										if not isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																return Dual(self.real + other		,				self.duals      )
										lowercase   :       Tuple								=self.duals.copy()
										lowercase   :       str								=other.duals.copy()
										if len(UpperCAmelCase__      ) > len(UpperCAmelCase__      ):
																o_dual.extend([1] * (len(UpperCAmelCase__      ) - len(UpperCAmelCase__      ))      )
										elif len(UpperCAmelCase__      ) < len(UpperCAmelCase__      ):
																s_dual.extend([1] * (len(UpperCAmelCase__      ) - len(UpperCAmelCase__      ))      )
										lowercase   :       Optional[Any]								=[]
										for i in range(len(UpperCAmelCase__      )      ):
																new_duals.append(s_dual[i] + o_dual[i]      )
										return Dual(self.real + other.real		,				UpperCAmelCase__      )

				lowerCamelCase_							=						__add__







				def __sub__(					self    :		List[str]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										return self + other * -1







				def __mul__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										if not isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																lowercase   :       str								=[]
																for i in self.duals:
																						new_duals.append(i * other      )
																return Dual(self.real * other		,				UpperCAmelCase__      )
										lowercase   :       Any								=[0] * (len(self.duals      ) + len(other.duals      ) + 1)
										for i, item in enumerate(self.duals      ):
																for j, jtem in enumerate(other.duals      ):
																						new_duals[i + j + 1] += item * jtem
										for k in range(len(self.duals      )      ):
																new_duals[k] += self.duals[k] * other.real
										for index in range(len(other.duals      )      ):
																new_duals[index] += other.duals[index] * self.real
										return Dual(self.real * other.real		,				UpperCAmelCase__      )

				lowerCamelCase_							=						__mul__







				def __truediv__(					self    :		int		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										if not isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																lowercase   :       Any								=[]
																for i in self.duals:
																						new_duals.append(i / other      )
																return Dual(self.real / other		,				UpperCAmelCase__      )
										raise ValueError







				def __floordiv__(					self    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										if not isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																lowercase   :       Dict								=[]
																for i in self.duals:
																						new_duals.append(i // other      )
																return Dual(self.real // other		,				UpperCAmelCase__      )
										raise ValueError







				def __pow__(					self    :		Any		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										if n < 0 or isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																raise ValueError('''power must be a positive integer'''      )
										if n == 0:
																return 1
										if n == 1:
																return self
										lowercase   :       Optional[int]								=self
										for _ in range(n - 1      ):
																x *= self
										return x






def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    List[str]      ,   __magic_name__	:    Optional[int]	)							->     Any:
						if not callable(__magic_name__	):
												raise ValueError('''differentiate() requires a function as input for func'''	)
						if not isinstance(__magic_name__      ,   (float, int)	):
												raise ValueError('''differentiate() requires a float as input for position'''	)
						if not isinstance(__magic_name__      ,   __magic_name__	):
												raise ValueError('''differentiate() requires an int as input for order'''	)
						lowercase   :       Optional[int]								=Dual(__magic_name__      ,   1	)
						lowercase   :       Tuple								=func(__magic_name__	)
						if order == 0:
												return result.real
						return result.duals[order - 1] * factorial(__magic_name__	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()






							def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]	)							->     str:
													return y**2 * y**4







							print(differentiate(f, 9, 2))



'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

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

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





