
"""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 		a__						(      UpperCAmelCase_,			UpperCAmelCase_,			UpperCAmelCase_,			unittest.TestCase						):
   __lowerCAmelCase            =				StableUnCLIPImgaImgPipeline
   __lowerCAmelCase            =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   __lowerCAmelCase            =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __lowerCAmelCase            =				frozenset(
       []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   __lowerCAmelCase            =				frozenset([]						)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							32
          lowercase    :				Optional[int]       		=							embedder_hidden_size

          # image encoding components

          lowercase    :				Union[str, Any]       		=							CLIPImageProcessor(crop_size=32	,    size=32				)

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

          # regular denoising components

          torch.manual_seed(0				)
          lowercase    :				str       		=							StableUnCLIPImageNormalizer(embedding_dim=__lowercase				)
          lowercase    :				Optional[int]       		=							DDPMScheduler(beta_schedule="squaredcos_cap_v2"				)

          torch.manual_seed(0				)
          lowercase    :				Union[str, Any]       		=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

          torch.manual_seed(0				)
          lowercase    :				Dict       		=							CLIPTextModel(
              CLIPTextConfig(
                  bos_token_id=0	,    eos_token_id=2	,    hidden_size=__lowercase	,    projection_dim=32	,    intermediate_size=37	,    layer_norm_eps=1E-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1_000	,    )				)

          torch.manual_seed(0				)
          lowercase    :				List[Any]       		=							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=__lowercase	,    layers_per_block=1	,    upcast_attention=__lowercase	,    use_linear_projection=__lowercase	,    )

          torch.manual_seed(0				)
          lowercase    :				Tuple       		=							DDIMScheduler(
              beta_schedule="scaled_linear"	,    beta_start=0.0_0_0_8_5	,    beta_end=0.0_1_2	,    prediction_type="v_prediction"	,    set_alpha_to_one=__lowercase	,    steps_offset=1	,    )

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

          lowercase    :				Tuple       		=							{
              # 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 			__magic_name__			(			self	,    _a	,    _a=0	,    _a=True				):
          if str(__lowercase				).startswith("mps"				):
                 lowercase    :				Union[str, Any]       		=							torch.manual_seed(__lowercase				)
          else:
                 lowercase    :				int       		=							torch.Generator(device=__lowercase				).manual_seed(__lowercase				)

          lowercase    :				int       		=							floats_tensor((1, 3, 32, 32)	,    rng=random.Random(__lowercase				)				).to(__lowercase				)

          if pil_image:
                 lowercase    :				List[Any]       		=							input_image * 0.5 + 0.5
                 lowercase    :				Optional[Any]       		=							input_image.clamp(0	,    1				)
                 lowercase    :				int       		=							input_image.cpu().permute(0	,    2	,    3	,    1				).float().numpy()
                 lowercase    :				Optional[Any]       		=							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 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          lowercase    :				Tuple       		=							self.get_dummy_components()
          lowercase    :				Any       		=							StableUnCLIPImgaImgPipeline(**__lowercase				)
          lowercase    :				Optional[Any]       		=							sd_pipe.to(__lowercase				)
          sd_pipe.set_progress_bar_config(disable=__lowercase				)

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

          assert image.shape == (1, 32, 32, 3)
          lowercase    :				List[Any]       		=							np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							torch_device in ['''cpu''', '''mps''']

          self._test_attention_slicing_forward_pass(test_max_difference=__lowercase				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							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 			__magic_name__			(			self				):
          self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase				)


@slow
@require_torch_gpu
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"				)

          lowercase    :				Any       		=							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    :				List[Any]       		=							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    :				int       		=							torch.Generator(device="cpu"				).manual_seed(0				)
          lowercase    :				Dict       		=							pipe(__lowercase	,    "anime turle"	,    generator=__lowercase	,    output_type="np"				)

          lowercase    :				Dict       		=							output.images[0]

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

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

          lowercase    :				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"				)

          lowercase    :				Any       		=							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    :				int       		=							torch.Generator(device="cpu"				).manual_seed(0				)
          lowercase    :				Optional[int]       		=							pipe(__lowercase	,    "anime turle"	,    generator=__lowercase	,    output_type="np"				)

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

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

          assert_mean_pixel_difference(__lowercase	,    __lowercase				)

   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							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    :				List[Any]       		=							StableUnCLIPImgaImgPipeline.from_pretrained(
              "fusing/stable-unclip-2-1-h-img2img"	,    torch_dtype=torch.floataa				)
          lowercase    :				Union[str, Any]       		=							pipe.to(__lowercase				)
          pipe.set_progress_bar_config(disable=__lowercase				)
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

          lowercase    :				Optional[Any]       		=							pipe(
              __lowercase	,    "anime turtle"	,    num_inference_steps=2	,    output_type="np"	,    )

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		[0 for i in range(len(SCREAMING_SNAKE_CASE	)	)]

    # initialize interval's left pointer and right pointer
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		0, 0

    for i in range(1  ,			len(SCREAMING_SNAKE_CASE	)	):
        # case when current index is inside the interval
        if i <= right_pointer:
            __UpperCamelCase     :Union[str, Any]			  =		min(right_pointer - i + 1  ,			z_result[i - left_pointer]	)
            __UpperCamelCase     :Tuple			  =		min_edge

        while go_next(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		i, i + z_result[i] - 1

    return z_result



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return i + z_result[i] < len(SCREAMING_SNAKE_CASE	) and s[z_result[i]] == s[i + z_result[i]]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    __UpperCamelCase     :Tuple			  =		z_function(pattern + input_str	)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(SCREAMING_SNAKE_CASE	):
            answer += 1

    return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()





