'''simple docstring'''

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class     __UpperCAmelCase       ( __a		):
  __A :				Union[str, Any]											= (DPMSolverSinglestepScheduler,)
  __A :				List[str]											= (('num_inference_steps', 25),)





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
         '''solver_order''': 2,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
         '''sample_max_value''': 1.0,
         '''algorithm_type''': '''dpmsolver++''',
         '''solver_type''': '''midpoint''',
         '''lambda_min_clipped''': -float('''inf''' ),
         '''variance_type''': None,
     }

     config.update(**_lowerCamelCase )
     return config





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase=0    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      dict(self.forward_default_kwargs )
     lowerCAmelCase_			=      kwargs.pop('''num_inference_steps'''    ,     _lowerCamelCase )
     lowerCAmelCase_			=      self.dummy_sample
     lowerCAmelCase_			=      0.1 * sample
     lowerCAmelCase_			=      [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			=      self.get_scheduler_config(**_lowerCamelCase )
        lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )
        scheduler.set_timesteps(_lowerCamelCase )
        # copy over dummy past residuals
        lowerCAmelCase_			=      dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_lowerCamelCase )
           lowerCAmelCase_			=      scheduler_class.from_pretrained(_lowerCamelCase )
           new_scheduler.set_timesteps(_lowerCamelCase )
           # copy over dummy past residuals
           lowerCAmelCase_			=      dummy_past_residuals[: new_scheduler.config.solver_order]

        lowerCAmelCase_	,lowerCAmelCase_			=      sample, sample
        for t in range(_lowerCamelCase    ,     time_step + scheduler.config.solver_order + 1 ):
           lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ).prev_sample
           lowerCAmelCase_			=      new_scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ).prev_sample

           assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"





  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase=0    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      dict(self.forward_default_kwargs )
     lowerCAmelCase_			=      kwargs.pop('''num_inference_steps'''    ,     _lowerCamelCase )
     lowerCAmelCase_			=      self.dummy_sample
     lowerCAmelCase_			=      0.1 * sample
     lowerCAmelCase_			=      [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			=      self.get_scheduler_config()
        lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )
        scheduler.set_timesteps(_lowerCamelCase )

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			=      dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_lowerCamelCase )
           lowerCAmelCase_			=      scheduler_class.from_pretrained(_lowerCamelCase )
           # copy over dummy past residuals
           new_scheduler.set_timesteps(_lowerCamelCase )

           # copy over dummy past residual (must be after setting timesteps)
           lowerCAmelCase_			=      dummy_past_residuals[: new_scheduler.config.solver_order]

        lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ).prev_sample
        lowerCAmelCase_			=      new_scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ).prev_sample

        assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase=None    ,     **_lowerCamelCase ):
     if scheduler is None:
        lowerCAmelCase_			=      self.scheduler_classes[0]
        lowerCAmelCase_			=      self.get_scheduler_config(**_lowerCamelCase )
        lowerCAmelCase_			=      scheduler_class(**_lowerCamelCase )

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

     lowerCAmelCase_			=      10
     lowerCAmelCase_			=      self.dummy_model()
     lowerCAmelCase_			=      self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCamelCase )

     for i, t in enumerate(scheduler.timesteps ):
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     _lowerCamelCase )
        lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ).prev_sample

     return sample





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
     lowerCAmelCase_			=      50
     lowerCAmelCase_			=      self.dummy_model()
     lowerCAmelCase_			=      self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCamelCase )

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:] ):
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     _lowerCamelCase )
        lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ).prev_sample

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

     assert abs(result_mean.item() - 0.25_74 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     for timesteps in [25, 50, 100, 999, 1000]:
        self.check_over_configs(num_train_timesteps=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     lowerCAmelCase_			=      DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
     lowerCAmelCase_			=      self.full_loop(scheduler=_lowerCamelCase )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_mean.item() - 0.27_91 ) < 1E-3

     lowerCAmelCase_			=      DEISMultistepScheduler.from_config(scheduler.config )
     lowerCAmelCase_			=      DPMSolverMultistepScheduler.from_config(scheduler.config )
     lowerCAmelCase_			=      UniPCMultistepScheduler.from_config(scheduler.config )
     lowerCAmelCase_			=      DPMSolverSinglestepScheduler.from_config(scheduler.config )

     lowerCAmelCase_			=      self.full_loop(scheduler=_lowerCamelCase )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_mean.item() - 0.27_91 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     self.check_over_configs(thresholding=_lowerCamelCase )
     for order in [1, 2, 3]:
        for solver_type in ["midpoint", "heun"]:
           for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=_lowerCamelCase    ,     prediction_type=_lowerCamelCase    ,     sample_max_value=_lowerCamelCase    ,     algorithm_type='''dpmsolver++'''    ,     solver_order=_lowerCamelCase    ,     solver_type=_lowerCamelCase    ,     )





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





  def 						UpperCAmelCase_	(	self ):
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
        for solver_type in ["midpoint", "heun"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=_lowerCamelCase    ,     solver_type=_lowerCamelCase    ,     prediction_type=_lowerCamelCase    ,     algorithm_type=_lowerCamelCase    ,     )
                 lowerCAmelCase_			=      self.full_loop(
                     solver_order=_lowerCamelCase    ,     solver_type=_lowerCamelCase    ,     prediction_type=_lowerCamelCase    ,     algorithm_type=_lowerCamelCase    ,     )
                 assert not torch.isnan(_lowerCamelCase ).any(), "Samples have nan numbers"





  def 						UpperCAmelCase_	(	self ):
     self.check_over_configs(lower_order_final=_lowerCamelCase )
     self.check_over_configs(lower_order_final=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
     self.check_over_configs(lambda_min_clipped=-5.1 )





  def 						UpperCAmelCase_	(	self ):
     self.check_over_configs(variance_type=_lowerCamelCase )
     self.check_over_configs(variance_type='''learned_range''' )





  def 						UpperCAmelCase_	(	self ):
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
        self.check_over_forward(num_inference_steps=_lowerCamelCase    ,     time_step=0 )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.full_loop()
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_mean.item() - 0.27_91 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.full_loop(use_karras_sigmas=_lowerCamelCase )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_mean.item() - 0.22_48 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.full_loop(prediction_type='''v_prediction''' )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_mean.item() - 0.14_53 ) < 1E-3





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.full_loop(prediction_type='''v_prediction'''    ,     use_karras_sigmas=_lowerCamelCase )
     lowerCAmelCase_			=      torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_mean.item() - 0.06_49 ) < 1E-3







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

     lowerCAmelCase_			=      10
     lowerCAmelCase_			=      self.dummy_model()
     lowerCAmelCase_			=      self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCamelCase )

     for i, t in enumerate(scheduler.timesteps ):
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     _lowerCamelCase )
        lowerCAmelCase_			=      scheduler.step(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ).prev_sample

     assert sample.dtype == torch.floataa






'''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])





