




import math
import sys







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     ''''''
     try:
          with open(_lowercase					,		'''rb''' ) as binary_file:
               UpperCAmelCase_     :				Dict  =     binary_file.read()
          for dat in data:
               UpperCAmelCase_     :				Union[str, Any]  =     f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('''File not accessible''' )
          sys.exit()







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     {'''0''': '''0''', '''1''': '''1'''}
     UpperCAmelCase_,       UpperCAmelCase_     :				str  =     '''''', ''''''
     UpperCAmelCase_     :				Any  =     len(_lowercase )

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

          UpperCAmelCase_     :				Any  =     lexicon[curr_string]
          result += last_match_id
          UpperCAmelCase_     :				Any  =     last_match_id + '''0'''

          if math.loga(_lowercase ).is_integer():
               UpperCAmelCase_     :				int  =     {}
               for curr_key in list(_lowercase ):
                    UpperCAmelCase_     :				List[Any]  =     lexicon.pop(_lowercase )
               UpperCAmelCase_     :				List[Any]  =     new_lex

          UpperCAmelCase_     :				Any  =     last_match_id + '''1'''
          index += 1
          UpperCAmelCase_     :				List[Any]  =     ''''''
     return result







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     8
     try:
          with open(_lowercase					,		'''wb''' ) as opened_file:
               UpperCAmelCase_     :				List[str]  =     [
                   to_write[i : i + byte_length]
                   for i in range(0					,		len(_lowercase )					,		_lowercase )
               ]

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







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     UpperCAmelCase_     :				Optional[int]  =     data_bits[counter:]
     UpperCAmelCase_     :				Dict  =     data_bits[counter + 1 :]
     return data_bits







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     read_file_binary(_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     remove_prefix(_lowercase )
     UpperCAmelCase_     :				Tuple  =     decompress_data(_lowercase )
     write_file_binary(_lowercase					,		_lowercase )


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


'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 	lowerCAmelCase  (   unittest.TestCase ):







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            torch.manual_seed(0   )
            __lowercase      		=UNetaDModel(
                block_out_channels=(32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')    ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')    ,   )
            return model







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.dummy_uncond_unet
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'   ).images

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'    ,   return_dict=__lowercase   )[0]

            __lowercase      		=image[0, -3:, -3:, -1]
            __lowercase      		=image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		='google/ddpm-cifar10-32'

            __lowercase      		=UNetaDModel.from_pretrained(__lowercase   )
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   output_type='numpy'   ).images

            __lowercase      		=image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2


