



"""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__						(  UpperCAmelCase__,     unittest.TestCase   ):
   _lowerCamelCase    							= DiTPipeline
   _lowerCamelCase    							= CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   _lowerCamelCase    							= PipelineTesterMixin.required_optional_params - {
       """latents""",
       """num_images_per_prompt""",
       """callback""",
       """callback_steps""",
   }
   _lowerCamelCase    							= CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   _lowerCamelCase    							= False


   def 				lowercase (							self					:							Dict	)			->				List[str]:
         torch.manual_seed(0	)
         lowercase     :					Optional[Any] 		=      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=__lowercase,   activation_fn='gelu-approximate',   num_embeds_ada_norm=1000,   norm_type='ada_norm_zero',   norm_elementwise_affine=__lowercase,   )
         lowercase     :					Optional[int] 		=      AutoencoderKL()
         lowercase     :					List[Any] 		=      DDIMScheduler()
         lowercase     :					Union[str, Any] 		=      {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components


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


   def 				lowercase (							self					:							int	)			->				Any:
         lowercase     :					Any 		=      '''cpu'''

         lowercase     :					int 		=      self.get_dummy_components()
         lowercase     :					Tuple 		=      self.pipeline_class(**__lowercase	)
         pipe.to(__lowercase	)
         pipe.set_progress_bar_config(disable=__lowercase	)

         lowercase     :					str 		=      self.get_dummy_inputs(__lowercase	)
         lowercase     :					int 		=      pipe(**__lowercase	).images
         lowercase     :					str 		=      image[0, -3:, -3:, -1]

         self.assertEqual(image.shape,   (1, 16, 16, 3)	)
         lowercase     :					Any 		=      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]	)
         lowercase     :					Optional[int] 		=      np.abs(image_slice.flatten() - expected_slice	).max()
         self.assertLessEqual(__lowercase,   1e-3	)


   def 				lowercase (							self					:							Optional[Any]	)			->				int:
         self._test_inference_batch_single_identical(relax_max_difference=__lowercase,   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					:							int	)			->				Optional[Any]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)




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


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


   def 				lowercase (							self					:							List[Any]	)			->				int:
         lowercase     :					Dict 		=      torch.manual_seed(0	)

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

         lowercase     :					Optional[Any] 		=      ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         lowercase     :					str 		=      pipe.get_label_ids(__lowercase	)

         lowercase     :					Union[str, Any] 		=      pipe(__lowercase,   generator=__lowercase,   num_inference_steps=40,   output_type='np'	).images

         for word, image in zip(__lowercase,   __lowercase	):
               lowercase     :					Dict 		=      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					:							Optional[Any]	)			->				List[str]:
         lowercase     :					Any 		=      DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'	)
         lowercase     :					List[str] 		=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
         pipe.to('cuda'	)

         lowercase     :					Any 		=      ['''vase''', '''umbrella''']
         lowercase     :					List[Any] 		=      pipe.get_label_ids(__lowercase	)

         lowercase     :					Optional[Any] 		=      torch.manual_seed(0	)
         lowercase     :					List[Any] 		=      pipe(__lowercase,   generator=__lowercase,   num_inference_steps=25,   output_type='np'	).images

         for word, image in zip(__lowercase,   __lowercase	):
               lowercase     :					int 		=      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




from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels