'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class     __UpperCAmelCase       ( __a		):
  __A :				Tuple											= (DDPMScheduler,)





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**_lowerCamelCase )
     return config





  def 						UpperCAmelCase_	(	self ):
     for timesteps in [1, 5, 100, 1000]:
        self.check_over_configs(num_train_timesteps=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,     [0.0_02, 0.02, 0.2, 2] ):
        self.check_over_configs(beta_start=_lowerCamelCase    ,     beta_end=_lowerCamelCase )





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





  def 						UpperCAmelCase_	(	self ):
     for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     self.check_over_configs(thresholding=_lowerCamelCase )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(
               thresholding=_lowerCamelCase    ,     prediction_type=_lowerCamelCase    ,     sample_max_value=_lowerCamelCase    ,     )





  def 						UpperCAmelCase_	(	self ):
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(prediction_type=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     for t in [0, 500, 999]:
        self.check_over_forward(time_step=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_09_79 ) ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      len(_lowerCamelCase )

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

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

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

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

     lowerCAmelCase_			=      torch.sum(torch.abs(_lowerCamelCase ) )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
     assert abs(result_mean.item() - 0.33_72 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config(prediction_type='''v_prediction''' )
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      len(_lowerCamelCase )

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

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

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

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

     lowerCAmelCase_			=      torch.sum(torch.abs(_lowerCamelCase ) )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
     assert abs(result_mean.item() - 0.26_31 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=_lowerCamelCase )

     lowerCAmelCase_			=      scheduler.timesteps

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

        lowerCAmelCase_			=      scheduler.previous_timestep(_lowerCamelCase )
        lowerCAmelCase_			=      prev_t.item()

        self.assertEqual(_lowerCamelCase    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [100, 87, 50, 51, 0]

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [100, 87, 50, 1, 0]
     lowerCAmelCase_			=      len(_lowerCamelCase )

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







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.scheduler_classes[0]
     lowerCAmelCase_			=      self.get_scheduler_config()
     lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

     lowerCAmelCase_			=      [scheduler.config.num_train_timesteps]

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






'''simple docstring'''

import math
import sys



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      ''''''
   try:
      with open(__snake_case	,   '''rb''') as binary_file:
         lowerCAmelCase_			=      binary_file.read()
      for dat in data:
         lowerCAmelCase_			=      F'''{dat:08b}'''
         result += curr_byte
      return result
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      {'''0''': '''0''', '''1''': '''1'''}
   lowerCAmelCase_	,lowerCAmelCase_			=      '''''', ''''''
   lowerCAmelCase_			=      len(__snake_case)

   for i in range(len(__snake_case)):
      curr_string += data_bits[i]
      if curr_string not in lexicon:
         continue

      lowerCAmelCase_			=      lexicon[curr_string]
      result += last_match_id
      lowerCAmelCase_			=      last_match_id + '''0'''

      if math.loga(__snake_case).is_integer():
         lowerCAmelCase_			=      {}
         for curr_key in list(__snake_case):
            lowerCAmelCase_			=      lexicon.pop(__snake_case)
         lowerCAmelCase_			=      new_lex

      lowerCAmelCase_			=      last_match_id + '''1'''
      index += 1
      lowerCAmelCase_			=      ''''''
   return result



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      8
   try:
      with open(__snake_case	,   '''wb''') as opened_file:
         lowerCAmelCase_			=      [
             to_write[i : i + byte_length]
             for i in range(0	,   len(__snake_case)	,   __snake_case)
         ]

         if len(result_byte_array[-1]) % byte_length == 0:
            result_byte_array.append('''10000000''')
         else:
            result_byte_array[-1] += "1" + "0" * (
                byte_length - len(result_byte_array[-1]) - 1
            )

         for elem in result_byte_array[:-1]:
            opened_file.write(int(__snake_case	,   2).to_bytes(1	,   byteorder='''big'''))
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      0
   for letter in data_bits:
      if letter == "1":
         break
      counter += 1

   lowerCAmelCase_			=      data_bits[counter:]
   lowerCAmelCase_			=      data_bits[counter + 1 :]
   return data_bits



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      read_file_binary(__snake_case)
   lowerCAmelCase_			=      remove_prefix(__snake_case)
   lowerCAmelCase_			=      decompress_data(__snake_case)
   write_file_binary(__snake_case	,   __snake_case)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])





