





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					DiTPipeline
  a_              =					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  a_              =					PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  a_              =					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  a_              =					False






  def 							lowercase						(							self					:						Tuple	)				->		List[Any]:
     torch.manual_seed(0	)
     __lowerCAmelCase				      =      TransformeraDModel(
         sample_size=1_6   ,    num_layers=2   ,    patch_size=4   ,    attention_head_dim=8   ,    num_attention_heads=2   ,    in_channels=4   ,    out_channels=8   ,    attention_bias=lowerCAmelCase_   ,    activation_fn='gelu-approximate'   ,    num_embeds_ada_norm=1_0_0_0   ,    norm_type='ada_norm_zero'   ,    norm_elementwise_affine=lowerCAmelCase_   ,    )
     __lowerCAmelCase				      =      AutoencoderKL()
     __lowerCAmelCase				      =      DDIMScheduler()
     __lowerCAmelCase				      =      {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
     return components






  def 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Tuple=0	)				->		int:
     if str(lowerCAmelCase_	).startswith('mps'	):
        __lowerCAmelCase				      =      torch.manual_seed(lowerCAmelCase_	)
     else:
        __lowerCAmelCase				      =      torch.Generator(device=lowerCAmelCase_	).manual_seed(lowerCAmelCase_	)
     __lowerCAmelCase				      =      {
         'class_labels': [1],
         'generator': generator,
         'num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs






  def 							lowercase						(							self					:						Any	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      'cpu'

     __lowerCAmelCase				      =      self.get_dummy_components()
     __lowerCAmelCase				      =      self.pipeline_class(**lowerCAmelCase_	)
     pipe.to(lowerCAmelCase_	)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

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

     self.assertEqual(image.shape   ,    (1, 1_6, 1_6, 3)	)
     __lowerCAmelCase				      =      np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]	)
     __lowerCAmelCase				      =      np.abs(image_slice.flatten() - expected_slice	).max()
     self.assertLessEqual(lowerCAmelCase_   ,    1e-3	)






  def 							lowercase						(							self					:						List[Any]	)				->		Tuple:
     self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase_   ,    expected_max_diff=1e-3	)






  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()   ,    reason='XFormers attention is only available with CUDA and `xformers` installed'   ,    )
  def 							lowercase						(							self					:						List[str]	)				->		List[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)





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

  """simple docstring"""


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






  def 							lowercase						(							self					:						str	)				->		List[str]:
     __lowerCAmelCase				      =      torch.manual_seed(0	)

     __lowerCAmelCase				      =      DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'	)
     pipe.to('cuda'	)

     __lowerCAmelCase				      =      ['vase', 'umbrella', 'white shark', 'white wolf']
     __lowerCAmelCase				      =      pipe.get_label_ids(lowerCAmelCase_	)

     __lowerCAmelCase				      =      pipe(lowerCAmelCase_   ,    generator=lowerCAmelCase_   ,    num_inference_steps=4_0   ,    output_type='np'	).images

     for word, image in zip(lowerCAmelCase_   ,    lowerCAmelCase_	):
        __lowerCAmelCase				      =      load_numpy(
            f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""	)
        assert np.abs((expected_image - image).max()	) < 1e-2






  def 							lowercase						(							self					:						int	)				->		int:
     __lowerCAmelCase				      =      DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'	)
     __lowerCAmelCase				      =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
     pipe.to('cuda'	)

     __lowerCAmelCase				      =      ['vase', 'umbrella']
     __lowerCAmelCase				      =      pipe.get_label_ids(lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.manual_seed(0	)
     __lowerCAmelCase				      =      pipe(lowerCAmelCase_   ,    generator=lowerCAmelCase_   ,    num_inference_steps=2_5   ,    output_type='np'	).images

     for word, image in zip(lowerCAmelCase_   ,    lowerCAmelCase_	):
        __lowerCAmelCase				      =      load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            f"""/dit/{word}_512.npy"""	)

        assert np.abs((expected_image - image).max()	) < 1e-1






"""simple docstring"""







from typing import TYPE_CHECKING

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


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




