
import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		KandinskyInpaintPipeline
       UpperCamelCase_   =		['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
       UpperCamelCase_   =		[
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
           '''mask_image''',
       ]
       UpperCamelCase_   =		[
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase_   =		False
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




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





         """simple docstring"""




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





         """simple docstring"""




         return self.time_input_dim
       @property
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




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





         """simple docstring"""




         return 100
       @property
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Tuple       =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''       )
         return tokenizer
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         torch.manual_seed(0       )
         _lowercase:			Union[str, Any]       =     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 ,				)

         _lowercase:			Union[str, Any]       =     MultilingualCLIP(A_       )
         _lowercase:			Union[str, Any]       =     text_encoder.eval()

         return text_encoder
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         torch.manual_seed(0       )

         _lowercase:			Union[str, Any]       =     {
             '''in_channels''': 9,
             # 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,
         }

         _lowercase:			int       =     UNetaDConditionModel(**A_       )
         return model
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         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       )     ->	Optional[Any]:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:			Dict       =     self.dummy_text_encoder
         _lowercase:			List[str]       =     self.dummy_tokenizer
         _lowercase:			List[Any]       =     self.dummy_unet
         _lowercase:			Optional[Any]       =     self.dummy_movq

         _lowercase:			Dict       =     DDIMScheduler(
             num_train_timesteps=1000 ,				beta_schedule='''linear''' ,				beta_start=0.0_00_85 ,				beta_end=0.0_12 ,				clip_sample=A_ ,				set_alpha_to_one=A_ ,				steps_offset=1 ,				prediction_type='''epsilon''' ,				thresholding=A_ ,				)

         _lowercase:			List[Any]       =     {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components
       def 							lowercase_							(     self ,				A_ ,				A_=0       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(A_       )       ).to(A_       )
         _lowercase:			Tuple       =     floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(seed + 1       )       ).to(A_       )
         # create init_image
         _lowercase:			int       =     floats_tensor((1, 3, 64, 64) ,				rng=random.Random(A_       )       ).to(A_       )
         _lowercase:			int       =     image.cpu().permute(0 ,				2 ,				3 ,				1       )[0]
         _lowercase:			Tuple       =     Image.fromarray(np.uinta(A_       )       ).convert('''RGB'''       ).resize((256, 256)       )
         # create mask
         _lowercase:			Optional[Any]       =     np.ones((64, 64) ,				dtype=np.floataa       )
         _lowercase:			Any       =     0

         if str(A_       ).startswith('''mps'''       ):
           _lowercase:			str       =     torch.manual_seed(A_       )
         else:
           _lowercase:			Tuple       =     torch.Generator(device=A_       ).manual_seed(A_       )
         _lowercase:			Optional[Any]       =     {
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Optional[int]       =     '''cpu'''

         _lowercase:			Optional[Any]       =     self.get_dummy_components()

         _lowercase:			Optional[Any]       =     self.pipeline_class(**A_       )
         _lowercase:			Dict       =     pipe.to(A_       )

         pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			Dict       =     pipe(**self.get_dummy_inputs(A_       )       )
         _lowercase:			Tuple       =     output.images

         _lowercase:			List[Any]       =     pipe(
             **self.get_dummy_inputs(A_       ) ,				return_dict=A_ ,				)[0]

         _lowercase:			int       =     image[0, -3:, -3:, -1]
         _lowercase:			str       =     image_from_tuple[0, -3:, -3:, -1]

         print(f'''image.shape {image.shape}'''       )

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

         _lowercase:			Union[str, Any]       =     np.array(
             [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]       )

         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()}'''



       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )


@slow
@require_torch_gpu
class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




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



       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''       )

         _lowercase:			int       =     load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
         _lowercase:			Optional[Any]       =     np.ones((768, 768) ,				dtype=np.floataa       )
         _lowercase:			Any       =     0

         _lowercase:			Optional[int]       =     '''a hat'''

         _lowercase:			Tuple       =     KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior''' ,				torch_dtype=torch.floataa       )
         pipe_prior.to(A_       )

         _lowercase:			Any       =     KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint''' ,				torch_dtype=torch.floataa       )
         _lowercase:			Optional[int]       =     pipeline.to(A_       )
         pipeline.set_progress_bar_config(disable=A_       )

