



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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__				(lowercase__					,	unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase     :List[Any]						=     KandinskyInpaintPipeline
						__lowerCAmelCase     :Optional[int]						=     ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
						__lowerCAmelCase     :Union[str, Any]						=     [
						    "prompt",
						    "negative_prompt",
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						    "mask_image",
						]
						__lowerCAmelCase     :str						=     [
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "negative_prompt",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						__lowerCAmelCase     :Optional[Any]						=     False

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									return 3_2

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Any:
									"""simple docstring"""




									return 3_2

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									return self.time_input_dim

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									return self.time_input_dim * 4

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									return 1_0_0

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Any:
									"""simple docstring"""




									a__   :						Optional[int]						       =  XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
									return tokenizer

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									torch.manual_seed(0  )
									a__   :						Optional[int]						       =  MCLIPConfig(
									    numDims=self.cross_attention_dim					,   transformerDimensions=self.text_embedder_hidden_size					,   hidden_size=self.text_embedder_hidden_size					,   intermediate_size=3_7					,   num_attention_heads=4					,   num_hidden_layers=5					,   vocab_size=1_0_0_5					,   )

									a__   :						Union[str, Any]						       =  MultilingualCLIP(__lowerCamelCase  )
									a__   :						Any						       =  text_encoder.eval()

									return text_encoder

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      str:
									"""simple docstring"""




									torch.manual_seed(0  )

									a__   :						Dict						       =  {
									    "in_channels": 9,
									    # Out channels is double in channels because predicts mean and variance
									    "out_channels": 8,
									    "addition_embed_type": "text_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": "text_image_proj",
									    "cross_attention_dim": self.cross_attention_dim,
									    "attention_head_dim": 4,
									    "resnet_time_scale_shift": "scale_shift",
									    "class_embed_type": None,
									}

									a__   :						str						       =  UNetaDConditionModel(**__lowerCamelCase  )
									return model

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									return {
									    "block_out_channels": [3_2, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									torch.manual_seed(0  )
									a__   :						Any						       =  VQModel(**self.dummy_movq_kwargs  )
									return model

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						str						       =  self.dummy_text_encoder
									a__   :						str						       =  self.dummy_tokenizer
									a__   :						Optional[Any]						       =  self.dummy_unet
									a__   :						Union[str, Any]						       =  self.dummy_movq

									a__   :						List[str]						       =  DDIMScheduler(
									    num_train_timesteps=1_0_0_0					,   beta_schedule="""linear"""					,   beta_start=0.0_0_0_8_5					,   beta_end=0.0_1_2					,   clip_sample=__lowerCamelCase					,   set_alpha_to_one=__lowerCamelCase					,   steps_offset=1					,   prediction_type="""epsilon"""					,   thresholding=__lowerCamelCase					,   )

									a__   :						Any						       =  {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components

						def 							SCREAMING_SNAKE_CASE__(   self					,   __lowercase					,   __lowercase=0  )					->      List[str]:
									"""simple docstring"""




									a__   :						Optional[Any]						       =  floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
									a__   :						Tuple						       =  floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(seed + 1  )  ).to(__lowerCamelCase  )
									# create init_image
									a__   :						int						       =  floats_tensor((1, 3, 6_4, 6_4)					,   rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
									a__   :						Tuple						       =  image.cpu().permute(0					,   2					,   3					,   1  )[0]
									a__   :						str						       =  Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert("""RGB"""  ).resize((2_5_6, 2_5_6)  )
									# create mask
									a__   :						Optional[Any]						       =  np.ones((6_4, 6_4)					,   dtype=np.floataa  )
									a__   :						str						       =  0

									if str(__lowerCamelCase  ).startswith("""mps"""  ):
												a__   :						Any						       =  torch.manual_seed(__lowerCamelCase  )
									else:
												a__   :						Any						       =  torch.Generator(device=__lowerCamelCase  ).manual_seed(__lowerCamelCase  )
									a__   :						Optional[Any]						       =  {
									    "prompt": "horse",
									    "image": init_image,
									    "mask_image": mask,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 2,
									    "guidance_scale": 4.0,
									    "output_type": "np",
									}
									return inputs

