
"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				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"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DDPMScheduler,)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,						[0.002, 0.02, 0.2, 2]):
												self.check_over_configs(beta_start=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							self.check_over_configs(thresholding=__a)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=__a							,						prediction_type=__a							,						sample_max_value=__a							,						)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 258.9606) < 1e-2
							assert abs(result_mean.item() - 0.3372) < 1e-3



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 202.0296) < 1e-2
							assert abs(result_mean.item() - 0.2631) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=__a)

							_UpperCamelCase									=    scheduler.timesteps

							for i, timestep in enumerate(__a):
												if i == len(__a) - 1:
																	_UpperCamelCase									=    -1
												else:
																	_UpperCamelCase									=    timesteps[i + 1]

												_UpperCamelCase									=    scheduler.previous_timestep(__a)
												_UpperCamelCase									=    prev_t.item()

												self.assertEqual(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 51, 0]

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



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]
							_UpperCamelCase									=    len(__a)

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




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [scheduler.config.num_train_timesteps]

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






