

'''simple docstring'''





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						(CMStochasticIterativeScheduler,)
				lowerCamelCase_							=						10







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								={
										    '''num_train_timesteps''': 201,
										    '''sigma_min''': 0.0_02,
										    '''sigma_max''': 80.0,
										}

										config.update(**UpperCAmelCase__      )
										return config







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       str								=10

										lowercase   :       Dict								=self.get_scheduler_config()
										lowercase   :       Union[str, Any]								=self.scheduler_classes[0](**UpperCAmelCase__      )

										scheduler.set_timesteps(UpperCAmelCase__      )

										lowercase   :       Optional[int]								=scheduler.timesteps[0]
										lowercase   :       List[Any]								=scheduler.timesteps[1]

										lowercase   :       List[Any]								=self.dummy_sample
										lowercase   :       Tuple								=0.1 * sample

										lowercase   :       Union[str, Any]								=scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      ).prev_sample
										lowercase   :       Optional[int]								=scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      ).prev_sample

										self.assertEqual(output_a.shape		,				sample.shape      )
										self.assertEqual(output_a.shape		,				output_a.shape      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										for clip_denoised in [True, False]:
																self.check_over_configs(clip_denoised=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Any								=self.scheduler_classes[0]
										lowercase   :       Dict								=self.get_scheduler_config()
										lowercase   :       Optional[Any]								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Optional[int]								=1
										scheduler.set_timesteps(UpperCAmelCase__      )
										lowercase   :       Optional[int]								=scheduler.timesteps

										lowercase   :       Optional[int]								=torch.manual_seed(0      )

										lowercase   :       Union[str, Any]								=self.dummy_model()
										lowercase   :       Any								=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(UpperCAmelCase__      ):
																# 1. scale model input
																lowercase   :       Tuple								=scheduler.scale_model_input(UpperCAmelCase__		,				UpperCAmelCase__      )

																# 2. predict noise residual
																lowercase   :       Optional[Any]								=model(UpperCAmelCase__		,				UpperCAmelCase__      )

																# 3. predict previous sample x_t-1
																lowercase   :       Tuple								=scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

																lowercase   :       List[Any]								=pred_prev_sample

										lowercase   :       int								=torch.sum(torch.abs(UpperCAmelCase__      )      )
										lowercase   :       List[str]								=torch.mean(torch.abs(UpperCAmelCase__      )      )

										assert abs(result_sum.item() - 1_92.76_14      ) < 1E-2
										assert abs(result_mean.item() - 0.25_10      ) < 1E-3







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       str								=self.scheduler_classes[0]
										lowercase   :       Optional[Any]								=self.get_scheduler_config()
										lowercase   :       str								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=[106, 0]
										scheduler.set_timesteps(timesteps=UpperCAmelCase__      )
										lowercase   :       Any								=scheduler.timesteps

										lowercase   :       Dict								=torch.manual_seed(0      )

										lowercase   :       Union[str, Any]								=self.dummy_model()
										lowercase   :       Tuple								=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
																# 1. scale model input
																lowercase   :       Optional[Any]								=scheduler.scale_model_input(UpperCAmelCase__		,				UpperCAmelCase__      )

																# 2. predict noise residual
																lowercase   :       str								=model(UpperCAmelCase__		,				UpperCAmelCase__      )

																# 3. predict previous sample x_t-1
																lowercase   :       Tuple								=scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

																lowercase   :       Dict								=pred_prev_sample

										lowercase   :       str								=torch.sum(torch.abs(UpperCAmelCase__      )      )
										lowercase   :       int								=torch.mean(torch.abs(UpperCAmelCase__      )      )

										assert abs(result_sum.item() - 3_47.63_57      ) < 1E-2
										assert abs(result_mean.item() - 0.45_27      ) < 1E-3







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.scheduler_classes[0]
										lowercase   :       Dict								=self.get_scheduler_config()
										lowercase   :       str								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Any								=[39, 30, 12, 15, 0]

										with self.assertRaises(UpperCAmelCase__		,				msg='''`timesteps` must be in descending order.'''      ):
																scheduler.set_timesteps(timesteps=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.scheduler_classes[0]
										lowercase   :       Dict								=self.get_scheduler_config()
										lowercase   :       Dict								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=[39, 30, 12, 1, 0]
										lowercase   :       str								=len(UpperCAmelCase__      )

										with self.assertRaises(UpperCAmelCase__		,				msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''      ):
																scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__		,				timesteps=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.scheduler_classes[0]
										lowercase   :       Union[str, Any]								=self.get_scheduler_config()
										lowercase   :       Optional[Any]								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    UpperCAmelCase__		,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,				):
																scheduler.set_timesteps(timesteps=UpperCAmelCase__      )








'''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)





