


"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class    lowerCamelCase__						(		_a    ):
      a :     Optional[int]											=		(UnCLIPScheduler,)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       **A_	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	{
           """num_train_timesteps""": 1_0_0_0,
           """variance_type""": """fixed_small_log""",
           """clip_sample""": True,
           """clip_sample_range""": 1.0,
           """prediction_type""": """epsilon""",
       }

       config.update(**A_							)
       return config
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       for variance in ["fixed_small_log", "learned_range"]:
        self.check_over_configs(variance_type=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       for clip_sample_range in [1, 5, 1_0, 2_0]:
        self.check_over_configs(clip_sample_range=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(prediction_type=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       for time_step in [0, 5_0_0, 9_9_9]:
        for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
         if prev_timestep is not None and prev_timestep >= time_step:
          continue

         self.check_over_forward(time_step=A_      ,       prev_timestep=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config(variance_type="""fixed_small_log"""							)
       __lowercase									=	scheduler_class(**A_							)

       assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_0_0_0e-1_0							)							) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_54_96_25							)							) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.9_99_49_87							)							) < 1e-5
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config(variance_type="""learned_range"""							)
       __lowercase									=	scheduler_class(**A_							)

       __lowercase									=	0.5

       assert scheduler._get_variance(1      ,       predicted_variance=A_							) - -10.1_71_27_90 < 1e-5
       assert scheduler._get_variance(4_8_7      ,       predicted_variance=A_							) - -5.7_99_80_52 < 1e-5
       assert scheduler._get_variance(9_9_9      ,       predicted_variance=A_							) - -0.0_01_00_11 < 1e-5
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config()
       __lowercase									=	scheduler_class(**A_							)

       __lowercase									=	scheduler.timesteps

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

       for i, t in enumerate(A_							):
        # 1. predict noise residual
        __lowercase									=	model(A_      ,       A_							)

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

        __lowercase									=	pred_prev_sample

       __lowercase									=	torch.sum(torch.abs(A_							)							)
       __lowercase									=	torch.mean(torch.abs(A_							)							)

       assert abs(result_sum.item() - 2_52.2_68_24_95							) < 1e-2
       assert abs(result_mean.item() - 0.3_28_47_43							) < 1e-3
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.scheduler_classes[0]
       __lowercase									=	self.get_scheduler_config()
       __lowercase									=	scheduler_class(**A_							)

       scheduler.set_timesteps(2_5							)

       __lowercase									=	scheduler.timesteps

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

       for i, t in enumerate(A_							):
        # 1. predict noise residual
        __lowercase									=	model(A_      ,       A_							)

        if i + 1 == timesteps.shape[0]:
         __lowercase									=	None
        else:
         __lowercase									=	timesteps[i + 1]

        # 2. predict previous mean of sample x_t-1
        __lowercase									=	scheduler.step(
            A_      ,       A_      ,       A_      ,       prev_timestep=A_      ,       generator=A_							).prev_sample

        __lowercase									=	pred_prev_sample

       __lowercase									=	torch.sum(torch.abs(A_							)							)
       __lowercase									=	torch.mean(torch.abs(A_							)							)

       assert abs(result_sum.item() - 2_58.2_04_49_83							) < 1e-2
       assert abs(result_mean.item() - 0.3_36_20_38							) < 1e-3
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       pass






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       pass




"""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__  							=logging.getLogger(__name__)

if __name__ == "__main__":
       UpperCAmelCase__  							=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=3_0522, type=int)
       UpperCAmelCase__  							=parser.parse_args()

       logger.info(f"""Loading data from {args.data_file}""")
       with open(args.data_file, "rb") as fp:
              UpperCAmelCase__  							=pickle.load(fp)

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