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      __lowercase					(    _A       ,     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        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									torch.manual_seed(0   )
									lowercase							=		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=10_00							,							norm_type='''ada_norm_zero'''							,							norm_elementwise_affine=__lowerCamelCase							,							)
									lowercase							=		AutoencoderKL()
									lowercase							=		DDIMScheduler()
									lowercase							=		{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
									return components

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int=0   )							->		Union[str, Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		'''cpu'''

									lowercase							=		self.get_dummy_components()
									lowercase							=		self.pipeline_class(**__lowerCamelCase   )
									pipe.to(__lowerCamelCase   )
									pipe.set_progress_bar_config(disable=__lowerCamelCase   )

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

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

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

									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				:      Dict   )							->		Dict:


									'''simple docstring'''

									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )






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

						def        __a		(	self				:      List[Any]   )							->		List[str]:


									'''simple docstring'''

									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									lowercase							=		torch.manual_seed(0   )

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

									lowercase							=		['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
									lowercase							=		pipe.get_label_ids(__lowerCamelCase   )

									lowercase							=		pipe(__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=40							,							output_type='''np'''   ).images

									for word, image in zip(__lowerCamelCase							,							__lowerCamelCase   ):
												lowercase							=		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				:      Any   )							->		Dict:


									'''simple docstring'''

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

									lowercase							=		['''vase''', '''umbrella''']
									lowercase							=		pipe.get_label_ids(__lowerCamelCase   )

									lowercase							=		torch.manual_seed(0   )
									lowercase							=		pipe(__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=25							,							output_type='''np'''   ).images

									for word, image in zip(__lowerCamelCase							,							__lowerCamelCase   ):
												lowercase							=		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






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      Optional[int]=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      Union[str, Any]=4_00							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Tuple=True							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		size if size is not None else {'''shortest_edge''': 20}
									lowercase							=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		image_size
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		size
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		do_flip_channel_order



						def        __a		(	self				:      Optional[int]   )							->		Any:


									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_flip_channel_order": self.do_flip_channel_order,
									}






@require_torch
@require_vision
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     MobileViTImageProcessor if is_vision_available() else None

						def        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

									lowercase							=		MobileViTImageProcessingTester(self   )

						@property
						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_resize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''size'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_center_crop'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''center_crop'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_flip_channel_order'''   )   )

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


									'''simple docstring'''

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 20}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 18, '''width''': 18}   )

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 42}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 84, '''width''': 84}   )

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							Image.Image   )

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		image_processing(__lowerCamelCase							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							numpify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							np.ndarray   )

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		image_processing(__lowerCamelCase							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)



						def        __a		(	self				:      Dict   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							torchify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							torch.Tensor   )

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		image_processing(__lowerCamelCase							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)





