


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Union[str, Any]			=      KandinskyVaaPriorPipeline
     A:		Dict			=      ["prompt"]
     A:		List[Any]			=      ["prompt", "negative_prompt"]
     A:		List[str]			=      [
         "num_images_per_prompt",
         "generator",
         "num_inference_steps",
         "latents",
         "negative_prompt",
         "guidance_scale",
         "output_type",
         "return_dict",
     ]
     A:		int			=      False


     @property
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Tuple:





      '''simple docstring'''






      return 32


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      int:





      '''simple docstring'''






      return 32


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      List[str]:





      '''simple docstring'''






      return self.time_input_dim


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[Any]:





      '''simple docstring'''






      return self.time_input_dim * 4


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Dict:





      '''simple docstring'''






      return 100


     @property
     def      UpperCAmelCase__	(      self							:     str			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
      return tokenizer


     @property
     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[Any]:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    List[str]          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=self.text_embedder_hidden_size							,	projection_dim=self.text_embedder_hidden_size							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      return CLIPTextModelWithProjection(lowerCamelCase__			)


     @property
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Any:





      '''simple docstring'''






      torch.manual_seed(0			)

      UpperCamelCase__					:    Union[str, Any]          =   {
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      UpperCamelCase__					:    List[Any]          =   PriorTransformer(**lowerCamelCase__			)
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      UpperCamelCase__					:    Union[str, Any]          =   nn.Parameter(torch.ones(model.clip_std.shape			)			)
      return model


     @property
     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Union[str, Any]          =   CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size							,	image_size=224							,	projection_dim=self.text_embedder_hidden_size							,	intermediate_size=37							,	num_attention_heads=4							,	num_channels=3							,	num_hidden_layers=5							,	patch_size=14							,	)

      UpperCamelCase__					:    List[Any]          =   CLIPVisionModelWithProjection(lowerCamelCase__			)
      return model


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   CLIPImageProcessor(
          crop_size=224							,	do_center_crop=lowerCamelCase__							,	do_normalize=lowerCamelCase__							,	do_resize=lowerCamelCase__							,	image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]							,	image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,	resample=3							,	size=224							,	)

      return image_processor


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.dummy_prior
      UpperCamelCase__					:    int          =   self.dummy_image_encoder
      UpperCamelCase__					:    List[str]          =   self.dummy_text_encoder
      UpperCamelCase__					:    Any          =   self.dummy_tokenizer
      UpperCamelCase__					:    Dict          =   self.dummy_image_processor

      UpperCamelCase__					:    Optional[Any]          =   UnCLIPScheduler(
          variance_type='''fixed_small_log'''							,	prediction_type='''sample'''							,	num_train_timesteps=1000							,	clip_sample=lowerCamelCase__							,	clip_sample_range=10.0							,	)

      UpperCamelCase__					:    int          =   {
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''scheduler''': scheduler,
          '''image_processor''': image_processor,
      }

      return components


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Any=0			)  ->      str:





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Dict          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Optional[int]          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   {
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   '''cpu'''

      UpperCamelCase__					:    Optional[int]          =   self.get_dummy_components()

      UpperCamelCase__					:    int          =   self.pipeline_class(**lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   pipe.to(lowerCamelCase__			)

      pipe.set_progress_bar_config(disable=lowerCamelCase__			)

      UpperCamelCase__					:    str          =   pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
      UpperCamelCase__					:    Any          =   output.image_embeds

      UpperCamelCase__					:    Optional[int]          =   pipe(
          **self.get_dummy_inputs(lowerCamelCase__			)							,	return_dict=lowerCamelCase__							,	)[0]

      UpperCamelCase__					:    List[str]          =   image[0, -10:]
      UpperCamelCase__					:    List[Any]          =   image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      UpperCamelCase__					:    Optional[Any]          =   np.array(
          [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

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


     @skip_mps
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   torch_device == '''cpu'''
      UpperCamelCase__					:    List[Any]          =   True
      UpperCamelCase__					:    str          =   False

      self._test_inference_batch_single_identical(
          test_max_difference=lowerCamelCase__							,	relax_max_difference=lowerCamelCase__							,	test_mean_pixel_difference=lowerCamelCase__							,	)


     @skip_mps
     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   torch_device == '''cpu'''
      UpperCamelCase__					:    str          =   False

      self._test_attention_slicing_forward_pass(
          test_max_difference=lowerCamelCase__							,	test_mean_pixel_difference=lowerCamelCase__							,	)






import math
import os
import sys

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   ''''''
 try:
  with open(SCREAMING_SNAKE_CASE    , '''rb'''     ) as binary_file:
   UpperCamelCase__					:    int          =   binary_file.read()
  for dat in data:
   UpperCamelCase__					:    Dict          =   F"{dat:08b}"
   result += curr_byte
  return result
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict[str, str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 lexicon.pop(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   last_match_id

 if math.loga(SCREAMING_SNAKE_CASE     ).is_integer():
  for curr_key in lexicon:
   UpperCamelCase__					:    Any          =   '''0''' + lexicon[curr_key]

 UpperCamelCase__					:    Dict          =   bin(SCREAMING_SNAKE_CASE     )[2:]

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   {'''0''': '''0''', '''1''': '''1'''}
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   '''''', ''''''
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )

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

  UpperCamelCase__					:    List[str]          =   lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
  index += 1
  UpperCamelCase__					:    Dict          =   ''''''

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

 if curr_string != "":
  UpperCamelCase__					:    Union[str, Any]          =   lexicon[curr_string]
  result += last_match_id

 return result

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



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

 return "0" * (length_length - 1) + file_length_binary + compressed

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   8
 try:
  with open(SCREAMING_SNAKE_CASE    , '''wb'''     ) as opened_file:
   UpperCamelCase__					:    Optional[Any]          =   [
       to_write[i : i + byte_length]
       for i in range(0    , len(SCREAMING_SNAKE_CASE     )    , SCREAMING_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:
    opened_file.write(int(SCREAMING_SNAKE_CASE    , 2     ).to_bytes(1    , byteorder='''big'''     )     )
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   read_file_binary(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   compress_data(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   add_file_length(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 write_file_binary(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


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


