"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 					UpperCAmelCase_     (   _a			,							_a			,							_a			,							unittest.TestCase):
      lowerCamelCase__		:   Any			  =  StableDiffusionImageVariationPipeline
      lowerCamelCase__		:   Tuple			  =  IMAGE_VARIATION_PARAMS
      lowerCamelCase__		:   Optional[Any]			  =  IMAGE_VARIATION_BATCH_PARAMS
      lowerCamelCase__		:   Union[str, Any]			  =  frozenset([])
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCamelCase__		:   Optional[Any]			  =  frozenset([])


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             torch.manual_seed(0							)
             lowercase__      :		Dict          =     UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=4		,      out_channels=4		,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,      cross_attention_dim=3_2		,      )
             lowercase__      :		Dict          =     PNDMScheduler(skip_prk_steps=a							)
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     AutoencoderKL(
                 block_out_channels=[3_2, 6_4]		,      in_channels=3		,      out_channels=3		,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,      latent_channels=4		,      )
             torch.manual_seed(0							)
             lowercase__      :		str          =     CLIPVisionConfig(
                 hidden_size=3_2		,      projection_dim=3_2		,      intermediate_size=3_7		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      image_size=3_2		,      patch_size=4		,      )
             lowercase__      :		int          =     CLIPVisionModelWithProjection(a							)
             lowercase__      :		Optional[Any]          =     CLIPImageProcessor(crop_size=3_2		,      size=3_2							)

             lowercase__      :		Dict          =     {
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'image_encoder': image_encoder,
                 'feature_extractor': feature_extractor,
                 'safety_checker': None,
             }
             return components


      def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Optional[Any]:
             lowercase__      :		int          =     floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(a							)							)
             lowercase__      :		Optional[Any]          =     image.cpu().permute(0		,      2		,      3		,      1							)[0]
             lowercase__      :		int          =     Image.fromarray(np.uinta(a							)							).convert('RGB'							).resize((3_2, 3_2)							)
             if str(a							).startswith('mps'							):
                    lowercase__      :		Union[str, Any]          =     torch.manual_seed(a							)
             else:
                    lowercase__      :		Union[str, Any]          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		Union[str, Any]          =     {
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Union[str, Any]          =     'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__      :		int          =     self.get_dummy_components()
             lowercase__      :		str          =     StableDiffusionImageVariationPipeline(**a							)
             lowercase__      :		Optional[Any]          =     sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Dict          =     self.get_dummy_inputs(a							)
             lowercase__      :		List[str]          =     sd_pipe(**a							).images
             lowercase__      :		Union[str, Any]          =     image[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)
             lowercase__      :		Union[str, Any]          =     np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]							)

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


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		int          =     'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__      :		str          =     self.get_dummy_components()
             lowercase__      :		Optional[int]          =     StableDiffusionImageVariationPipeline(**a							)
             lowercase__      :		Tuple          =     sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Optional[int]          =     self.get_dummy_inputs(a							)
             lowercase__      :		Optional[int]          =     2 * [inputs['image']]
             lowercase__      :		Optional[Any]          =     sd_pipe(**a							)

             lowercase__      :		Optional[int]          =     output.images

             lowercase__      :		int          =     image[-1, -3:, -3:, -1]

             assert image.shape == (2, 6_4, 6_4, 3)
             lowercase__      :		str          =     np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]							)

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


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





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


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 		_UpperCAmelCase	(       self		,      a		,      a="cpu"		,      a=torch.floataa		,      a=0							)	-> int:
             lowercase__      :		List[str]          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		Tuple          =     load_image(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/input_image_vermeer.png'							)
             lowercase__      :		Union[str, Any]          =     np.random.RandomState(a							).standard_normal((1, 4, 6_4, 6_4)							)
             lowercase__      :		Any          =     torch.from_numpy(a							).to(device=a		,      dtype=a							)
             lowercase__      :		Any          =     {
                 'image': init_image,
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		int          =     StableDiffusionImageVariationPipeline.from_pretrained(
                 'lambdalabs/sd-image-variations-diffusers'		,      safety_checker=a							)
             lowercase__      :		List[str]          =     sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Tuple          =     self.get_inputs(a							)
             lowercase__      :		List[Any]          =     sd_pipe(**a							).images
             lowercase__      :		List[str]          =     image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             lowercase__      :		List[str]          =     np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]							)
             assert np.abs(image_slice - expected_slice							).max() < 6e-3


