"""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	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					DiTPipeline
    UpperCamelCase_	:					Tuple							=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    UpperCamelCase_	:					Union[str, Any]							=					PipelineTesterMixin.required_optional_params - {
        '''latents''',
        '''num_images_per_prompt''',
        '''callback''',
        '''callback_steps''',
    }
    UpperCamelCase_	:					int							=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    UpperCamelCase_	:					List[Any]							=					False
    def  lowercase					(		self							:     Tuple    )    ->       str:
         torch.manual_seed(0    )
         __snake_case				=							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=A_	,      activation_fn='''gelu-approximate'''	,      num_embeds_ada_norm=1_000	,      norm_type='''ada_norm_zero'''	,      norm_elementwise_affine=A_	,      )
         __snake_case				=							AutoencoderKL()
         __snake_case				=							DDIMScheduler()
         __snake_case				=							{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     Dict=0    )    ->       Any:
         if str(A_    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(A_    )
         else:
              __snake_case				=							torch.Generator(device=A_    ).manual_seed(A_    )
         __snake_case				=							{
             '''class_labels''': [1],
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs
    def  lowercase					(		self							:     Tuple    )    ->       str:
         __snake_case				=							'''cpu'''

         __snake_case				=							self.get_dummy_components()
         __snake_case				=							self.pipeline_class(**A_    )
         pipe.to(A_    )
         pipe.set_progress_bar_config(disable=A_    )

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

         self.assertEqual(image.shape	,      (1, 16, 16, 3)    )
         __snake_case				=							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]    )
         __snake_case				=							np.abs(image_slice.flatten() - expected_slice    ).max()
         self.assertLessEqual(A_	,      1E-3    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[int]:
         self._test_inference_batch_single_identical(relax_max_difference=A_	,      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  lowercase					(		self							:     List[str]    )    ->       List[str]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )






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




    """simple docstring"""




    def  lowercase					(		self							:     List[str]    )    ->       Any:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     str    )    ->       Optional[Any]:
         __snake_case				=							torch.manual_seed(0    )

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

         __snake_case				=							['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         __snake_case				=							pipe.get_label_ids(A_    )

         __snake_case				=							pipe(A_	,      generator=A_	,      num_inference_steps=40	,      output_type='''np'''    ).images

         for word, image in zip(A_	,      A_    ):
              __snake_case				=							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  lowercase					(		self							:     int    )    ->       Optional[int]:
         __snake_case				=							DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''    )
         __snake_case				=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
         pipe.to('''cuda'''    )

         __snake_case				=							['''vase''', '''umbrella''']
         __snake_case				=							pipe.get_label_ids(A_    )

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

         for word, image in zip(A_	,      A_    ):
              __snake_case				=							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, Iterable, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase		:		str														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					['''pixel_values''']
    def __init__(		self							:     Optional[int]	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN	,      A_							:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD	,      **A_							:     Dict	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 224}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )

         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     int	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
              __snake_case				=							int((256 / 224) * size['''shortest_edge''']    )
              __snake_case				=							get_resize_output_image_size(A_	,      size=A_	,      default_to_square=A_    )
              __snake_case				=							{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
              raise ValueError(
                  f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"    )
         return resize(
             A_	,      size=(size_dict['''height'''], size_dict['''width'''])	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[Any]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     np.ndarray	,      A_							:     Union[int, float]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[Any]	,      )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )







    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[Union[float, Iterable[float]]] = None	,      A_							:     Optional[TensorType] = None	,      A_							:     ChannelDimension = ChannelDimension.FIRST	,      **A_							:     Any	,      )    ->       BatchFeature:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

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

         __snake_case				=							make_list_of_images(A_    )

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

         if do_resize and size is None:
              raise ValueError('''Size 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.'''    )

         # All transformations expect numpy arrays.
         __snake_case				=							[to_numpy_array(A_    ) for image in images]

         if do_resize:
              __snake_case				=							[self.resize(A_	,      A_	,      A_    ) for image in images]

         if do_center_crop:
              __snake_case				=							[self.center_crop(A_	,      A_    ) for image in images]

         if do_rescale:
              __snake_case				=							[self.rescale(A_	,      A_    ) for image in images]

         if do_normalize:
              __snake_case				=							[self.normalize(A_	,      A_	,      A_    ) for image in images]

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )