



"""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


__lowerCAmelCase							:       Optional[Any]          =       logging.get_logger(__name__)



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							['''pixel_values''']





		def __init__(       self				,     _lowercase = True				,     _lowercase = None				,     _lowercase = PILImageResampling.BICUBIC				,     _lowercase = True				,     _lowercase = None				,     _lowercase = True				,     _lowercase = 1 / 2_5_5				,     _lowercase = True				,     _lowercase = IMAGENET_DEFAULT_MEAN				,     _lowercase = IMAGENET_DEFAULT_STD				,     **_lowercase				,     )			->    None:







								'''simple docstring'''

								super().__init__(**_lowercase )
								snake_case_ :			Tuple            =      size if size is not None else {"""shortest_edge""": 2_2_4}
								snake_case_ :			Tuple            =      get_size_dict(_lowercase				,     default_to_square=_lowercase )
								snake_case_ :			Tuple            =      crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
								snake_case_ :			Optional[int]            =      get_size_dict(_lowercase				,     param_name="""crop_size""" )

								snake_case_ :			Union[str, Any]            =      do_resize
								snake_case_ :			List[Any]            =      size
								snake_case_ :			Optional[Any]            =      resample
								snake_case_ :			Tuple            =      do_center_crop
								snake_case_ :			Optional[Any]            =      crop_size
								snake_case_ :			List[str]            =      do_rescale
								snake_case_ :			int            =      rescale_factor
								snake_case_ :			Tuple            =      do_normalize
								snake_case_ :			str            =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								snake_case_ :			Any            =      image_std if image_std is not None else IMAGENET_DEFAULT_STD





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = PILImageResampling.BICUBIC				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      get_size_dict(_lowercase				,     default_to_square=_lowercase )
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
														snake_case_ :			Optional[Any]            =      int((2_5_6 / 2_2_4) * size["""shortest_edge"""] )
														snake_case_ :			Union[str, Any]            =      get_resize_output_image_size(_lowercase				,     size=_lowercase				,     default_to_square=_lowercase )
														snake_case_ :			Tuple            =      {"""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(
								    _lowercase				,     size=(size_dict["""height"""], size_dict["""width"""])				,     resample=_lowercase				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								snake_case_ :			str            =      get_size_dict(_lowercase )
								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(_lowercase				,     size=(size["""height"""], size["""width"""])				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

								return rescale(_lowercase				,     scale=_lowercase				,     data_format=_lowercase				,     **_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     **_lowercase				,     )			->    np.ndarray:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = None				,     _lowercase = ChannelDimension.FIRST				,     **_lowercase				,     )			->    BatchFeature:







								'''simple docstring'''

								snake_case_ :			List[str]            =      do_resize if do_resize is not None else self.do_resize
								snake_case_ :			str            =      resample if resample is not None else self.resample
								snake_case_ :			Dict            =      do_center_crop if do_center_crop is not None else self.do_center_crop
								snake_case_ :			Optional[int]            =      do_rescale if do_rescale is not None else self.do_rescale
								snake_case_ :			Any            =      rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case_ :			List[str]            =      do_normalize if do_normalize is not None else self.do_normalize
								snake_case_ :			Optional[int]            =      image_mean if image_mean is not None else self.image_mean
								snake_case_ :			Any            =      image_std if image_std is not None else self.image_std

								snake_case_ :			Optional[int]            =      size if size is not None else self.size
								snake_case_ :			List[str]            =      get_size_dict(_lowercase				,     default_to_square=_lowercase )
								snake_case_ :			Any            =      crop_size if crop_size is not None else self.crop_size
								snake_case_ :			Any            =      get_size_dict(_lowercase				,     param_name="""crop_size""" )

								snake_case_ :			Optional[Any]            =      make_list_of_images(_lowercase )

								if not valid_images(_lowercase ):
														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_ :			List[str]            =      [to_numpy_array(_lowercase ) for image in images]

								if do_resize:
														snake_case_ :			Any            =      [self.resize(_lowercase				,     _lowercase				,     _lowercase ) for image in images]

								if do_center_crop:
														snake_case_ :			Dict            =      [self.center_crop(_lowercase				,     _lowercase ) for image in images]

								if do_rescale:
														snake_case_ :			str            =      [self.rescale(_lowercase				,     _lowercase ) for image in images]

								if do_normalize:
														snake_case_ :			List[str]            =      [self.normalize(_lowercase				,     _lowercase				,     _lowercase ) for image in images]

								snake_case_ :			List[str]            =      [to_channel_dimension_format(_lowercase				,     _lowercase ) for image in images]

								snake_case_ :			List[Any]            =      {"""pixel_values""": images}
								return BatchFeature(data=_lowercase				,     tensor_type=_lowercase )






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case     :str			       =False



class        lowerCAmelCase__					(  unittest.TestCase       ):
				pass





@nightly
@require_torch_gpu
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(__UpperCamelCase       )
																		A      						=    VersatileDiffusionPipeline.from_pretrained(__UpperCamelCase						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    generator.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

											assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"


				def __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    'cyberpunk 2077'
											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt=__UpperCamelCase						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'						, ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-1

											A      						=    'A painting of a squirrel eating a burger '
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.text_to_image(
											    prompt=__UpperCamelCase						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-1

											A      						=    pipe.image_variation(__UpperCamelCase						, generator=__UpperCamelCase						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-1