



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


a__      :				Optional[int]   	=			False



class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Optional[Any]=32)	->				str:




         """simple docstring"""

         set_seed(0)
         lowercase__     =  UNetaDModel(sample_size=lowerCAmelCase			,				in_channels=3			,				out_channels=3)
         lowercase__     =  torch.optim.SGD(model.parameters()			,				lr=0.00_01)
         return model, optimizer



   @slow
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				List[str]:




         """simple docstring"""

         lowercase__     =  'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         lowercase__     =  DDPMScheduler(
             num_train_timesteps=10_00			,				beta_start=0.00_01			,				beta_end=0.02			,				beta_schedule='linear'			,				clip_sample=lowerCAmelCase			,				)
         lowercase__     =  DDIMScheduler(
             num_train_timesteps=10_00			,				beta_start=0.00_01			,				beta_end=0.02			,				beta_schedule='linear'			,				clip_sample=lowerCAmelCase			,				)

         assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

         # shared batches for DDPM and DDIM
         set_seed(0)
         lowercase__     =  [torch.randn((4, 3, 32, 32)).clip(-1			,				1).to(lowerCAmelCase) for _ in range(4)]
         lowercase__     =  [torch.randn((4, 3, 32, 32)).to(lowerCAmelCase) for _ in range(4)]
         lowercase__     =  [torch.randint(0			,				10_00			,				(4,)).long().to(lowerCAmelCase) for _ in range(4)]

         # train with a DDPM scheduler
         lowercase__,	lowercase__     =  self.get_model_optimizer(resolution=32)
         model.train().to(lowerCAmelCase)
         for i in range(4):
               optimizer.zero_grad()
               lowercase__     =  ddpm_scheduler.add_noise(clean_images[i]			,				noise[i]			,				timesteps[i])
               lowercase__     =  model(lowerCAmelCase			,				timesteps[i]).sample
               lowercase__     =  torch.nn.functional.mse_loss(lowerCAmelCase			,				noise[i])
               loss.backward()
               optimizer.step()
         del model, optimizer

         # recreate the model and optimizer, and retry with DDIM
         lowercase__,	lowercase__     =  self.get_model_optimizer(resolution=32)
         model.train().to(lowerCAmelCase)
         for i in range(4):
               optimizer.zero_grad()
               lowercase__     =  ddim_scheduler.add_noise(clean_images[i]			,				noise[i]			,				timesteps[i])
               lowercase__     =  model(lowerCAmelCase			,				timesteps[i]).sample
               lowercase__     =  torch.nn.functional.mse_loss(lowerCAmelCase			,				noise[i])
               loss.backward()
               optimizer.step()
         del model, optimizer

         self.assertTrue(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=1E-5))
         self.assertTrue(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=1E-5))





import math
import sys

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

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  {'0': '0', '1': '1'}
      lowercase__,	lowercase__     =  '', ''
      lowercase__     =  len(A__	)

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

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

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

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

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  8
      try:
            with open(A__						,  'wb'	) as opened_file:
                  lowercase__     =  [
                      to_write[i : i + byte_length]
                      for i in range(0						,  len(A__	)						,  A__	)
                  ]

                  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(A__						,  2	).to_bytes(1						,  byteorder='big'	)	)
      except OSError:
            print('File not accessible'	)
            sys.exit()

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  0
      for letter in data_bits:
            if letter == "1":
                  break
            counter += 1

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

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  read_file_binary(A__	)
      lowercase__     =  remove_prefix(A__	)
      lowercase__     =  decompress_data(A__	)
      write_file_binary(A__						,  A__	)


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