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


				def      lowerCAmelCase_  (					self	:  Dict					):
						torch.manual_seed(0					)
						_UpperCAmelCase						 =      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=__lowerCAmelCase   ,				activation_fn="""gelu-approximate"""   ,				num_embeds_ada_norm=1000   ,				norm_type="""ada_norm_zero"""   ,				norm_elementwise_affine=__lowerCAmelCase   ,				)
						_UpperCAmelCase						 =      AutoencoderKL()
						_UpperCAmelCase						 =      DDIMScheduler()
						_UpperCAmelCase						 =      {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
						return components


				def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Dict=0					):
						if str(__lowerCAmelCase					).startswith("""mps"""					):
								_UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
						else:
								_UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
						_UpperCAmelCase						 =      {
						    """class_labels""": [1],
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """output_type""": """numpy""",
						}
						return inputs


				def      lowerCAmelCase_  (					self	:  Tuple					):
						_UpperCAmelCase						 =      """cpu"""

						_UpperCAmelCase						 =      self.get_dummy_components()
						_UpperCAmelCase						 =      self.pipeline_class(**__lowerCAmelCase					)
						pipe.to(__lowerCAmelCase					)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

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

						self.assertEqual(image.shape   ,				(1, 16, 16, 3)					)
						_UpperCAmelCase						 =      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						 =      np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(__lowerCAmelCase   ,				1e-3					)


				def      lowerCAmelCase_  (					self	:  Dict					):
						self._test_inference_batch_single_identical(relax_max_difference=__lowerCAmelCase   ,				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	:  List[Any]					):
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)



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


				def      lowerCAmelCase_  (					self	:  int					):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()


				def      lowerCAmelCase_  (					self	:  Tuple					):
						_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(__lowerCAmelCase					)

						_UpperCAmelCase						 =      pipe(__lowerCAmelCase   ,				generator=__lowerCAmelCase   ,				num_inference_steps=40   ,				output_type="""np"""					).images

						for word, image in zip(__lowerCAmelCase   ,				__lowerCAmelCase					):
								_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      lowerCAmelCase_  (					self	:  List[Any]					):
						_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(__lowerCAmelCase					)

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

						for word, image in zip(__lowerCAmelCase   ,				__lowerCAmelCase					):
								_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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    UpperCAmelCase__  		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
