


"""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):


					"""simple docstring"""
					snake_case__	:					Tuple													=DiTPipeline
					snake_case__	:					Optional[int]													=CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
					snake_case__	:					Optional[Any]													=PipelineTesterMixin.required_optional_params - {
					    '''latents''',
					    '''num_images_per_prompt''',
					    '''callback''',
					    '''callback_steps''',
					}
					snake_case__	:					Tuple													=CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
					snake_case__	:					Optional[int]													=False

					def 		a__						(	self:						List[Any]	)->    Any:
											torch.manual_seed(0	)
											lowerCamelCase					:					int        =	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=1_000     ,		norm_type="""ada_norm_zero"""     ,		norm_elementwise_affine=lowerCAmelCase__     ,		)
											lowerCamelCase					:					List[Any]        =	AutoencoderKL()
											lowerCamelCase					:					Optional[Any]        =	DDIMScheduler()
											lowerCamelCase					:					Optional[Any]        =	{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
											return components

					def 		a__						(	self:						int     ,		__a:						Optional[int]     ,		__a:						List[Any]=0	)->    Tuple:
											if str(lowerCAmelCase__	).startswith("""mps"""	):
																	lowerCamelCase					:					int        =	torch.manual_seed(lowerCAmelCase__	)
											else:
																	lowerCamelCase					:					Union[str, Any]        =	torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
											lowerCamelCase					:					Optional[int]        =	{
											    """class_labels""": [1],
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """output_type""": """numpy""",
											}
											return inputs

					def 		a__						(	self:						Union[str, Any]	)->    Any:
											lowerCamelCase					:					str        =	"""cpu"""

											lowerCamelCase					:					Optional[int]        =	self.get_dummy_components()
											lowerCamelCase					:					str        =	self.pipeline_class(**lowerCAmelCase__	)
											pipe.to(lowerCAmelCase__	)
											pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

											lowerCamelCase					:					Dict        =	self.get_dummy_inputs(lowerCAmelCase__	)
											lowerCamelCase					:					Dict        =	pipe(**lowerCAmelCase__	).images
											lowerCamelCase					:					Union[str, Any]        =	image[0, -3:, -3:, -1]

											self.assertEqual(image.shape     ,		(1, 16, 16, 3)	)
											lowerCamelCase					:					Optional[Any]        =	np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]	)
											lowerCamelCase					:					Optional[Any]        =	np.abs(image_slice.flatten() - expected_slice	).max()
											self.assertLessEqual(lowerCAmelCase__     ,		1e-3	)

					def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
											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 		a__						(	self:						Any	)->    str:
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)


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


					"""simple docstring"""

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

					def 		a__						(	self:						List[str]	)->    Optional[Any]:
											lowerCamelCase					:					List[str]        =	torch.manual_seed(0	)

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

											lowerCamelCase					:					Any        =	["""vase""", """umbrella""", """white shark""", """white wolf"""]
											lowerCamelCase					:					List[str]        =	pipe.get_label_ids(lowerCAmelCase__	)

											lowerCamelCase					:					List[Any]        =	pipe(lowerCAmelCase__     ,		generator=lowerCAmelCase__     ,		num_inference_steps=40     ,		output_type="""np"""	).images

											for word, image in zip(lowerCAmelCase__     ,		lowerCAmelCase__	):
																	lowerCamelCase					:					Optional[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 		a__						(	self:						Optional[int]	)->    List[Any]:
											lowerCamelCase					:					List[str]        =	DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""	)
											lowerCamelCase					:					str        =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
											pipe.to("""cuda"""	)

											lowerCamelCase					:					Optional[Any]        =	["""vase""", """umbrella"""]
											lowerCamelCase					:					Any        =	pipe.get_label_ids(lowerCAmelCase__	)

											lowerCamelCase					:					Dict        =	torch.manual_seed(0	)
											lowerCamelCase					:					str        =	pipe(lowerCAmelCase__     ,		generator=lowerCAmelCase__     ,		num_inference_steps=25     ,		output_type="""np"""	).images

											for word, image in zip(lowerCAmelCase__     ,		lowerCAmelCase__	):
																	lowerCamelCase					:					Dict        =	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,
    is_vision_available,
)


__lowerCamelCase      :List[str]	    =		{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[int]	    =		['OwlViTFeatureExtractor']
    __lowerCamelCase      :List[str]	    =		['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

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




