

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_SCREAMING_SNAKE_CASE  :						Dict         =    logging.get_logger(__name__)


if is_vision_available():
       import PIL






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      ["pixel_values"]






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

       SCREAMING_SNAKE_CASE__					=					do_resize
       SCREAMING_SNAKE_CASE__					=					size
       SCREAMING_SNAKE_CASE__					=					resample
       SCREAMING_SNAKE_CASE__					=					do_center_crop
       SCREAMING_SNAKE_CASE__					=					crop_size
       SCREAMING_SNAKE_CASE__					=					do_rescale
       SCREAMING_SNAKE_CASE__					=					rescale_factor
       SCREAMING_SNAKE_CASE__					=					do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else OPENAI_CLIP_STD
       SCREAMING_SNAKE_CASE__					=					do_convert_rgb






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BICUBIC			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	str			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
       if "shortest_edge" not in size:
            raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''      )
       SCREAMING_SNAKE_CASE__					=					get_resize_output_image_size(__lowerCamelCase			,     size=size['''shortest_edge''']			,     default_to_square=__lowerCamelCase      )
       return resize(__lowerCamelCase			,     size=__lowerCamelCase			,     resample=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[int]			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''      )
       return center_crop(__lowerCamelCase			,     size=(size['''height'''], size['''width'''])			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[int, float]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[int]			,     )	->							List[Any]:
       return rescale(__lowerCamelCase			,     scale=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[float, List[float]]			,     __lowerCamelCase		:	Union[float, List[float]]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Tuple			,     )	->							np.ndarray:
       return normalize(__lowerCamelCase			,     mean=__lowerCamelCase			,     std=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	ImageInput			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	PILImageResampling = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	int = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	float = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Optional[Union[float, List[float]]] = None			,     __lowerCamelCase		:	Optional[Union[float, List[float]]] = None			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Optional[Union[str, TensorType]] = None			,     __lowerCamelCase		:	Optional[ChannelDimension] = ChannelDimension.FIRST			,     **__lowerCamelCase		:	Union[str, Any]			,     )	->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__					=					do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__					=					size if size is not None else self.size
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase			,     param_name='''size'''			,     default_to_square=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__					=					do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__					=					crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase			,     param_name='''crop_size'''			,     default_to_square=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__					=					rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__					=					do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE__					=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       SCREAMING_SNAKE_CASE__					=					make_list_of_images(__lowerCamelCase      )

       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.'''      )

       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.'''      )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
            SCREAMING_SNAKE_CASE__					=					[convert_to_rgb(__lowerCamelCase      ) for image in images]

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

       if do_resize:
            SCREAMING_SNAKE_CASE__					=					[self.resize(image=__lowerCamelCase			,     size=__lowerCamelCase			,     resample=__lowerCamelCase      ) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__					=					[self.center_crop(image=__lowerCamelCase			,     size=__lowerCamelCase      ) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__					=					[self.rescale(image=__lowerCamelCase			,     scale=__lowerCamelCase      ) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__					=					[self.normalize(image=__lowerCamelCase			,     mean=__lowerCamelCase			,     std=__lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__					=					[to_channel_dimension_format(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase			,     tensor_type=__lowerCamelCase      )





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      KandinskyImgaImgPipeline
  a     =      ["prompt", "image_embeds", "negative_image_embeds", "image"]
  a     =      [
      "prompt",
      "negative_prompt",
      "image_embeds",
      "negative_image_embeds",
      "image",
  ]
  a     =      [
      "generator",
      "height",
      "width",
      "strength",
      "guidance_scale",
      "negative_prompt",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  a     =      False






  @property
  def    lowercase_	(							self		:	str      )	->							List[str]:
       return 32






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							int:
       return 32






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       return self.time_input_dim






  @property
  def    lowercase_	(							self		:	List[str]      )	->							int:
       return self.time_input_dim * 4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							Any:
       return 100






  @property
  def    lowercase_	(							self		:	Any      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
       return tokenizer






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					MCLIPConfig(
           numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=37			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1005			,     )

       SCREAMING_SNAKE_CASE__					=					MultilingualCLIP(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder.eval()

       return text_encoder






  @property
  def    lowercase_	(							self		:	str      )	->							Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					{
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''text_image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''text_image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(**__lowerCamelCase      )
       return model






  @property
  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






  @property
  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					VQModel(**self.dummy_movq_kwargs      )
       return model






  def    lowercase_	(							self		:	int      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__					=					self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__					=					self.dummy_unet
       SCREAMING_SNAKE_CASE__					=					self.dummy_movq

       SCREAMING_SNAKE_CASE__					=					{
           '''num_train_timesteps''': 1000,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					{
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]=0      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(__lowerCamelCase      )
       # create init_image
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 64, 64)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image.cpu().permute(0			,     2			,     3			,     1      )[0]
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(np.uinta(__lowerCamelCase      )      ).convert('''RGB'''      ).resize((256, 256)      )

       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs







  def    lowercase_	(							self		:	int      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()

       SCREAMING_SNAKE_CASE__					=					self.pipeline_class(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )

       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					pipe(**self.get_dummy_inputs(__lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					pipe(
           **self.get_dummy_inputs(__lowerCamelCase      )			,     return_dict=__lowerCamelCase			,     )[0]

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
       assert (
           np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__					=					'''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__					=					KandinskyPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-prior'''			,     torch_dtype=torch.floataa      )
       pipe_prior.to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					KandinskyImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1'''			,     torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__					=					pipeline.to(__lowerCamelCase      )

       pipeline.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					pipe_prior(
           __lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=5			,     negative_prompt=''''''			,     ).to_tuple()

       SCREAMING_SNAKE_CASE__					=					pipeline(
           __lowerCamelCase			,     image=__lowerCamelCase			,     image_embeds=__lowerCamelCase			,     negative_image_embeds=__lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=100			,     height=768			,     width=768			,     strength=0.2			,     output_type='''np'''			,     )

       SCREAMING_SNAKE_CASE__					=					output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__lowerCamelCase			,     __lowerCamelCase      )