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		str          =     0

             def callback_fn(a		,      a		,      a							) -> None:
                    lowercase__      :		Any          =     True
                    nonlocal number_of_steps
                    number_of_steps += 1
                    if step == 1:
                           lowercase__      :		str          =     latents.detach().cpu().numpy()
                           assert latents.shape == (1, 4, 6_4, 6_4)
                           lowercase__      :		Tuple          =     latents[0, -3:, -3:, -1]
                           lowercase__      :		Union[str, Any]          =     np.array(
                               [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]							)

                           assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
                    elif step == 2:
                           lowercase__      :		Optional[Any]          =     latents.detach().cpu().numpy()
                           assert latents.shape == (1, 4, 6_4, 6_4)
                           lowercase__      :		str          =     latents[0, -3:, -3:, -1]
                           lowercase__      :		int          =     np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]							)

                           assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

             lowercase__      :		Dict          =     False

             lowercase__      :		Tuple          =     StableDiffusionImageVariationPipeline.from_pretrained(
                 'fusing/sd-image-variations-diffusers'		,      safety_checker=a		,      torch_dtype=torch.floataa		,      )
             pipe.to(a							)
             pipe.set_progress_bar_config(disable=a							)
             pipe.enable_attention_slicing()

             lowercase__      :		Any          =     self.get_inputs(a		,      dtype=torch.floataa							)
             pipe(**a		,      callback=a		,      callback_steps=1							)
             assert callback_fn.has_been_called
             assert number_of_steps == inputs["num_inference_steps"]


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             lowercase__      :		int          =     'fusing/sd-image-variations-diffusers'
             lowercase__      :		Union[str, Any]          =     StableDiffusionImageVariationPipeline.from_pretrained(
                 a		,      safety_checker=a		,      torch_dtype=torch.floataa							)
             lowercase__      :		List[Any]          =     pipe.to(a							)
             pipe.set_progress_bar_config(disable=a							)
             pipe.enable_attention_slicing(1							)
             pipe.enable_sequential_cpu_offload()

             lowercase__      :		Optional[int]          =     self.get_inputs(a		,      dtype=torch.floataa							)
             lowercase__      :		int          =     pipe(**a							)

             lowercase__      :		Union[str, Any]          =     torch.cuda.max_memory_allocated()
             # make sure that less than 2.6 GB is allocated
             assert mem_bytes < 2.6 * 1_0**9





@nightly
@require_torch_gpu
class 					UpperCAmelCase_     (   unittest.TestCase):


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 		_UpperCAmelCase	(       self		,      a		,      a="cpu"		,      a=torch.floataa		,      a=0							)	-> Optional[Any]:
             lowercase__      :		Tuple          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		List[str]          =     load_image(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/input_image_vermeer.png'							)
             lowercase__      :		Dict          =     np.random.RandomState(a							).standard_normal((1, 4, 6_4, 6_4)							)
             lowercase__      :		Any          =     torch.from_numpy(a							).to(device=a		,      dtype=a							)
             lowercase__      :		Optional[int]          =     {
                 'image': init_image,
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 5_0,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Union[str, Any]          =     StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'							)
             sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		List[str]          =     self.get_inputs(a							)
             lowercase__      :		Any          =     sd_pipe(**a							).images[0]

             lowercase__      :		Optional[Any]          =     load_numpy(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'							)
             lowercase__      :		Dict          =     np.abs(expected_image - image							).max()
             assert max_diff < 1e-3


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Any          =     StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'							)
             lowercase__      :		List[Any]          =     DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config							)
             sd_pipe.to(a							)
             sd_pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		List[str]          =     self.get_inputs(a							)
             lowercase__      :		int          =     2_5
             lowercase__      :		Dict          =     sd_pipe(**a							).images[0]

             lowercase__      :		Optional[Any]          =     load_numpy(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'							)
             lowercase__      :		List[str]          =     np.abs(expected_image - image							).max()
             assert max_diff < 1e-3


"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase       :						int     =   {
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						Tuple     =   ["RetriBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase       :						int     =   [
           "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RetriBertModel",
           "RetriBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
       from .tokenization_retribert import RetriBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_retribert_fast import RetriBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_retribert import (
                  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RetriBertModel,
                  RetriBertPreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase       :						Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

