



'''simple docstring'''





import math
import os
import sys
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= """"""
    try:
        with open(UpperCamelCase							,   """rb"""      ) as binary_file:
            lowerCAmelCase__			:  Optional[int]											= binary_file.read()
        for dat in data:
            lowerCAmelCase__			:  Dict											= f"""{dat:08b}"""
            result += curr_byte
        return result
    except OSError:
        print("""File not accessible"""      )
        sys.exit()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lexicon.pop(UpperCamelCase      )
    lowerCAmelCase__			:  Optional[Any]											= last_match_id

    if math.loga(UpperCamelCase      ).is_integer():
        for curr_key in lexicon:
            lowerCAmelCase__			:  int											= """0""" + lexicon[curr_key]

    lowerCAmelCase__			:  Tuple											= bin(UpperCamelCase      )[2:]
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= {"""0""": """0""", """1""": """1"""}
    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= """""", """"""
    lowerCAmelCase__			:  List[str]											= len(UpperCamelCase      )

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

        lowerCAmelCase__			:  Any											= lexicon[curr_string]
        result += last_match_id
        add_key_to_lexicon(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        index += 1
        lowerCAmelCase__			:  List[Any]											= """"""

    while curr_string != "" and curr_string not in lexicon:
        curr_string += "0"

    if curr_string != "":
        lowerCAmelCase__			:  List[str]											= lexicon[curr_string]
        result += last_match_id

    return result
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[int]											= os.path.getsize(UpperCamelCase      )
    lowerCAmelCase__			:  Any											= bin(UpperCamelCase      )[2:]
    lowerCAmelCase__			:  Tuple											= len(UpperCamelCase      )

    return "0" * (length_length - 1) + file_length_binary + compressed
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Dict											= 8
    try:
        with open(UpperCamelCase							,   """wb"""      ) as opened_file:
            lowerCAmelCase__			:  List[Any]											= [
                to_write[i : i + byte_length]
                for i in range(0							,   len(UpperCamelCase      )							,   UpperCamelCase      )
            ]

            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:
                opened_file.write(int(UpperCamelCase							,   2      ).to_bytes(1							,   byteorder="""big"""      )      )
    except OSError:
        print("""File not accessible"""      )
        sys.exit()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[Any]											= read_file_binary(UpperCamelCase      )
    lowerCAmelCase__			:  List[str]											= compress_data(UpperCamelCase      )
    lowerCAmelCase__			:  Optional[Any]											= add_file_length(UpperCamelCase							,   UpperCamelCase      )
    write_file_binary(UpperCamelCase							,   UpperCamelCase      )


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







'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


