'''simple docstring'''

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 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   KandinskyInpaintPipeline
      UpperCAmelCase_      :      Optional[Any]      							=   ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_      :      Union[str, Any]      							=   [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_      :      str      							=   [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_      :      Dict      							=   False




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return self.time_input_dim




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''							)
         return tokenizer




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				Dict   				=  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					,)

         UpperCAmelCase__							:				Optional[Any]   				=  MultilingualCLIP(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  text_encoder.eval()

         return text_encoder




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         torch.manual_seed(0							)

         UpperCAmelCase__							:				Tuple   				=  {
             '''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,
         }

         UpperCAmelCase__							:				int   				=  UNetaDConditionModel(**lowerCamelCase_							)
         return model




      @property
      def 		lowerCAmelCase__					(				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 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				int   				=  VQModel(**self.dummy_movq_kwargs							)
         return model




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.dummy_text_encoder
         UpperCAmelCase__							:				Optional[int]   				=  self.dummy_tokenizer
         UpperCAmelCase__							:				List[Any]   				=  self.dummy_unet
         UpperCAmelCase__							:				Tuple   				=  self.dummy_movq

         UpperCAmelCase__							:				Union[str, Any]   				=  DDIMScheduler(
             num_train_timesteps=1000					,beta_schedule='''linear'''					,beta_start=0.0_0085					,beta_end=0.012					,clip_sample=lowerCamelCase_					,set_alpha_to_one=lowerCamelCase_					,steps_offset=1					,prediction_type='''epsilon'''					,thresholding=lowerCamelCase_					,)

         UpperCAmelCase__							:				Optional[int]   				=  {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			List[str]:



         '''simple docstring'''



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

         if str(lowerCamelCase_							).startswith('''mps'''							):
            UpperCAmelCase__							:				int   				=  torch.manual_seed(lowerCamelCase_							)
         else:
            UpperCAmelCase__							:				Optional[int]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  {
             '''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 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  '''cpu'''

         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_dummy_components()

         UpperCAmelCase__							:				Tuple   				=  self.pipeline_class(**lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  pipe.to(lowerCamelCase_							)

         pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  pipe(**self.get_dummy_inputs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				List[Any]   				=  output.images

         UpperCAmelCase__							:				str   				=  pipe(
             **self.get_dummy_inputs(lowerCamelCase_							)					,return_dict=lowerCamelCase_					,)[0]

         UpperCAmelCase__							:				Union[str, Any]   				=  image[0, -3:, -3:, -1]
         UpperCAmelCase__							:				Optional[Any]   				=  image_from_tuple[0, -3:, -3:, -1]

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

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

         UpperCAmelCase__							:				List[str]   				=  np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]							)

         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 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



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




@slow
@require_torch_gpu
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



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

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

         UpperCAmelCase__							:				List[Any]   				=  '''a hat'''

         UpperCAmelCase__							:				List[str]   				=  KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''					,torch_dtype=torch.floataa							)
         pipe_prior.to(lowerCamelCase_							)

         UpperCAmelCase__							:				Dict   				=  KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''					,torch_dtype=torch.floataa							)
         UpperCAmelCase__							:				List[Any]   				=  pipeline.to(lowerCamelCase_							)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)
         UpperCAmelCase__							:				Optional[int]   				=  pipe_prior(
             lowerCamelCase_					,generator=lowerCamelCase_					,num_inference_steps=5					,negative_prompt=''''''					,).to_tuple()

         UpperCAmelCase__							:				Union[str, Any]   				=  pipeline(
             lowerCamelCase_					,image=lowerCamelCase_					,mask_image=lowerCamelCase_					,image_embeds=lowerCamelCase_					,negative_image_embeds=lowerCamelCase_					,generator=lowerCamelCase_					,num_inference_steps=100					,height=768					,width=768					,output_type='''np'''					,)

         UpperCAmelCase__							:				Tuple   				=  output.images[0]

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

         assert_mean_pixel_difference(lowerCamelCase_					,lowerCamelCase_							)





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   ['''image_processor''', '''tokenizer''']
      UpperCAmelCase_      :      str      							=   '''CLIPImageProcessor'''
      UpperCAmelCase_      :      Dict      							=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''					,lowerCamelCase_					,)
            UpperCAmelCase__							:				str   				=  kwargs.pop('''feature_extractor'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''							)
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''							)

         super().__init__(lowerCamelCase_					,lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''							)

         if text is not None:
            UpperCAmelCase__							:				Tuple   				=  self.tokenizer(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if images is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if text is not None and images is not None:
            UpperCAmelCase__							:				str   				=  image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**lowerCamelCase_							)					,tensor_type=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.batch_decode(*lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.decode(*lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.tokenizer.model_input_names
         UpperCAmelCase__							:				Optional[Any]   				=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowerCamelCase_					,)
         return self.image_processor_class




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowerCamelCase_					,)
         return self.image_processor




