import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     KandinskyVaaInpaintPipeline
  lowercase_			=     ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  lowercase_			=     [
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  lowercase_			=     [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  lowercase_			=     False




  @property
  def 	__lowercase(      self     :	Optional[int]				)->       str:




       """simple docstring"""
       return 32




  @property
  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       return 32




  @property
  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       return self.time_input_dim




  @property
  def 	__lowercase(      self     :	Tuple				)->       Optional[int]:




       """simple docstring"""
       return self.time_input_dim * 4




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




       """simple docstring"""
       return 100




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




       """simple docstring"""
       torch.manual_seed(0				)

       SCREAMING_SNAKE_CASE__				:				Any			=		{
           'in_channels': 9,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'image',
           'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
           'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
           'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
           'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
           'layers_per_block': 1,
           'encoder_hid_dim': self.text_embedder_hidden_size,
           'encoder_hid_dim_type': 'image_proj',
           'cross_attention_dim': self.cross_attention_dim,
           'attention_head_dim': 4,
           'resnet_time_scale_shift': 'scale_shift',
           'class_embed_type': None,
       }

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		UNetaDConditionModel(**a_				)
       return model




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




       """simple docstring"""
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }




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




       """simple docstring"""
       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		VQModel(**self.dummy_movq_kwargs				)
       return model




  def 	__lowercase(      self     :	List[str]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.dummy_unet
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.dummy_movq

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		DDIMScheduler(
           num_train_timesteps=1000     , beta_schedule='linear'     , beta_start=0.0_0085     , beta_end=0.012     , clip_sample=a_     , set_alpha_to_one=a_     , steps_offset=1     , prediction_type='epsilon'     , thresholding=a_     , )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components




  def 	__lowercase(      self     :	List[str]     , a_     :	Any     , a_     :	Dict=0				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(a_				)				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(seed + 1				)				).to(
           a_				)
       # create init_image
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		floats_tensor((1, 3, 64, 64)     , rng=random.Random(a_				)				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image.cpu().permute(0     , 2     , 3     , 1				)[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		Image.fromarray(np.uinta(a_				)				).convert('RGB'				).resize((256, 256)				)
       # create mask
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.ones((64, 64)     , dtype=np.floataa				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		0

       if str(a_				).startswith('mps'				):
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.manual_seed(a_				)
       else:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.Generator(device=a_				).manual_seed(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{
           'image': init_image,
           'mask_image': mask,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 2,
           'guidance_scale': 4.0,
           'output_type': 'np',
       }
       return inputs




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		'cpu'

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		self.pipeline_class(**a_				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		pipe.to(a_				)

       pipe.set_progress_bar_config(disable=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		pipe(**self.get_dummy_inputs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				int			=		output.images

       SCREAMING_SNAKE_CASE__				:				Any			=		pipe(
           **self.get_dummy_inputs(a_				)     , return_dict=a_     , )[0]

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image_from_tuple[0, -3:, -3:, -1]

       print(F'''image.shape {image.shape}'''				)

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array(
           [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]				)

       assert (
           np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




  def 	__lowercase(      self     :	List[str]				)->       str:




       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




@slow
@require_torch_gpu
class       snake_case					(      unittest.TestCase						):




  def 	__lowercase(      self     :	str				)->       int:




       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




  def 	__lowercase(      self     :	Tuple				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
       SCREAMING_SNAKE_CASE__				:				Any			=		np.ones((768, 768)     , dtype=np.floataa				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		0

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'a hat'

       SCREAMING_SNAKE_CASE__				:				int			=		KandinskyVaaPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'     , torch_dtype=torch.floataa				)
       pipe_prior.to(a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		KandinskyVaaInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-decoder-inpaint'     , torch_dtype=torch.floataa				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pipeline.to(a_				)
       pipeline.set_progress_bar_config(disable=a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.Generator(device='cpu'				).manual_seed(0				)
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		pipe_prior(
           a_     , generator=a_     , num_inference_steps=5     , negative_prompt=''     , ).to_tuple()

       SCREAMING_SNAKE_CASE__				:				str			=		pipeline(
           image=a_     , mask_image=a_     , image_embeds=a_     , negative_image_embeds=a_     , generator=a_     , num_inference_steps=100     , height=768     , width=768     , output_type='np'     , )

       SCREAMING_SNAKE_CASE__				:				Dict			=		output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a_     , a_				)




import math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

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

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   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     _a				(      lowercase__  :			str ):







     '''simple docstring'''


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

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


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



