

"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 				SCREAMING_SNAKE_CASE					(		UpperCAmelCase__       ):






					"""simple docstring"""



					lowercase__				 =       (DDIMParallelScheduler,)
					lowercase__				 =       (("eta", 0.0), ("num_inference_steps", 50))







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,**lowercase_			:				Optional[int]		):
						lowerCAmelCase__	:						Any  				=	{
						    '''num_train_timesteps''': 1_0_0_0,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''clip_sample''': True,
						}

						config.update(**lowercase_		)
						return config







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						str  				=	self.get_scheduler_config(**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						int  				=	1_0, 0.0

						lowerCAmelCase__	:						List[Any]  				=	self.dummy_model()
						lowerCAmelCase__	:						Tuple  				=	self.dummy_sample_deter

						scheduler.set_timesteps(lowercase_		)

						for t in scheduler.timesteps:
							lowerCAmelCase__	:						Union[str, Any]  				=	model(lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						Any  				=	scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		).prev_sample

						return sample







					def        __lowerCAmelCase (       self			:				str		):


						for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):


						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						List[Any]  				=	self.get_scheduler_config(steps_offset=1		)
						lowerCAmelCase__	:						Optional[int]  				=	scheduler_class(**lowercase_		)
						scheduler.set_timesteps(5		)
						assert torch.equal(scheduler.timesteps   ,torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]		)		)







					def        __lowerCAmelCase (       self			:				List[Any]		):


						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=lowercase_   ,beta_end=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict		):


						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):


						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):


						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):


						for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):


						for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						self.check_over_configs(thresholding=lowercase_		)
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=lowercase_   ,prediction_type=lowercase_   ,sample_max_value=lowercase_   ,)







					def        __lowerCAmelCase (       self			:				Optional[int]		):


						for t in [1, 1_0, 4_9]:
							self.check_over_forward(time_step=lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):


						for t, num_inference_steps in zip([1, 1_0, 5_0]   ,[1_0, 5_0, 5_0_0]		):
							self.check_over_forward(time_step=lowercase_   ,num_inference_steps=lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple		):


						for t, eta in zip([1, 1_0, 4_9]   ,[0.0, 0.5, 1.0]		):
							self.check_over_forward(time_step=lowercase_   ,eta=lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						str  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_scheduler_config()
						lowerCAmelCase__	:						List[Any]  				=	scheduler_class(**lowercase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0   ,0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_2_0   ,4_0_0		) - 0.1_4771		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_8_0   ,9_6_0		) - 0.3_2460		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0   ,0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ,4_8_6		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ,9_9_8		) - 0.02		)		) < 1E-5







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	self.scheduler_classes[0]
						lowerCAmelCase__	:						Optional[Any]  				=	self.get_scheduler_config()
						lowerCAmelCase__	:						Optional[Any]  				=	scheduler_class(**lowercase_		)

						lowerCAmelCase__	:						Dict  				=	1_0, 0.0
						scheduler.set_timesteps(lowercase_		)

						lowerCAmelCase__	:						Any  				=	self.dummy_model()
						lowerCAmelCase__	:						Optional[int]  				=	self.dummy_sample_deter
						lowerCAmelCase__	:						Tuple  				=	self.dummy_sample_deter + 0.1
						lowerCAmelCase__	:						List[Any]  				=	self.dummy_sample_deter - 0.1

						lowerCAmelCase__	:						Dict  				=	samplea.shape[0]
						lowerCAmelCase__	:						Optional[int]  				=	torch.stack([samplea, samplea, samplea]   ,dim=0		)
						lowerCAmelCase__	:						List[Any]  				=	torch.arange(lowercase_		)[0:3, None].repeat(1   ,lowercase_		)

						lowerCAmelCase__	:						str  				=	model(samples.flatten(0   ,1		)   ,timesteps.flatten(0   ,1		)		)
						lowerCAmelCase__	:						Any  				=	scheduler.batch_step_no_noise(lowercase_   ,timesteps.flatten(0   ,1		)   ,samples.flatten(0   ,1		)   ,lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	torch.sum(torch.abs(lowercase_		)		)
						lowerCAmelCase__	:						int  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_sum.item() - 1_1_4_7.7_9_0_4		) < 1E-2
						assert abs(result_mean.item() - 0.4982		) < 1E-3







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						str  				=	self.full_loop()

						lowerCAmelCase__	:						Dict  				=	torch.sum(torch.abs(lowercase_		)		)
						lowerCAmelCase__	:						int  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_sum.item() - 1_7_2.0_0_6_7		) < 1E-2
						assert abs(result_mean.item() - 0.22_3967		) < 1E-3







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						str  				=	self.full_loop(prediction_type='''v_prediction'''		)

						lowerCAmelCase__	:						Dict  				=	torch.sum(torch.abs(lowercase_		)		)
						lowerCAmelCase__	:						List[str]  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_sum.item() - 52.5302		) < 1E-2
						assert abs(result_mean.item() - 0.0684		) < 1E-3







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						int  				=	self.full_loop(set_alpha_to_one=lowercase_   ,beta_start=0.01		)
						lowerCAmelCase__	:						List[str]  				=	torch.sum(torch.abs(lowercase_		)		)
						lowerCAmelCase__	:						int  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_sum.item() - 1_4_9.8_2_9_5		) < 1E-2
						assert abs(result_mean.item() - 0.1951		) < 1E-3







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Tuple  				=	self.full_loop(set_alpha_to_one=lowercase_   ,beta_start=0.01		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.sum(torch.abs(lowercase_		)		)
						lowerCAmelCase__	:						Optional[Any]  				=	torch.mean(torch.abs(lowercase_		)		)

						assert abs(result_sum.item() - 1_4_9.0_7_8_4		) < 1E-2
						assert abs(result_mean.item() - 0.1941		) < 1E-3












"""simple docstring"""






import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_   : Dict   			= logging.getLogger(__name__)

if __name__ == "__main__":
  UpperCAmelCase_   : List[str]   			= argparse.ArgumentParser(
      description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
  )
  parser.add_argument(
      """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
  )
  parser.add_argument(
      """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
  )
  parser.add_argument("""--vocab_size""", default=30522, type=int)
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()

  logger.info(f'''Loading data from {args.data_file}''')
  with open(args.data_file, """rb""") as fp:
    UpperCAmelCase_   : Union[str, Any]   			= pickle.load(fp)

  logger.info("""Counting occurrences for MLM.""")
  UpperCAmelCase_   : Any   			= Counter()
  for tk_ids in data:
    counter.update(tk_ids)
  UpperCAmelCase_   : List[Any]   			= [0] * args.vocab_size
  for k, v in counter.items():
    UpperCAmelCase_   : Dict   			= v

  logger.info(f'''Dump to {args.token_counts_dump}''')
  with open(args.token_counts_dump, """wb""") as handle:
    pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


