


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 A						(     UpperCamelCase_					,							UpperCamelCase_					,							UpperCamelCase_					,							unittest.TestCase  ):
      UpperCamelCase__							:						Dict   =StableDiffusionImageVariationPipeline
      UpperCamelCase__							:						Any   =IMAGE_VARIATION_PARAMS
      UpperCamelCase__							:						Any   =IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase__							:						int   =frozenset([]  )
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCamelCase__							:						Union[str, Any]   =frozenset([]  )






      def  lowerCamelCase (      self		:  Any				)      ->		Any:
             """simple docstring"""




             torch.manual_seed(0				)
             _lowerCamelCase :	Optional[Any]						=UNetaDConditionModel(
                 block_out_channels=(32, 64)    ,     layers_per_block=2    ,     sample_size=32    ,     in_channels=4    ,     out_channels=4    ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,     cross_attention_dim=32    ,     )
             _lowerCamelCase :	Union[str, Any]						=PNDMScheduler(skip_prk_steps=lowercase_				)
             torch.manual_seed(0				)
             _lowerCamelCase :	List[Any]						=AutoencoderKL(
                 block_out_channels=[32, 64]    ,     in_channels=3    ,     out_channels=3    ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,     latent_channels=4    ,     )
             torch.manual_seed(0				)
             _lowerCamelCase :	int						=CLIPVisionConfig(
                 hidden_size=32    ,     projection_dim=32    ,     intermediate_size=37    ,     layer_norm_eps=1E-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     image_size=32    ,     patch_size=4    ,     )
             _lowerCamelCase :	int						=CLIPVisionModelWithProjection(lowercase_				)
             _lowerCamelCase :	str						=CLIPImageProcessor(crop_size=32    ,     size=32				)

             _lowerCamelCase :	Optional[Any]						={
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'image_encoder': image_encoder,
                 'feature_extractor': feature_extractor,
                 'safety_checker': None,
             }
             return components






      def  lowerCamelCase (      self		:  Tuple    ,     lowercase_		:  Any    ,     lowercase_		:  List[str]=0				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	Optional[Any]						=floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(lowercase_				)				)
             _lowerCamelCase :	str						=image.cpu().permute(0    ,     2    ,     3    ,     1				)[0]
             _lowerCamelCase :	Any						=Image.fromarray(np.uinta(lowercase_				)				).convert('RGB'				).resize((32, 32)				)
             if str(lowercase_				).startswith('mps'				):
                    _lowerCamelCase :	List[str]						=torch.manual_seed(lowercase_				)
             else:
                    _lowerCamelCase :	Union[str, Any]						=torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
             _lowerCamelCase :	int						={
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }
             return inputs






      def  lowerCamelCase (      self		:  Any				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Any						='cpu'  # ensure determinism for the device-dependent torch.Generator
             _lowerCamelCase :	Optional[int]						=self.get_dummy_components()
             _lowerCamelCase :	str						=StableDiffusionImageVariationPipeline(**lowercase_				)
             _lowerCamelCase :	str						=sd_pipe.to(lowercase_				)
             sd_pipe.set_progress_bar_config(disable=lowercase_				)

             _lowerCamelCase :	Union[str, Any]						=self.get_dummy_inputs(lowercase_				)
             _lowerCamelCase :	str						=sd_pipe(**lowercase_				).images
             _lowerCamelCase :	Any						=image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             _lowerCamelCase :	Optional[Any]						=np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]				)

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






      def  lowerCamelCase (      self		:  Tuple				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						='cpu'  # ensure determinism for the device-dependent torch.Generator
             _lowerCamelCase :	Optional[int]						=self.get_dummy_components()
             _lowerCamelCase :	Optional[Any]						=StableDiffusionImageVariationPipeline(**lowercase_				)
             _lowerCamelCase :	List[Any]						=sd_pipe.to(lowercase_				)
             sd_pipe.set_progress_bar_config(disable=lowercase_				)

             _lowerCamelCase :	str						=self.get_dummy_inputs(lowercase_				)
             _lowerCamelCase :	List[Any]						=2 * [inputs['image']]
             _lowerCamelCase :	Dict						=sd_pipe(**lowercase_				)

             _lowerCamelCase :	Tuple						=output.images

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

             assert image.shape == (2, 64, 64, 3)
             _lowerCamelCase :	Optional[Any]						=np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]				)

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






      def  lowerCamelCase (      self		:  Dict				)      ->		Optional[Any]:
             """simple docstring"""




             super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		Dict:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[str]    ,     lowercase_		:  str="cpu"    ,     lowercase_		:  List[Any]=torch.floataa    ,     lowercase_		:  str=0				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Dict						=torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
             _lowerCamelCase :	Any						=load_image(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/input_image_vermeer.png'				)
             _lowerCamelCase :	str						=np.random.RandomState(lowercase_				).standard_normal((1, 4, 64, 64)				)
             _lowerCamelCase :	str						=torch.from_numpy(lowercase_				).to(device=lowercase_    ,     dtype=lowercase_				)
             _lowerCamelCase :	List[str]						={
                 'image': init_image,
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs






      def  lowerCamelCase (      self		:  Dict				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Dict						=StableDiffusionImageVariationPipeline.from_pretrained(
                 'lambdalabs/sd-image-variations-diffusers'    ,     safety_checker=lowercase_				)
             _lowerCamelCase :	Optional[Any]						=sd_pipe.to(lowercase_				)
             sd_pipe.set_progress_bar_config(disable=lowercase_				)

             _lowerCamelCase :	int						=self.get_inputs(lowercase_				)
             _lowerCamelCase :	str						=sd_pipe(**lowercase_				).images
             _lowerCamelCase :	List[Any]						=image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase :	str						=np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]				)
             assert np.abs(image_slice - expected_slice				).max() < 6E-3






      def  lowerCamelCase (      self		:  List[Any]				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Any						=0

             def callback_fn(lowercase_		:  int    ,     lowercase_		:  int    ,     lowercase_		:  torch.FloatTensor				) -> None:
                    _lowerCamelCase :	Optional[int]						=True
                    nonlocal number_of_steps
                    number_of_steps += 1
                    if step == 1:
                           _lowerCamelCase :	str						=latents.detach().cpu().numpy()
                           assert latents.shape == (1, 4, 64, 64)
                           _lowerCamelCase :	Union[str, Any]						=latents[0, -3:, -3:, -1]
                           _lowerCamelCase :	List[str]						=np.array(
                               [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]				)

                           assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2
                    elif step == 2:
                           _lowerCamelCase :	Dict						=latents.detach().cpu().numpy()
                           assert latents.shape == (1, 4, 64, 64)
                           _lowerCamelCase :	Tuple						=latents[0, -3:, -3:, -1]
                           _lowerCamelCase :	int						=np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]				)

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

             _lowerCamelCase :	Optional[Any]						=False

             _lowerCamelCase :	Union[str, Any]						=StableDiffusionImageVariationPipeline.from_pretrained(
                 'fusing/sd-image-variations-diffusers'    ,     safety_checker=lowercase_    ,     torch_dtype=torch.floataa    ,     )
             pipe.to(lowercase_				)
             pipe.set_progress_bar_config(disable=lowercase_				)
             pipe.enable_attention_slicing()

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






      def  lowerCamelCase (      self		:  str				)      ->		Optional[int]:
             """simple docstring"""




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

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

             _lowerCamelCase :	Any						=self.get_inputs(lowercase_    ,     dtype=torch.floataa				)
             _lowerCamelCase :	List[Any]						=pipe(**lowercase_				)

             _lowerCamelCase :	List[Any]						=torch.cuda.max_memory_allocated()
             # make sure that less than 2.6 GB is allocated
             assert mem_bytes < 2.6 * 10**9




