


'''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, 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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , 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        _a							(							self :       str ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        return self.time_input_dim






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        return 100






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  int          =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
        return tokenizer






  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				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	,)

        A_						:  Any          =				MultilingualCLIP(UpperCamelCase__ )
        A_						:  Optional[Any]          =				text_encoder.eval()

        return text_encoder






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  str          =				{
            """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_						:  Tuple          =				UNetaDConditionModel(**UpperCamelCase__ )
        return model






  @property
  def        _a							(							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        _a							(							self :       Any ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Any          =				VQModel(**self.dummy_movq_kwargs )
        return model






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




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

        A_						:  List[str]          =				{
            """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,
        }

        A_						:  int          =				DDIMScheduler(**UpperCamelCase__ )

        A_						:  List[str]          =				{
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components






  def        _a							(							self :       List[str]	,_a :       Union[str, Any]	,_a :       int=0 ):

        '''simple docstring'''




        A_						:  List[str]          =				floats_tensor((1, self.cross_attention_dim)	,rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
        A_						:  Optional[int]          =				floats_tensor((1, self.cross_attention_dim)	,rng=random.Random(seed + 1 ) ).to(UpperCamelCase__ )
        # create init_image
        A_						:  Tuple          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
        A_						:  int          =				image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  str          =				Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert("""RGB""" ).resize((256, 256) )

        if str(UpperCamelCase__ ).startswith("""mps""" ):
              A_						:  Union[str, Any]          =				torch.manual_seed(UpperCamelCase__ )
        else:
              A_						:  Any          =				torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
        A_						:  Optional[Any]          =				{
            """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        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				"""cpu"""

        A_						:  List[str]          =				self.get_dummy_components()

        A_						:  Union[str, Any]          =				self.pipeline_class(**UpperCamelCase__ )
        A_						:  Tuple          =				pipe.to(UpperCamelCase__ )

        pipe.set_progress_bar_config(disable=UpperCamelCase__ )

        A_						:  Tuple          =				pipe(**self.get_dummy_inputs(UpperCamelCase__ ) )
        A_						:  List[Any]          =				output.images

        A_						:  Dict          =				pipe(
            **self.get_dummy_inputs(UpperCamelCase__ )	,return_dict=UpperCamelCase__	,)[0]

        A_						:  Optional[int]          =				image[0, -3:, -3:, -1]
        A_						:  int          =				image_from_tuple[0, -3:, -3:, -1]

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

        A_						:  Optional[int]          =				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 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       str ):

        '''simple docstring'''




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






  def        _a							(							self :       int ):

        '''simple docstring'''




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

        A_						:  Optional[Any]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
        A_						:  Any          =				"""A red cartoon frog, 4k"""

        A_						:  Optional[int]          =				KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""	,torch_dtype=torch.floataa )
        pipe_prior.to(UpperCamelCase__ )

        A_						:  List[str]          =				KandinskyImgaImgPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1"""	,torch_dtype=torch.floataa )
        A_						:  List[str]          =				pipeline.to(UpperCamelCase__ )

        pipeline.set_progress_bar_config(disable=UpperCamelCase__ )

        A_						:  Any          =				torch.Generator(device="""cpu""" ).manual_seed(0 )
        A_   ,					A_						:  Union[str, Any]          =				pipe_prior(
            UpperCamelCase__	,generator=UpperCamelCase__	,num_inference_steps=5	,negative_prompt=""""""	,).to_tuple()

        A_						:  Dict          =				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_						:  str          =				output.images[0]

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

        assert_mean_pixel_difference(UpperCamelCase__	,UpperCamelCase__ )




'''simple docstring'''


import baseaa




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return baseaa.aaaencode(string.encode("""utf-8"""))




def      lowerCamelCase					(      lowerCamelCase  :      bytes):
      return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")


if __name__ == "__main__":
     import doctest

     doctest.testmod()
