






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 				_UpperCamelCase      (a_				,		a_				,		a_				,		unittest.TestCase      ):
      snake_case_	=				StableUnCLIPImgaImgPipeline
      snake_case_	=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS
      snake_case_	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      snake_case_	=				frozenset(
          []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      snake_case_	=				frozenset([]      )


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       3_2
           __lowerCAmelCase             =       embedder_hidden_size

           # image encoding components

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

           torch.manual_seed(0						)
           __lowerCAmelCase             =       CLIPVisionModelWithProjection(
               CLIPVisionConfig(
                   hidden_size=__UpperCamelCase       ,    projection_dim=__UpperCamelCase       ,    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						)
           __lowerCAmelCase             =       StableUnCLIPImageNormalizer(embedding_dim=__UpperCamelCase						)
           __lowerCAmelCase             =       DDPMScheduler(beta_schedule="squaredcos_cap_v2"						)

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

           torch.manual_seed(0						)
           __lowerCAmelCase             =       CLIPTextModel(
               CLIPTextConfig(
                   bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=__UpperCamelCase       ,    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						)
           __lowerCAmelCase             =       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=__UpperCamelCase       ,    layers_per_block=1       ,    upcast_attention=__UpperCamelCase       ,    use_linear_projection=__UpperCamelCase       ,    )

           torch.manual_seed(0						)
           __lowerCAmelCase             =       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=__UpperCamelCase       ,    steps_offset=1       ,    )

           torch.manual_seed(0						)
           __lowerCAmelCase             =       AutoencoderKL()

           __lowerCAmelCase             =       {
               # 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       ,    __UpperCamelCase       ,    __UpperCamelCase=0       ,    __UpperCamelCase=True						)->						Tuple:

           if str(__UpperCamelCase						).startswith("mps"						):
                __lowerCAmelCase             =       torch.manual_seed(__UpperCamelCase						)
           else:
                __lowerCAmelCase             =       torch.Generator(device=__UpperCamelCase						).manual_seed(__UpperCamelCase						)

           __lowerCAmelCase             =       floats_tensor((1, 3, 3_2, 3_2)       ,    rng=random.Random(__UpperCamelCase						)						).to(__UpperCamelCase						)

           if pil_image:
                __lowerCAmelCase             =       input_image * 0.5 + 0.5
                __lowerCAmelCase             =       input_image.clamp(0       ,    1						)
                __lowerCAmelCase             =       input_image.cpu().permute(0       ,    2       ,    3       ,    1						).float().numpy()
                __lowerCAmelCase             =       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						)->						Optional[Any]:
           __lowerCAmelCase             =       "cpu"  # ensure determinism for the device-dependent torch.Generator
           __lowerCAmelCase             =       self.get_dummy_components()
           __lowerCAmelCase             =       StableUnCLIPImgaImgPipeline(**__UpperCamelCase						)
           __lowerCAmelCase             =       sd_pipe.to(__UpperCamelCase						)
           sd_pipe.set_progress_bar_config(disable=__UpperCamelCase						)

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

           assert image.shape == (1, 3_2, 3_2, 3)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       torch_device in ["cpu", "mps"]

           self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       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						)->						Optional[int]:
           self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__UpperCamelCase						)



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


      def 			__UpperCAmelCase		(						self						)->						str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"						)

           __lowerCAmelCase             =       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"						)

           __lowerCAmelCase             =       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()

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

           __lowerCAmelCase             =       output.images[0]

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

           assert_mean_pixel_difference(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"						)

           __lowerCAmelCase             =       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"						)

           __lowerCAmelCase             =       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()

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

           __lowerCAmelCase             =       output.images[0]

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

           assert_mean_pixel_difference(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       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()

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

           __lowerCAmelCase             =       pipe(
               __UpperCamelCase       ,    "anime turtle"       ,    num_inference_steps=2       ,    output_type="np"       ,    )

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











import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      Optional[Any]      ,					**lowerCAmelCase	:      List[Any])						->					None:




         """simple docstring"""

         warnings.warn(
             'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use GLPNImageProcessor instead.'      ,					lowerCAmelCase      ,					)
         super().__init__(*lowerCAmelCase      ,					**lowerCAmelCase)