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






      def  lowerCamelCase (      self		:  int				)      ->		Optional[int]:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  Tuple    ,     lowercase_		:  int="cpu"    ,     lowercase_		:  Any=torch.floataa    ,     lowercase_		:  List[Any]=0				)      ->		Dict:
             """simple docstring"""




             _lowerCamelCase :	Dict						=torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=load_image(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/input_image_vermeer.png'				)
             _lowerCamelCase :	int						=np.random.RandomState(lowercase_				).standard_normal((1, 4, 64, 64)				)
             _lowerCamelCase :	Dict						=torch.from_numpy(lowercase_				).to(device=lowercase_    ,     dtype=lowercase_				)
             _lowerCamelCase :	str						={
                 'image': init_image,
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 50,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs






      def  lowerCamelCase (      self		:  str				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'				)
             sd_pipe.to(lowercase_				)
             sd_pipe.set_progress_bar_config(disable=lowercase_				)

             _lowerCamelCase :	int						=self.get_inputs(lowercase_				)
             _lowerCamelCase :	int						=sd_pipe(**lowercase_				).images[0]

             _lowerCamelCase :	Tuple						=load_numpy(
                 'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
                 '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'				)
             _lowerCamelCase :	str						=np.abs(expected_image - image				).max()
             assert max_diff < 1E-3






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'				)
             _lowerCamelCase :	List[str]						=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config				)
             sd_pipe.to(lowercase_				)
             sd_pipe.set_progress_bar_config(disable=lowercase_				)

             _lowerCamelCase :	Optional[Any]						=self.get_inputs(lowercase_				)
             _lowerCamelCase :	Any						=25
             _lowerCamelCase :	Optional[Any]						=sd_pipe(**lowercase_				).images[0]

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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   ='AutoFeatureExtractor'
      UpperCamelCase__							:						List[str]   ='Speech2Text2Tokenizer'






      def __init__(      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             super().__init__(lowercase_    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=self.feature_extractor
             _lowerCamelCase :	Tuple						=False






      def __call__(      self		:  Tuple    ,     *lowercase_		:  str    ,     **lowercase_		:  List[Any]				)      ->		List[str]:
             """simple docstring"""




             if self._in_target_context_manager:
                    return self.current_processor(*lowercase_    ,     **lowercase_				)

             if "raw_speech" in kwargs:
                    warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
                    _lowerCamelCase :	List[str]						=kwargs.pop('raw_speech'				)
             else:
                    _lowerCamelCase :	str						=kwargs.pop('audio'    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=kwargs.pop('sampling_rate'    ,     lowercase_				)
             _lowerCamelCase :	List[str]						=kwargs.pop('text'    ,     lowercase_				)
             if len(lowercase_				) > 0:
                    _lowerCamelCase :	Dict						=args[0]
                    _lowerCamelCase :	Optional[Any]						=args[1:]

             if audio is None and text is None:
                    raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

             if audio is not None:
                    _lowerCamelCase :	List[Any]						=self.feature_extractor(lowercase_    ,     *lowercase_    ,     sampling_rate=lowercase_    ,     **lowercase_				)
             if text is not None:
                    _lowerCamelCase :	Tuple						=self.tokenizer(lowercase_    ,     **lowercase_				)

             if text is None:
                    return inputs
             elif audio is None:
                    return encodings
             else:
                    _lowerCamelCase :	Dict						=encodings['input_ids']
                    return inputs






      def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             return self.tokenizer.batch_decode(*lowercase_    ,     **lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]    ,     *lowercase_		:  Dict    ,     **lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             return self.tokenizer.decode(*lowercase_    ,     **lowercase_				)






      @contextmanager
      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




             warnings.warn(
                 '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                 'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                 'your audio inputs, or in a separate call.'				)
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	List[Any]						=self.tokenizer
             yield
             _lowerCamelCase :	Optional[int]						=self.feature_extractor
             _lowerCamelCase :	List[str]						=False