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						List[str]						       =  "cpu"

									a__   :						Dict						       =  self.get_dummy_components()

									a__   :						str						       =  self.pipeline_class(**__lowerCamelCase  )
									a__   :						str						       =  pipe.to(__lowerCamelCase  )

									pipe.set_progress_bar_config(disable=__lowerCamelCase  )

									a__   :						List[str]						       =  pipe(**self.get_dummy_inputs(__lowerCamelCase  )  )
									a__   :						str						       =  output.images

									a__   :						str						       =  pipe(
									    **self.get_dummy_inputs(__lowerCamelCase  )					,   return_dict=__lowerCamelCase					,   )[0]

									a__   :						Dict						       =  image[0, -3:, -3:, -1]
									a__   :						Any						       =  image_from_tuple[0, -3:, -3:, -1]

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

									assert image.shape == (1, 6_4, 6_4, 3)

									a__   :						str						       =  np.array(
									    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]  )

									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 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									super().test_inference_batch_single_identical(expected_max_diff=3E-3  )




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







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Tuple:
									"""simple docstring"""




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Optional[int]:
									"""simple docstring"""




									a__   :						Optional[Any]						       =  load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""  )

									a__   :						Any						       =  load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
									a__   :						Optional[int]						       =  np.ones((7_6_8, 7_6_8)					,   dtype=np.floataa  )
									a__   :						str						       =  0

									a__   :						int						       =  "a hat"

									a__   :						Any						       =  KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""					,   torch_dtype=torch.floataa  )
									pipe_prior.to(__lowerCamelCase  )

									a__   :						Optional[Any]						       =  KandinskyInpaintPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-inpaint"""					,   torch_dtype=torch.floataa  )
									a__   :						Tuple						       =  pipeline.to(__lowerCamelCase  )
									pipeline.set_progress_bar_config(disable=__lowerCamelCase  )

									a__   :						str						       =  torch.Generator(device="""cpu"""  ).manual_seed(0  )
									a__   :						str						       =  pipe_prior(
									    __lowerCamelCase					,   generator=__lowerCamelCase					,   num_inference_steps=5					,   negative_prompt=""""""					,   ).to_tuple()

									a__   :						int						       =  pipeline(
									    __lowerCamelCase					,   image=__lowerCamelCase					,   mask_image=__lowerCamelCase					,   image_embeds=__lowerCamelCase					,   negative_image_embeds=__lowerCamelCase					,   generator=__lowerCamelCase					,   num_inference_steps=1_0_0					,   height=7_6_8					,   width=7_6_8					,   output_type="""np"""					,   )

									a__   :						Any						       =  output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(__lowerCamelCase					,   __lowerCamelCase  )








import math
import os
import sys

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    dict[str, str]    ,					_A			:    str    ,					_A			:    int    ,					_A			:    str						):

      """simple docstring"""




      lexicon.pop(_A						)
      lowerCamelCase__		:				List[Any]    =  last_match_id

      if math.loga(_A						).is_integer():
            for curr_key in lexicon:
                  lowerCamelCase__		:				Any    =  "0" + lexicon[curr_key]

      lowerCamelCase__		:				Tuple    =  bin(_A						)[2:]

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  {"0": "0", "1": "1"}
      lowerCamelCase__    ,   lowerCamelCase__		:				int    =  "", ""
      lowerCamelCase__		:				int    =  len(_A						)

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

            lowerCamelCase__		:				Optional[Any]    =  lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(_A    ,					_A    ,					_A    ,					_A						)
            index += 1
            lowerCamelCase__		:				Dict    =  ""

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

      if curr_string != "":
            lowerCamelCase__		:				int    =  lexicon[curr_string]
            result += last_match_id

      return result

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  os.path.getsize(_A						)
      lowerCamelCase__		:				Dict    =  bin(_A						)[2:]
      lowerCamelCase__		:				List[Any]    =  len(_A						)

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

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  8
      try:
            with open(_A    ,					"wb"						) as opened_file:
                  lowerCamelCase__		:				Any    =  [
                      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:
                        opened_file.write(int(_A    ,					2						).to_bytes(1    ,					byteorder="big"						)						)
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  read_file_binary(_A						)
      lowerCamelCase__		:				List[Any]    =  compress_data(_A						)
      lowerCamelCase__		:				str    =  add_file_length(_A    ,					_A						)
      write_file_binary(_A    ,					_A						)


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


