



"""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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     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 	snake_case ( self							:						List[str]  )->      List[str]:
        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=1000,						norm_type='''ada_norm_zero''',						norm_elementwise_affine=lowerCamelCase,						)
        lowerCamelCase__	:	Tuple				=AutoencoderKL()
        lowerCamelCase__	:	Optional[int]				=DDIMScheduler()
        lowerCamelCase__	:	Dict				={'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
        return components



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



     def 	snake_case ( self							:						Dict  )->      int:
        lowerCamelCase__	:	Union[str, Any]				='''cpu'''

        lowerCamelCase__	:	Optional[Any]				=self.get_dummy_components()
        lowerCamelCase__	:	Any				=self.pipeline_class(**lowerCamelCase  )
        pipe.to(lowerCamelCase  )
        pipe.set_progress_bar_config(disable=lowerCamelCase  )

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

        self.assertEqual(image.shape,						(1, 16, 16, 3)  )
        lowerCamelCase__	:	Union[str, 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]  )
        lowerCamelCase__	:	Optional[int]				=np.abs(image_slice.flatten() - expected_slice  ).max()
        self.assertLessEqual(lowerCamelCase,						1E-3  )



     def 	snake_case ( self							:						str  )->      List[str]:
        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 	snake_case ( self							:						Dict  )->      Any:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )





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


     '''simple docstring'''





     def 	snake_case ( self							:						List[Any]  )->      Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



     def 	snake_case ( self							:						Dict  )->      str:
        lowerCamelCase__	:	Any				=torch.manual_seed(0  )

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

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

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

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

        lowerCamelCase__	:	Any				=['''vase''', '''umbrella''']
        lowerCamelCase__	:	Dict				=pipe.get_label_ids(lowerCamelCase  )

        lowerCamelCase__	:	Tuple				=torch.manual_seed(0  )
        lowerCamelCase__	:	Tuple				=pipe(lowerCamelCase,						generator=lowerCamelCase,						num_inference_steps=25,						output_type='''np'''  ).images

        for word, image in zip(lowerCamelCase,						lowerCamelCase  ):
           lowerCamelCase__	:	List[Any]				=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,
)


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase				:				Union[str, Any]   		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




