

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def   a   (    A__			:     int  )					->   Dict:





      """simple docstring"""



      _lowercase			 =[tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]  )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						StableDiffusionLatentUpscalePipeline
  _a		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      """height""",
      """width""",
      """cross_attention_kwargs""",
      """negative_prompt_embeds""",
      """prompt_embeds""",
  }
  _a		    =						PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
  _a		    =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _a		    =						frozenset(
      []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _a		    =						frozenset([]							)

  _a		    =						True




  @property
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =1
        _lowercase			 =4
        _lowercase			 =(16, 16)

        _lowercase			 =floats_tensor((batch_size, num_channels) + sizes							,     rng=random.Random(0      )      ).to(lowerCAmelCase      )
        return image




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        torch.manual_seed(0      )
        _lowercase			 =UNetaDConditionModel(
            act_fn='gelu'							,     attention_head_dim=8							,     norm_num_groups=lowerCAmelCase							,     block_out_channels=[32, 32, 64, 64]							,     time_cond_proj_dim=160							,     conv_in_kernel=1							,     conv_out_kernel=1							,     cross_attention_dim=32							,     down_block_types=(
                'KDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
            )							,     in_channels=8							,     mid_block_type=lowerCAmelCase							,     only_cross_attention=lowerCAmelCase							,     out_channels=5							,     resnet_time_scale_shift='scale_shift'							,     time_embedding_type='fourier'							,     timestep_post_act='gelu'							,     up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')							,     )
        _lowercase			 =AutoencoderKL(
            block_out_channels=[32, 32, 64, 64]							,     in_channels=3							,     out_channels=3							,     down_block_types=[
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
            ]							,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']							,     latent_channels=4							,     )
        _lowercase			 =EulerDiscreteScheduler(prediction_type='sample'      )
        _lowercase			 =CLIPTextConfig(
            bos_token_id=0							,     eos_token_id=2							,     hidden_size=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							,     hidden_act='quick_gelu'							,     projection_dim=512							,     )
        _lowercase			 =CLIPTextModel(lowerCAmelCase      )
        _lowercase			 =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

        _lowercase			 ={
            'unet': model.eval(),
            'vae': vae.eval(),
            'scheduler': scheduler,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
        }

        return components




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase=0      )						->				str:
        '''simple docstring'''






        if str(lowerCAmelCase      ).startswith('mps'      ):
              _lowercase			 =torch.manual_seed(lowerCAmelCase      )
        else:
              _lowercase			 =torch.Generator(device=lowerCAmelCase      ).manual_seed(lowerCAmelCase      )
        _lowercase			 ={
            'prompt': 'A painting of a squirrel eating a burger',
            'image': self.dummy_image.cpu(),
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 ='cpu'

        _lowercase			 =self.get_dummy_components()
        _lowercase			 =self.pipeline_class(**lowerCAmelCase      )
        pipe.to(lowerCAmelCase      )
        pipe.set_progress_bar_config(disable=lowerCAmelCase      )

        _lowercase			 =self.get_dummy_inputs(lowerCAmelCase      )
        _lowercase			 =pipe(**lowerCAmelCase      ).images
        _lowercase			 =image[0, -3:, -3:, -1]

        self.assertEqual(image.shape							,     (1, 256, 256, 3)      )
        _lowercase			 =np.array(
            [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]      )
        _lowercase			 =np.abs(image_slice.flatten() - expected_slice      ).max()
        self.assertLessEqual(lowerCAmelCase							,     1e-3      )




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        super().test_attention_slicing_forward_pass(expected_max_diff=7e-3      )




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        super().test_cpu_offload_forward_pass(expected_max_diff=3e-3      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=7e-3      )




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3      )




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        super().test_save_load_local(expected_max_difference=3e-3      )




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        super().test_save_load_optional_components(expected_max_difference=3e-3      )




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =[
            'DDIMScheduler',
            'DDPMScheduler',
            'PNDMScheduler',
            'HeunDiscreteScheduler',
            'EulerAncestralDiscreteScheduler',
            'KDPM2DiscreteScheduler',
            'KDPM2AncestralDiscreteScheduler',
            'DPMSolverSDEScheduler',
        ]
        _lowercase			 =self.get_dummy_components()
        _lowercase			 =self.pipeline_class(**lowerCAmelCase      )

        # make sure that PNDM does not need warm-up
        pipe.scheduler.register_to_config(skip_prk_steps=lowerCAmelCase      )

        pipe.to(lowerCAmelCase      )
        pipe.set_progress_bar_config(disable=lowerCAmelCase      )
        _lowercase			 =self.get_dummy_inputs(lowerCAmelCase      )
        _lowercase			 =2

        _lowercase			 =[]
        for scheduler_enum in KarrasDiffusionSchedulers:
              if scheduler_enum.name in skip_schedulers:
                    # no sigma schedulers are not supported
                    # no schedulers
                    continue

              _lowercase			 =getattr(lowerCAmelCase							,     scheduler_enum.name      )
              _lowercase			 =scheduler_cls.from_config(pipe.scheduler.config      )
              _lowercase			 =pipe(**lowerCAmelCase      )[0]
              outputs.append(lowerCAmelCase      )

        assert check_same_shape(lowerCAmelCase      )





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




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =torch.manual_seed(33      )

        _lowercase			 =StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'							,     torch_dtype=torch.floataa      )
        pipe.to('cuda'      )

        _lowercase			 =StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler'							,     torch_dtype=torch.floataa      )
        upscaler.to('cuda'      )

        _lowercase			 ='a photo of an astronaut high resolution, unreal engine, ultra realistic'

        _lowercase			 =pipe(lowerCAmelCase							,     generator=lowerCAmelCase							,     output_type='latent'      ).images

        _lowercase			 =upscaler(
            prompt=lowerCAmelCase							,     image=lowerCAmelCase							,     num_inference_steps=20							,     guidance_scale=0							,     generator=lowerCAmelCase							,     output_type='np'							,     ).images[0]

        _lowercase			 =load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'      )
        assert np.abs((expected_image - image).mean()      ) < 5e-2




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =torch.manual_seed(33      )

        _lowercase			 =StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler'							,     torch_dtype=torch.floataa      )
        upscaler.to('cuda'      )

        _lowercase			 ='the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

        _lowercase			 =load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'      )

        _lowercase			 =upscaler(
            prompt=lowerCAmelCase							,     image=lowerCAmelCase							,     num_inference_steps=20							,     guidance_scale=0							,     generator=lowerCAmelCase							,     output_type='np'							,     ).images[0]

        _lowercase			 =load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'      )
        assert np.abs((expected_image - image).max()      ) < 5e-2





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowercase_             =     {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['BeitFeatureExtractor']
  lowercase_             =     ['BeitImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BeitForImageClassification',
      'BeitForMaskedImageModeling',
      'BeitForSemanticSegmentation',
      'BeitModel',
      'BeitPreTrainedModel',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'FlaxBeitForImageClassification',
      'FlaxBeitForMaskedImageModeling',
      'FlaxBeitModel',
      'FlaxBeitPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_beit import BeitFeatureExtractor
    from .image_processing_beit import BeitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_beit import (
        BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
        BeitPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_beit import (
        FlaxBeitForImageClassification,
        FlaxBeitForMaskedImageModeling,
        FlaxBeitModel,
        FlaxBeitPreTrainedModel,
    )


else:
  import sys

  lowercase_             =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


