





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()



class        _lowerCAmelCase    (			UpperCAmelCase_							,    UpperCAmelCase_							,    UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       Optional[int]      					=StableUnCLIPImgaImgPipeline
  a_							:       Dict      					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  a_							:       int      					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  a_							:       List[str]      					=frozenset(
      []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  a_							:       int      					=frozenset([]							)







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Dict														=	32
    _snake_case   :				Tuple														=	embedder_hidden_size

    # image encoding components

    _snake_case   :				Tuple														=	CLIPImageProcessor(crop_size=32      ,				size=32							)

    torch.manual_seed(0							)
    _snake_case   :				Union[str, Any]														=	CLIPVisionModelWithProjection(
        CLIPVisionConfig(
            hidden_size=UpperCamelCase      ,				projection_dim=UpperCamelCase      ,				num_hidden_layers=5      ,				num_attention_heads=4      ,				image_size=32      ,				intermediate_size=37      ,				patch_size=1      ,				)							)

    # regular denoising components

    torch.manual_seed(0							)
    _snake_case   :				Any														=	StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase							)
    _snake_case   :				Optional[int]														=	DDPMScheduler(beta_schedule='squaredcos_cap_v2'							)

    torch.manual_seed(0							)
    _snake_case   :				int														=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

    torch.manual_seed(0							)
    _snake_case   :				List[str]														=	CLIPTextModel(
        CLIPTextConfig(
            bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=UpperCamelCase      ,				projection_dim=32      ,				intermediate_size=37      ,				layer_norm_eps=1e-0_5      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=10_00      ,				)							)

    torch.manual_seed(0							)
    _snake_case   :				str														=	UNetaDConditionModel(
        sample_size=32      ,				in_channels=4      ,				out_channels=4      ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')      ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')      ,				block_out_channels=(32, 64)      ,				attention_head_dim=(2, 4)      ,				class_embed_type='projection'      ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,				cross_attention_dim=UpperCamelCase      ,				layers_per_block=1      ,				upcast_attention=UpperCamelCase      ,				use_linear_projection=UpperCamelCase      ,				)

    torch.manual_seed(0							)
    _snake_case   :				Union[str, Any]														=	DDIMScheduler(
        beta_schedule='scaled_linear'      ,				beta_start=0.0_00_85      ,				beta_end=0.0_12      ,				prediction_type='v_prediction'      ,				set_alpha_to_one=UpperCamelCase      ,				steps_offset=1      ,				)

    torch.manual_seed(0							)
    _snake_case   :				List[str]														=	AutoencoderKL()

    _snake_case   :				int														=	{
        # image encoding components
        'feature_extractor': feature_extractor,
        'image_encoder': image_encoder.eval(),
        # image noising components
        'image_normalizer': image_normalizer.eval(),
        'image_noising_scheduler': image_noising_scheduler,
        # regular denoising components
        'tokenizer': tokenizer,
        'text_encoder': text_encoder.eval(),
        'unet': unet.eval(),
        'scheduler': scheduler,
        'vae': vae.eval(),
    }

    return components







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					List[Any]      ,				UpperCamelCase      :					Tuple=0      ,				UpperCamelCase      :					Dict=True							):


    '''simple docstring'''





    if str(UpperCamelCase							).startswith('mps'							):
      _snake_case   :				int														=	torch.manual_seed(UpperCamelCase							)
    else:
      _snake_case   :				str														=	torch.Generator(device=UpperCamelCase							).manual_seed(UpperCamelCase							)

    _snake_case   :				Any														=	floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCamelCase							)							).to(UpperCamelCase							)

    if pil_image:
      _snake_case   :				List[str]														=	input_image * 0.5 + 0.5
      _snake_case   :				Any														=	input_image.clamp(0      ,				1							)
      _snake_case   :				str														=	input_image.cpu().permute(0      ,				2      ,				3      ,				1							).float().numpy()
      _snake_case   :				Dict														=	DiffusionPipeline.numpy_to_pil(UpperCamelCase							)[0]

    return {
        "prompt": "An anime racoon running a marathon",
        "image": input_image,
        "generator": generator,
        "num_inference_steps": 2,
        "output_type": "np",
    }







  @skip_mps
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				str														=	'cpu'  # ensure determinism for the device-dependent torch.Generator
    _snake_case   :				int														=	self.get_dummy_components()
    _snake_case   :				str														=	StableUnCLIPImgaImgPipeline(**UpperCamelCase							)
    _snake_case   :				List[str]														=	sd_pipe.to(UpperCamelCase							)
    sd_pipe.set_progress_bar_config(disable=UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	self.get_dummy_inputs(UpperCamelCase							)
    inputs.update({'image_embeds': None}							)
    _snake_case   :				int														=	sd_pipe(**UpperCamelCase							).images
    _snake_case   :				List[str]														=	image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case   :				Union[str, Any]														=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]							)

    assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	torch_device in ['cpu', 'mps']

    self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	torch_device in ['cpu', 'mps']

    self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase							)







  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()      ,				reason='XFormers attention is only available with CUDA and `xformers` installed'      ,				)
  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase							)




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


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'							)

    _snake_case   :				Dict														=	load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'							)

    _snake_case   :				int														=	StableUnCLIPImgaImgPipeline.from_pretrained(
        'fusing/stable-unclip-2-1-l-img2img'      ,				torch_dtype=torch.floataa							)
    pipe.to(UpperCamelCase							)
    pipe.set_progress_bar_config(disable=UpperCamelCase							)
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    _snake_case   :				Optional[int]														=	torch.Generator(device='cpu'							).manual_seed(0							)
    _snake_case   :				Optional[int]														=	pipe(UpperCamelCase      ,				'anime turle'      ,				generator=UpperCamelCase      ,				output_type='np'							)

    _snake_case   :				List[Any]														=	output.images[0]

    assert image.shape == (7_68, 7_68, 3)

    assert_mean_pixel_difference(UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'							)

    _snake_case   :				Any														=	load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'							)

    _snake_case   :				Tuple														=	StableUnCLIPImgaImgPipeline.from_pretrained(
        'fusing/stable-unclip-2-1-h-img2img'      ,				torch_dtype=torch.floataa							)
    pipe.to(UpperCamelCase							)
    pipe.set_progress_bar_config(disable=UpperCamelCase							)
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    _snake_case   :				Optional[Any]														=	torch.Generator(device='cpu'							).manual_seed(0							)
    _snake_case   :				List[str]														=	pipe(UpperCamelCase      ,				'anime turle'      ,				generator=UpperCamelCase      ,				output_type='np'							)

    _snake_case   :				int														=	output.images[0]

    assert image.shape == (7_68, 7_68, 3)

    assert_mean_pixel_difference(UpperCamelCase      ,				UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'							)

    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    _snake_case   :				str														=	StableUnCLIPImgaImgPipeline.from_pretrained(
        'fusing/stable-unclip-2-1-h-img2img'      ,				torch_dtype=torch.floataa							)
    _snake_case   :				Optional[int]														=	pipe.to(UpperCamelCase							)
    pipe.set_progress_bar_config(disable=UpperCamelCase							)
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    _snake_case   :				Optional[int]														=	pipe(
        UpperCamelCase      ,				'anime turtle'      ,				num_inference_steps=2      ,				output_type='np'      ,				)

    _snake_case   :				Dict														=	torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 10**9











# Function to print upper half of diamond (pyramid)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		List[str]:
  for i in range(0    ,							lowerCAmelCase				):
    for _ in range(0    ,							n - i - 1				):  # printing spaces
      print(' '    ,							end=''				)
    for _ in range(0    ,							i + 1				):  # printing stars
      print('* '    ,							end=''				)
    print()


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  for i in range(lowerCAmelCase    ,							0    ,							-1				):
    for _ in range(lowerCAmelCase    ,							0    ,							-1				):  # printing stars
      print('* '    ,							end=''				)
    print()
    for _ in range(n - i + 1    ,							0    ,							-1				):  # printing spaces
      print(' '    ,							end=''				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		int:
  if n <= 0:
    print('       ...       ....        nothing printing :('				)
    return
  floyd(lowerCAmelCase				)  # upper half
  reverse_floyd(lowerCAmelCase				)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      lowerCAmelCase_						   =    1
      while K:
            lowerCAmelCase_						   =    int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            lowerCAmelCase_						   =    int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")




