

'''simple docstring'''




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 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[Any]     	= DiTPipeline
  __lowerCAmelCase       :			str     	= CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  __lowerCAmelCase       :			Dict     	= PipelineTesterMixin.required_optional_params - {
      "latents",
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  __lowerCAmelCase       :			Union[str, Any]     	= CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  __lowerCAmelCase       :			str     	= False
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    torch.manual_seed(0  )
    UpperCAmelCase    				=						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=a_ ,			activation_fn='gelu-approximate' ,			num_embeds_ada_norm=1_0_0_0 ,			norm_type='ada_norm_zero' ,			norm_elementwise_affine=a_ ,			)
    UpperCAmelCase    				=						AutoencoderKL()
    UpperCAmelCase    				=						DDIMScheduler()
    UpperCAmelCase    				=						{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
    return components
  def     snake_case_      (  self ,			a_ ,			a_=0  )				->					int:


    """simple docstring"""
    if str(a_  ).startswith('mps'  ):
      UpperCAmelCase    				=						torch.manual_seed(a_  )
    else:
      UpperCAmelCase    				=						torch.Generator(device=a_  ).manual_seed(a_  )
    UpperCAmelCase    				=						{
        'class_labels': [1],
        'generator': generator,
        'num_inference_steps': 2,
        'output_type': 'numpy',
    }
    return inputs
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						'cpu'

    UpperCAmelCase    				=						self.get_dummy_components()
    UpperCAmelCase    				=						self.pipeline_class(**a_  )
    pipe.to(a_  )
    pipe.set_progress_bar_config(disable=a_  )

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

    self.assertEqual(image.shape ,			(1, 1_6, 1_6, 3)  )
    UpperCAmelCase    				=						np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
    UpperCAmelCase    				=						np.abs(image_slice.flatten() - expected_slice  ).max()
    self.assertLessEqual(a_ ,			1E-3  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    self._test_inference_batch_single_identical(relax_max_difference=a_ ,			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     snake_case_      (  self  )				->					int:


    """simple docstring"""
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )




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

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()
  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						torch.manual_seed(0  )

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

    UpperCAmelCase    				=						['vase', 'umbrella', 'white shark', 'white wolf']
    UpperCAmelCase    				=						pipe.get_label_ids(a_  )

    UpperCAmelCase    				=						pipe(a_ ,			generator=a_ ,			num_inference_steps=4_0 ,			output_type='np'  ).images

    for word, image in zip(a_ ,			a_  ):
      UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'  )
    UpperCAmelCase    				=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
    pipe.to('cuda'  )

    UpperCAmelCase    				=						['vase', 'umbrella']
    UpperCAmelCase    				=						pipe.get_label_ids(a_  )

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

    for word, image in zip(a_ ,			a_  ):
      UpperCAmelCase    				=						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_tf_available, is_torch_available


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


