

'''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)








'''simple docstring'''





from __future__ import annotations






def 							_lowerCAmelCase (      __magic_name__	:    list[list[int]]	)							->     bool:
						lowercase   :       str								=len(__magic_name__	)
						# We need to create solution object to save path.
						lowercase   :       int								=[[0 for _ in range(__magic_name__	)] for _ in range(__magic_name__	)]
						lowercase   :       List[Any]								=run_maze(__magic_name__      ,   0      ,   0      ,   __magic_name__	)
						if solved:
												print('''\n'''.join(str(__magic_name__	) for row in solutions	)	)
						else:
												print('''No solution exists!'''	)
						return solved






def 							_lowerCAmelCase (      __magic_name__	:    list[list[int]]      ,   __magic_name__	:    int      ,   __magic_name__	:    int      ,   __magic_name__	:    list[list[int]]	)							->     bool:
						lowercase   :       Optional[int]								=len(__magic_name__	)
						# Final check point.
						if i == j == (size - 1):
												lowercase   :       Optional[int]								=1
												return True

						lowercase   :       Optional[int]								=(not i < 0) and (not j < 0)  # Check lower bounds
						lowercase   :       Tuple								=(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												lowercase   :       Union[str, Any]								=(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		lowercase   :       Union[str, Any]								=1

																		# check for directions
																		if (
																		    run_maze(__magic_name__      ,   i + 1      ,   __magic_name__      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   __magic_name__      ,   j + 1      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   i - 1      ,   __magic_name__      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   __magic_name__      ,   j - 1      ,   __magic_name__	)
																		):
																								return True

																		lowercase   :       str								=0
																		return False
						return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()





