




'''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      __lowerCAmelCase    (						self :						List[Any]      )    ->							Any:


														"""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=lowerCAmelCase__							,    activation_fn='''gelu-approximate'''							,    num_embeds_ada_norm=10_00							,    norm_type='''ada_norm_zero'''							,    norm_elementwise_affine=lowerCAmelCase__							,    )
														_a			  =       AutoencoderKL()
														_a			  =       DDIMScheduler()
														_a			  =       {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
														return components







							def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						Optional[int]							,    lowerCAmelCase_ :						List[Any]=0      )    ->							Tuple:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()
														_a			  =       self.pipeline_class(**lowerCAmelCase__      )
														pipe.to(lowerCAmelCase__      )
														pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

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

														self.assertEqual(image.shape							,    (1, 16, 16, 3)      )
														_a			  =       np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]      )
														_a			  =       np.abs(image_slice.flatten() - expected_slice      ).max()
														self.assertLessEqual(lowerCAmelCase__							,    1e-3      )







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Union[str, Any]:


														"""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      __lowerCAmelCase    (						self :						Any      )    ->							str:


														"""simple docstring"""


														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )



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







							def      __lowerCAmelCase    (						self :						str      )    ->							Dict:


														"""simple docstring"""


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







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


														"""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(lowerCAmelCase__      )

														_a			  =       pipe(lowerCAmelCase__							,    generator=lowerCAmelCase__							,    num_inference_steps=40							,    output_type='''np'''      ).images

														for word, image in zip(lowerCAmelCase__							,    lowerCAmelCase__      ):
																					_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      __lowerCAmelCase    (						self :						Optional[int]      )    ->							List[Any]:


														"""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(lowerCAmelCase__      )

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

														for word, image in zip(lowerCAmelCase__							,    lowerCAmelCase__      ):
																					_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 Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

_snake_case   :   str     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							if isinstance(UpperCamelCase				,		(list, tuple)   ) and isinstance(videos[0]				,		(list, tuple)   ) and is_valid_image(videos[0][0]   ):
														return videos

							elif isinstance(UpperCamelCase				,		(list, tuple)   ) and is_valid_image(videos[0]   ):
														return [videos]

							elif is_valid_image(UpperCamelCase   ):
														return [[videos]]

							raise ValueError(f'Could not make batched video from {videos}'   )

class     A					(	_a					):
							lowercase_           =       ['pixel_values']







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = PILImageResampling.BILINEAR							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Union[int, float] = 1 / 2_55							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    **lowerCAmelCase_ :						List[str]							,    )    ->							None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_      )
														_a			  =       size if size is not None else {'''shortest_edge''': 2_56}
														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														_a			  =       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a			  =       get_size_dict(lowerCAmelCase_							,    param_name='''crop_size'''      )

														_a			  =       do_resize
														_a			  =       size
														_a			  =       do_center_crop
														_a			  =       crop_size
														_a			  =       resample
														_a			  =       do_rescale
														_a			  =       rescale_factor
														_a			  =       offset
														_a			  =       do_normalize
														_a			  =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a			  =       image_std if image_std is not None else IMAGENET_STANDARD_STD







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Dict[str, int]							,    lowerCAmelCase_ :						PILImageResampling = PILImageResampling.BILINEAR							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							np.ndarray:


														"""simple docstring"""


														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														if "shortest_edge" in size:
																					_a			  =       get_resize_output_image_size(lowerCAmelCase_							,    size['''shortest_edge''']							,    default_to_square=lowerCAmelCase_      )
														elif "height" in size and "width" in size:
																					_a			  =       (size['''height'''], size['''width'''])
														else:
																					raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
														return resize(lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Dict[str, int]							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Tuple							,    )    ->							np.ndarray:


														"""simple docstring"""


														_a			  =       get_size_dict(lowerCAmelCase_      )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'      )
														return center_crop(lowerCAmelCase_							,    size=(size['''height'''], size['''width'''])							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Union[int, float]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Any							,    )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       image.astype(np.floataa      )
														if offset:
																					_a			  =       image - (scale / 2)
														return rescale(lowerCAmelCase_							,    scale=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Union[float, List[float]]							,    lowerCAmelCase_ :						Union[float, List[float]]							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_							,    mean=lowerCAmelCase_							,    std=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Tuple							,    lowerCAmelCase_ :						ImageInput							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						float = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[ChannelDimension] = ChannelDimension.FIRST							,    )    ->							np.ndarray:


														"""simple docstring"""


														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

														if offset and not do_rescale:
																					raise ValueError('''For offset, do_rescale must also be set to True.'''      )

														# All transformations expect numpy arrays.
														_a			  =       to_numpy_array(lowerCAmelCase_      )

														if do_resize:
																					_a			  =       self.resize(image=lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_      )

														if do_center_crop:
																					_a			  =       self.center_crop(lowerCAmelCase_							,    size=lowerCAmelCase_      )

														if do_rescale:
																					_a			  =       self.rescale(image=lowerCAmelCase_							,    scale=lowerCAmelCase_							,    offset=lowerCAmelCase_      )

														if do_normalize:
																					_a			  =       self.normalize(image=lowerCAmelCase_							,    mean=lowerCAmelCase_							,    std=lowerCAmelCase_      )

														_a			  =       to_channel_dimension_format(lowerCAmelCase_							,    lowerCAmelCase_      )
														return image







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						ImageInput							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						float = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[str, TensorType]] = None							,    lowerCAmelCase_ :						ChannelDimension = ChannelDimension.FIRST							,    **lowerCAmelCase_ :						Any							,    )    ->							PIL.Image.Image:


														"""simple docstring"""


														_a			  =       do_resize if do_resize is not None else self.do_resize
														_a			  =       resample if resample is not None else self.resample
														_a			  =       do_center_crop if do_center_crop is not None else self.do_center_crop
														_a			  =       do_rescale if do_rescale is not None else self.do_rescale
														_a			  =       rescale_factor if rescale_factor is not None else self.rescale_factor
														_a			  =       offset if offset is not None else self.offset
														_a			  =       do_normalize if do_normalize is not None else self.do_normalize
														_a			  =       image_mean if image_mean is not None else self.image_mean
														_a			  =       image_std if image_std is not None else self.image_std

														_a			  =       size if size is not None else self.size
														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														_a			  =       crop_size if crop_size is not None else self.crop_size
														_a			  =       get_size_dict(lowerCAmelCase_							,    param_name='''crop_size'''      )

														if not valid_images(lowerCAmelCase_      ):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

														_a			  =       make_batched(lowerCAmelCase_      )

														_a			  =       [
														    [
														        self._preprocess_image(
														            image=lowerCAmelCase_							,    do_resize=lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_							,    do_center_crop=lowerCAmelCase_							,    crop_size=lowerCAmelCase_							,    do_rescale=lowerCAmelCase_							,    rescale_factor=lowerCAmelCase_							,    offset=lowerCAmelCase_							,    do_normalize=lowerCAmelCase_							,    image_mean=lowerCAmelCase_							,    image_std=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    )
														        for img in video
														    ]
														    for video in videos
														]

														_a			  =       {'''pixel_values''': videos}
														return BatchFeature(data=lowerCAmelCase_							,    tensor_type=lowerCAmelCase_      )