         _lowercase:			str       =     torch.Generator(device='''cpu'''       ).manual_seed(0       )
         _lowercase:			List[Any]       =     pipe_prior(
             A_ ,				generator=A_ ,				num_inference_steps=5 ,				negative_prompt='''''' ,				).to_tuple()

         _lowercase:			List[str]       =     pipeline(
             A_ ,				image=A_ ,				mask_image=A_ ,				image_embeds=A_ ,				negative_image_embeds=A_ ,				generator=A_ ,				num_inference_steps=100 ,				height=768 ,				width=768 ,				output_type='''np''' ,				)

         _lowercase:			Dict       =     output.images[0]

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

         assert_mean_pixel_difference(A_ ,				A_       )


"""simple docstring"""




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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A__   :					Dict							=   logging.get_logger(__name__)


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		['''pixel_values''']
       def __init__(     self ,				A_ = True ,				A_ = None ,				A_ = PILImageResampling.BICUBIC ,				A_ = True ,				A_ = True ,				A_ = 1 / 255 ,				A_ = None ,				A_ = True ,				A_ = None ,				A_ = None ,				**A_ ,				)     ->	None:





         """simple docstring"""




         super().__init__(**A_       )
         _lowercase:			List[str]       =     size if size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			Dict       =     get_size_dict(A_       )
         _lowercase:			Any       =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			List[str]       =     get_size_dict(A_ ,				default_to_square=A_ ,				param_name='''crop_size'''       )

         _lowercase:			List[Any]       =     do_resize
         _lowercase:			Tuple       =     do_rescale
         _lowercase:			Union[str, Any]       =     do_normalize
         _lowercase:			Optional[int]       =     do_center_crop
         _lowercase:			List[str]       =     crop_size
         _lowercase:			int       =     size
         _lowercase:			List[Any]       =     resample
         _lowercase:			Optional[Any]       =     rescale_factor
         _lowercase:			str       =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         _lowercase:			Dict       =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = PILImageResampling.BILINEAR ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     get_size_dict(A_       )

         if "shortest_edge" in size:
           _lowercase:			Optional[int]       =     get_resize_output_image_size(A_ ,				size=size['''shortest_edge'''] ,				default_to_square=A_       )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           _lowercase:			Dict       =     (size['''height'''], size['''width'''])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
         return resize(A_ ,				size=A_ ,				resample=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Dict       =     get_size_dict(A_       )
         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(A_ ,				size=(size['''height'''], size['''width''']) ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_       )     ->	np.ndarray:





         """simple docstring"""




         return rescale(A_ ,				scale=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




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



       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = ChannelDimension.FIRST ,				**A_ ,				)     ->	BatchFeature:





         """simple docstring"""




         _lowercase:			List[Any]       =     do_resize if do_resize is not None else self.do_resize
         _lowercase:			Tuple       =     do_rescale if do_rescale is not None else self.do_rescale
         _lowercase:			Optional[Any]       =     do_normalize if do_normalize is not None else self.do_normalize
         _lowercase:			Optional[int]       =     do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase:			Optional[int]       =     crop_size if crop_size is not None else self.crop_size
         _lowercase:			List[Any]       =     get_size_dict(A_ ,				param_name='''crop_size''' ,				default_to_square=A_       )
         _lowercase:			List[str]       =     resample if resample is not None else self.resample
         _lowercase:			List[str]       =     rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase:			List[Any]       =     image_mean if image_mean is not None else self.image_mean
         _lowercase:			List[str]       =     image_std if image_std is not None else self.image_std

         _lowercase:			List[Any]       =     size if size is not None else self.size
         _lowercase:			List[str]       =     get_size_dict(A_       )

         if not is_batched(A_       ):
           _lowercase:			Optional[Any]       =     [images]

         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.'''       )

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

         if do_resize:
           _lowercase:			int       =     [self.resize(image=A_ ,				size=A_ ,				resample=A_       ) for image in images]

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

         if do_rescale:
           _lowercase:			Optional[Any]       =     [self.rescale(image=A_ ,				scale=A_       ) for image in images]

         if do_normalize:
           _lowercase:			List[Any]       =     [self.normalize(image=A_ ,				mean=A_ ,				std=A_       ) for image in images]

         _lowercase:			Optional[Any]       =     [to_channel_dimension_format(A_ ,				A_       ) for image in images]

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