

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



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = 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=UpperCamelCase__						,			activation_fn="""gelu-approximate"""						,			num_embeds_ada_norm=1000						,			norm_type="""ada_norm_zero"""						,			norm_elementwise_affine=UpperCamelCase__						,			)
           A_		   = AutoencoderKL()
           A_		   = DDIMScheduler()
           A_		   = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
           return components



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=0    )  -> List[str]:





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



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = """cpu"""

           A_		   = self.get_dummy_components()
           A_		   = self.pipeline_class(**UpperCamelCase__    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

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

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



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           self._test_inference_batch_single_identical(relax_max_difference=UpperCamelCase__						,			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    )  -> Optional[int]:





           '''simple docstring'''
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )


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



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = torch.manual_seed(0    )

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

           A_		   = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
           A_		   = pipe.get_label_ids(UpperCamelCase__    )

           A_		   = pipe(UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=40						,			output_type="""np"""    ).images

           for word, image in zip(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = 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    )  -> List[str]:





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

           A_		   = ["""vase""", """umbrella"""]
           A_		   = pipe.get_label_ids(UpperCamelCase__    )

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(UpperCamelCase__						,			generator=UpperCamelCase__						,			num_inference_steps=25						,			output_type="""np"""    ).images

           for word, image in zip(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = 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						    =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      ['''BlenderbotTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BlenderbotForCausalLM''',
          '''BlenderbotForConditionalGeneration''',
          '''BlenderbotModel''',
          '''BlenderbotPreTrainedModel''',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''TFBlenderbotForConditionalGeneration''',
          '''TFBlenderbotModel''',
          '''TFBlenderbotPreTrainedModel''',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''FlaxBlenderbotForConditionalGeneration''',
          '''FlaxBlenderbotModel''',
          '''FlaxBlenderbotPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_blenderbot import (
          BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BlenderbotConfig,
          BlenderbotOnnxConfig,
      )
      from .tokenization_blenderbot import BlenderbotTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_blenderbot import (
                BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BlenderbotForCausalLM,
                BlenderbotForConditionalGeneration,
                BlenderbotModel,
                BlenderbotPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_blenderbot import (
                TFBlenderbotForConditionalGeneration,
                TFBlenderbotModel,
                TFBlenderbotPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_blenderbot import (
                FlaxBlenderbotForConditionalGeneration,
                FlaxBlenderbotModel,
                FlaxBlenderbotPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
