

"""simple docstring"""





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"""







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


 def 					_lowercase (			self							:			str	):
    __lowercase								=						3_2
    __lowercase								=						embedder_hidden_size

    # image encoding components

    __lowercase								=						CLIPImageProcessor(crop_size=3_2,					size=3_2	)

    torch.manual_seed(0	)
    __lowercase								=						CLIPVisionModelWithProjection(
        CLIPVisionConfig(
            hidden_size=lowercase_,					projection_dim=lowercase_,					num_hidden_layers=5,					num_attention_heads=4,					image_size=3_2,					intermediate_size=3_7,					patch_size=1,					)	)

    # regular denoising components

    torch.manual_seed(0	)
    __lowercase								=						StableUnCLIPImageNormalizer(embedding_dim=lowercase_	)
    __lowercase								=						DDPMScheduler(beta_schedule="squaredcos_cap_v2"	)

    torch.manual_seed(0	)
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

    torch.manual_seed(0	)
    __lowercase								=						CLIPTextModel(
        CLIPTextConfig(
            bos_token_id=0,					eos_token_id=2,					hidden_size=lowercase_,					projection_dim=3_2,					intermediate_size=3_7,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)	)

    torch.manual_seed(0	)
    __lowercase								=						UNetaDConditionModel(
        sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),					up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),					block_out_channels=(3_2, 6_4),					attention_head_dim=(2, 4),					class_embed_type="projection",					projection_class_embeddings_input_dim=embedder_projection_dim * 2,					cross_attention_dim=lowercase_,					layers_per_block=1,					upcast_attention=lowercase_,					use_linear_projection=lowercase_,					)

    torch.manual_seed(0	)
    __lowercase								=						DDIMScheduler(
        beta_schedule="scaled_linear",					beta_start=0.00_085,					beta_end=0.012,					prediction_type="v_prediction",					set_alpha_to_one=lowercase_,					steps_offset=1,					)

    torch.manual_seed(0	)
    __lowercase								=						AutoencoderKL()

    __lowercase								=						{
        # 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 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Dict=0,					UpperCAmelCase__							:			Optional[int]=True	):




    if str(lowercase_	).startswith("mps"	):
       __lowercase								=						torch.manual_seed(lowercase_	)
    else:
       __lowercase								=						torch.Generator(device=lowercase_	).manual_seed(lowercase_	)

    __lowercase								=						floats_tensor((1, 3, 3_2, 3_2),					rng=random.Random(lowercase_	)	).to(lowercase_	)

    if pil_image:
       __lowercase								=						input_image * 0.5 + 0.5
       __lowercase								=						input_image.clamp(0,					1	)
       __lowercase								=						input_image.cpu().permute(0,					2,					3,					1	).float().numpy()
       __lowercase								=						DiffusionPipeline.numpy_to_pil(lowercase_	)[0]

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


 @skip_mps
 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						'cpu'  # ensure determinism for the device-dependent torch.Generator
    __lowercase								=						self.get_dummy_components()
    __lowercase								=						StableUnCLIPImgaImgPipeline(**lowercase_	)
    __lowercase								=						sd_pipe.to(lowercase_	)
    sd_pipe.set_progress_bar_config(disable=lowercase_	)

    __lowercase								=						self.get_dummy_inputs(lowercase_	)
    inputs.update({"image_embeds": None}	)
    __lowercase								=						sd_pipe(**lowercase_	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 3_2, 3_2, 3)
    __lowercase								=						np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]	)

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


 def 					_lowercase (			self							:			str	):
    __lowercase								=						torch_device in ['cpu', 'mps']

    self._test_attention_slicing_forward_pass(test_max_difference=lowercase_	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						torch_device in ['cpu', 'mps']

    self._test_inference_batch_single_identical(test_max_difference=lowercase_	)



 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available(),					reason="XFormers attention is only available with CUDA and `xformers` installed",					)
 def 					_lowercase (			self							:			int	):
    self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase_	)







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







 """simple docstring"""







 def 					_lowercase (			self							:			str	):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					_lowercase (			self							:			int	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"	)

    __lowercase								=						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"	)

    __lowercase								=						StableUnCLIPImgaImgPipeline.from_pretrained(
        "fusing/stable-unclip-2-1-l-img2img",					torch_dtype=torch.floataa	)
    pipe.to(lowercase_	)
    pipe.set_progress_bar_config(disable=lowercase_	)
    # 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()

    __lowercase								=						torch.Generator(device="cpu"	).manual_seed(0	)
    __lowercase								=						pipe(lowercase_,					"anime turle",					generator=lowercase_,					output_type="np"	)

    __lowercase								=						output.images[0]

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

    assert_mean_pixel_difference(lowercase_,					lowercase_	)


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

    __lowercase								=						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"	)

    __lowercase								=						StableUnCLIPImgaImgPipeline.from_pretrained(
        "fusing/stable-unclip-2-1-h-img2img",					torch_dtype=torch.floataa	)
    pipe.to(lowercase_	)
    pipe.set_progress_bar_config(disable=lowercase_	)
    # 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()

    __lowercase								=						torch.Generator(device="cpu"	).manual_seed(0	)
    __lowercase								=						pipe(lowercase_,					"anime turle",					generator=lowercase_,					output_type="np"	)

    __lowercase								=						output.images[0]

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

    assert_mean_pixel_difference(lowercase_,					lowercase_	)



 def 					_lowercase (			self							:			str	):
    __lowercase								=						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()

    __lowercase								=						StableUnCLIPImgaImgPipeline.from_pretrained(
        "fusing/stable-unclip-2-1-h-img2img",					torch_dtype=torch.floataa	)
    __lowercase								=						pipe.to(lowercase_	)
    pipe.set_progress_bar_config(disable=lowercase_	)
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    __lowercase								=						pipe(
        lowercase_,					"anime turtle",					num_inference_steps=2,					output_type="np",					)

    __lowercase								=						torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 1_0**9








def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       assert x is not None
       assert y is not None

       _lowerCamelCase       :		Dict		=len(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=len(SCREAMING_SNAKE_CASE__     )

       # declaring the array for storing the dp values
       _lowerCamelCase       :		Optional[int]		=[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

       for i in range(1		,			m + 1     ):
              for j in range(1		,			n + 1     ):
                     _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

                     _lowerCamelCase       :		Union[str, Any]		=max(l[i - 1][j]		,			l[i][j - 1]		,			l[i - 1][j - 1] + match     )

       _lowerCamelCase       :		Optional[int]		=''
       _lowerCamelCase	,     _lowerCamelCase       :		Dict		=m, n
       while i > 0 and j > 0:
              _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            _lowerCamelCase       :		Union[str, Any]		=x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
 lowerCamelCase											=       'AGGTAB'
 lowerCamelCase											=       'GXTXAYB'
 lowerCamelCase											=       4
 lowerCamelCase											=       'GTAB'

 lowerCamelCase					,				lowerCamelCase											=       longest_common_subsequence(a, b)
 print('len =', ln, ', sub-sequence =', subseq)
 import doctest

 doctest.testmod()


