





"""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       a							(   a__   , unittest.TestCase  ):
 snake_case__             =       DiTPipeline
 snake_case__             =       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {
     '''latents''',
     '''num_images_per_prompt''',
     '''callback''',
     '''callback_steps''',
 }
 snake_case__             =       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= TransformeraDModel(
      sample_size=16				,       num_layers=2				,       patch_size=4				,       attention_head_dim=8				,       num_attention_heads=2				,       in_channels=4				,       out_channels=8				,       attention_bias=_snake_case				,       activation_fn='gelu-approximate'				,       num_embeds_ada_norm=10_00				,       norm_type='ada_norm_zero'				,       norm_elementwise_affine=_snake_case				,       )
  lowerCAmelCase    				= AutoencoderKL()
  lowerCAmelCase    				= DDIMScheduler()
  lowerCAmelCase    				= {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
  return components



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
  lowerCAmelCase    				= {
      'class_labels': [1],
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }
  return inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'cpu'

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

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

  self.assertEqual(image.shape				,       (1, 16, 16, 3)     )
  lowerCAmelCase    				= np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]     )
  lowerCAmelCase    				= np.abs(image_slice.flatten() - expected_slice     ).max()
  self.assertLessEqual(_snake_case				,       1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(relax_max_difference=_snake_case				,       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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )







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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  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(_snake_case     )

  lowerCAmelCase    				= pipe(_snake_case				,       generator=_snake_case				,       num_inference_steps=40				,       output_type='np'     ).images

  for word, image in zip(_snake_case				,       _snake_case     ):
   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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  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(_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= pipe(_snake_case				,       generator=_snake_case				,       num_inference_steps=25				,       output_type='np'     ).images

  for word, image in zip(_snake_case				,       _snake_case     ):
   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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase     :						Dict						       =  {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						int						       =  ['''LayoutXLMTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       __UpperCamelCase     :						Dict						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





