



"""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 							_UpperCAmelCase							( a		,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  __lowerCAmelCase	(					self		)							->					Optional[int]:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    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							:  Optional[Any]											=    AutoencoderKL()
         _UpperCAmelCase							:  int											=    DDIMScheduler()
         _UpperCAmelCase							:  Optional[int]											=    {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components





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





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

         _UpperCAmelCase							:  Dict											=    self.get_dummy_components()
         _UpperCAmelCase							:  List[Any]											=    self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Union[str, Any]											=    self.get_dummy_inputs(A		)
         _UpperCAmelCase							:  Tuple											=    pipe(**A		).images
         _UpperCAmelCase							:  str											=    image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,	(1, 1_6, 1_6, 3)		)
         _UpperCAmelCase							:  List[Any]											=    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]		)
         _UpperCAmelCase							:  Union[str, Any]											=    np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(A       ,	1E-3		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         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  __lowerCAmelCase	(					self		)							->					Dict:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)



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





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					str:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





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

         _UpperCAmelCase							:  Union[str, Any]											=    DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  Optional[Any]											=    ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         _UpperCAmelCase							:  int											=    pipe.get_label_ids(A		)

         _UpperCAmelCase							:  List[Any]											=    pipe(A       ,	generator=A       ,	num_inference_steps=4_0       ,	output_type='''np'''		).images

         for word, image in zip(A       ,	A		):
             _UpperCAmelCase							:  Union[str, Any]											=    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  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Dict											=    DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''		)
         _UpperCAmelCase							:  Any											=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  int											=    ['''vase''', '''umbrella''']
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.get_label_ids(A		)

         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe(A       ,	generator=A       ,	num_inference_steps=2_5       ,	output_type='''np'''		).images

         for word, image in zip(A       ,	A		):
             _UpperCAmelCase							:  Tuple											=    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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


