






import math
import sys

def      _lowerCamelCase	(						__A    :							str )      ->					str:
    _UpperCAmelCase				:      List[Any]			  =				''''''
    try:
        with open(__A		,		'''rb''' ) as binary_file:
            _UpperCAmelCase				:      Optional[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      _lowerCamelCase	(						__A    :							str )      ->					str:
    _UpperCAmelCase				:      Optional[Any]			  =				{'''0''': '''0''', '''1''': '''1'''}
    _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				'''''', ''''''
    _UpperCAmelCase				:      Any			  =				len(__A )

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

        _UpperCAmelCase				:      Tuple			  =				lexicon[curr_string]
        result += last_match_id
        _UpperCAmelCase				:      List[Any]			  =				last_match_id + '''0'''

        if math.loga(__A ).is_integer():
            _UpperCAmelCase				:      Union[str, Any]			  =				{}
            for curr_key in list(__A ):
                _UpperCAmelCase				:      Dict			  =				lexicon.pop(__A )
            _UpperCAmelCase				:      List[Any]			  =				new_lex

        _UpperCAmelCase				:      str			  =				last_match_id + '''1'''
        index += 1
        _UpperCAmelCase				:      List[str]			  =				''''''
    return result

def      _lowerCamelCase	(						__A    :							str		,		__A    :							str )      ->					None:
    _UpperCAmelCase				:      Optional[int]			  =				8
    try:
        with open(__A		,		'''wb''' ) as opened_file:
            _UpperCAmelCase				:      Optional[int]			  =				[
                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[:-1]:
                opened_file.write(int(__A		,		2 ).to_bytes(1		,		byteorder='''big''' ) )
    except OSError:
        print('''File not accessible''' )
        sys.exit()

def      _lowerCamelCase	(						__A    :							str )      ->					str:
    _UpperCAmelCase				:      Optional[int]			  =				0
    for letter in data_bits:
        if letter == "1":
            break
        counter += 1

    _UpperCAmelCase				:      List[str]			  =				data_bits[counter:]
    _UpperCAmelCase				:      Any			  =				data_bits[counter + 1 :]
    return data_bits

def      _lowerCamelCase	(						__A    :							str		,		__A    :							str )      ->					None:
    _UpperCAmelCase				:      List[Any]			  =				read_file_binary(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				remove_prefix(__A )
    _UpperCAmelCase				:      List[str]			  =				decompress_data(__A )
    write_file_binary(__A		,		__A )


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












import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 							A_    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Dict =					StableDiffusionXLImgaImgPipeline
 _SCREAMING_SNAKE_CASE							:			Any =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 _SCREAMING_SNAKE_CASE							:			List[Any] =					PipelineTesterMixin.required_optional_params - {"latents"}
 _SCREAMING_SNAKE_CASE							:			Dict =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE							:			List[Any] =					IMAGE_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE							:			Dict =					IMAGE_TO_IMAGE_IMAGE_PARAMS


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				UNetaDConditionModel(
         block_out_channels=(32, 64)							,  layers_per_block=2							,  sample_size=32							,  in_channels=4							,  out_channels=4							,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,  attention_head_dim=(2, 4)							,  use_linear_projection=_A							,  addition_embed_type='''text_time'''							,  addition_time_embed_dim=8							,  transformer_layers_per_block=(1, 2)							,  projection_class_embeddings_input_dim=80							,  cross_attention_dim=64							,  )
     _UpperCAmelCase				:      int			  =				EulerDiscreteScheduler(
         beta_start=0.00085							,  beta_end=0.012							,  steps_offset=1							,  beta_schedule='''scaled_linear'''							,  timestep_spacing='''leading'''							,  )
     torch.manual_seed(0)
     _UpperCAmelCase				:      Optional[int]			  =				AutoencoderKL(
         block_out_channels=[32, 64]							,  in_channels=3							,  out_channels=3							,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,  latent_channels=4							,  sample_size=128							,  )
     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				CLIPTextConfig(
         bos_token_id=0							,  eos_token_id=2							,  hidden_size=32							,  intermediate_size=37							,  layer_norm_eps=1e-05							,  num_attention_heads=4							,  num_hidden_layers=5							,  pad_token_id=1							,  vocab_size=1000							,  hidden_act='''gelu'''							,  projection_dim=32							,  )
     _UpperCAmelCase				:      int			  =				CLIPTextModel(_A)
     _UpperCAmelCase				:      Tuple			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,  local_files_only=_A)

     _UpperCAmelCase				:      int			  =				CLIPTextModelWithProjection(_A)
     _UpperCAmelCase				:      Optional[int]			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,  local_files_only=_A)

     _UpperCAmelCase				:      Any			  =				{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''text_encoder_2''': text_encoder_a,
         '''tokenizer_2''': tokenizer_a,
         # "safety_checker": None,
         # "feature_extractor": None,
     }
     return components


 def  snake_case__				(	self							,  _A							,  _A=0) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				floats_tensor((1, 3, 32, 32)							,  rng=random.Random(_A)).to(_A)
     _UpperCAmelCase				:      List[Any]			  =				image / 2 + 0.5
     if str(_A).startswith('''mps'''):
         _UpperCAmelCase				:      Union[str, Any]			  =				torch.manual_seed(_A)
     else:
         _UpperCAmelCase				:      List[str]			  =				torch.Generator(device=_A).manual_seed(_A)
     _UpperCAmelCase				:      Tuple			  =				{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 5.0,
         '''output_type''': '''numpy''',
         '''strength''': 0.75,
     }
     return inputs


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_components()
     _UpperCAmelCase				:      Any			  =				StableDiffusionXLImgaImgPipeline(**_A)
     _UpperCAmelCase				:      List[str]			  =				sd_pipe.to(_A)
     sd_pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Tuple			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Any			  =				sd_pipe(**_A).images
     _UpperCAmelCase				:      Any			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)

     _UpperCAmelCase				:      Optional[Any]			  =				np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165])

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


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)


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

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_components()
     _UpperCAmelCase				:      str			  =				StableDiffusionXLImgaImgPipeline(**_A)
     _UpperCAmelCase				:      Any			  =				sd_pipe.to(_A)
     _UpperCAmelCase				:      Tuple			  =				sd_pipe.to(_A)
     sd_pipe.set_progress_bar_config(disable=_A)

     # forward without prompt embeds
     _UpperCAmelCase				:      str			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Optional[int]			  =				negative_prompt
     _UpperCAmelCase				:      Optional[int]			  =				3 * [inputs['''prompt''']]

     _UpperCAmelCase				:      Optional[Any]			  =				sd_pipe(**_A)
     _UpperCAmelCase				:      Any			  =				output.images[0, -3:, -3:, -1]

     # forward with prompt embeds
     _UpperCAmelCase				:      Dict			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Dict			  =				3 * [inputs.pop('''prompt''')]

     (
         (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , 
     )				:      str			  =				sd_pipe.encode_prompt(_A							,  negative_prompt=_A)

     _UpperCAmelCase				:      str			  =				sd_pipe(
         **_A							,  prompt_embeds=_A							,  negative_prompt_embeds=_A							,  pooled_prompt_embeds=_A							,  negative_pooled_prompt_embeds=_A							,  )
     _UpperCAmelCase				:      List[str]			  =				output.images[0, -3:, -3:, -1]

     # make sure that it's equal
     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4



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






 '''simple docstring'''




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

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A="cpu"							,  _A=torch.floataa							,  _A=0) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				torch.Generator(device=_A).manual_seed(_A)
     _UpperCAmelCase				:      Any			  =				np.random.RandomState(_A).standard_normal((1, 4, 64, 64))
     _UpperCAmelCase				:      Dict			  =				torch.from_numpy(_A).to(device=_A							,  dtype=_A)
     _UpperCAmelCase				:      str			  =				{
         '''prompt''': '''a photograph of an astronaut riding a horse''',
         '''latents''': latents,
         '''generator''': generator,
         '''num_inference_steps''': 3,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs


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

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''')
     pipe.to(_A)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      str			  =				self.get_inputs(_A)
     _UpperCAmelCase				:      Union[str, Any]			  =				pipe(**_A).images
     _UpperCAmelCase				:      Optional[int]			  =				image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 512, 512, 3)
     _UpperCAmelCase				:      int			  =				np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
     assert np.abs(image_slice - expected_slice).max() < 7e-3




