






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 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




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







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




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		Dict    )       ->Optional[int]:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """simple docstring"""


       return self.time_input_dim







      @property
      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Tuple:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[int]:




       """simple docstring"""


       return 100







      @property
      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[Any]:




       """simple docstring"""


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







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




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = 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=1_005					,   )

       a						    = MultilingualCLIP(__UpperCAmelCase    )
       a						    = text_encoder.eval()

       return text_encoder







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




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = {
           '''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,
       }

       a						    = UNetaDConditionModel(**__UpperCAmelCase    )
       return model







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->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 :		List[Any]    )       ->int:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = VQModel(**self.dummy_movq_kwargs    )
       return model







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = self.dummy_text_encoder
       a						    = self.dummy_tokenizer
       a						    = self.dummy_unet
       a						    = self.dummy_movq

       a						    = {
           '''num_train_timesteps''': 1_000,
           '''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,
       }

       a						    = DDIMScheduler(**__UpperCAmelCase    )

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

       return components







      def  __lowerCAmelCase   ( self :		int					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Tuple=0    )       ->Any:




       """simple docstring"""


       a						    = floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(seed + 1    )    ).to(__UpperCAmelCase    )
       # create init_image
       a						    = floats_tensor((1, 3, 64, 64)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = image.cpu().permute(0					,   2					,   3					,   1    )[0]
       a						    = Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )

       if str(__UpperCAmelCase    ).startswith('''mps'''    ):
        a						    = torch.manual_seed(__UpperCAmelCase    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
       a						    = {
           '''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  __lowerCAmelCase   ( self :		str    )       ->int:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.get_dummy_components()

       a						    = self.pipeline_class(**__UpperCAmelCase    )
       a						    = pipe.to(__UpperCAmelCase    )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
       a						    = output.images

       a						    = pipe(
           **self.get_dummy_inputs(__UpperCAmelCase    )					,   return_dict=__UpperCAmelCase					,   )[0]

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

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

       a						    = 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 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->str:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Any:




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_img2img_frog.npy'''    )

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

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

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

       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

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

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

       a						    = output.images[0]

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

       assert_mean_pixel_difference(__UpperCAmelCase					,   __UpperCAmelCase    )










from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						lowercase_   :






      '''simple docstring'''




      pass


