





"""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  _SCREAMING_SNAKE_CASE(       A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  KandinskyImgaImgPipeline
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
       SCREAMING_SNAKE_CASE_       :				Dict     						=  [
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  [
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       SCREAMING_SNAKE_CASE_       :				Any     						=  False




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





             """simple docstring"""






             return 32




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





             """simple docstring"""






             return 32




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





             """simple docstring"""






             return self.time_input_dim




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





             """simple docstring"""






             return self.time_input_dim * 4




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





             """simple docstring"""






             return 1_00




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''							)
             return tokenizer




       @property
       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  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=10_05		,)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  MultilingualCLIP(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  text_encoder.eval()

             return text_encoder




       @property
       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             torch.manual_seed(0							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  {
                 '''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       :Optional[int]		    =  UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
             return model




       @property
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """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    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  VQModel(**self.dummy_movq_kwargs							)
             return model




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.dummy_text_encoder
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.dummy_tokenizer
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.dummy_unet
             __SCREAMING_SNAKE_CASE       :Any		    =  self.dummy_movq

             __SCREAMING_SNAKE_CASE       :Dict		    =  {
                 '''num_train_timesteps''': 10_00,
                 '''beta_schedule''': '''linear''',
                 '''beta_start''': 0.0_0_0_8_5,
                 '''beta_end''': 0.0_1_2,
                 '''clip_sample''': False,
                 '''set_alpha_to_one''': False,
                 '''steps_offset''': 0,
                 '''prediction_type''': '''epsilon''',
                 '''thresholding''': False,
             }

             __SCREAMING_SNAKE_CASE       :str		    =  DDIMScheduler(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''movq''': movq,
             }

             return components




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1							)							).to(SCREAMING_SNAKE_CASE__							)
             # create init_image
             __SCREAMING_SNAKE_CASE       :int		    =  floats_tensor((1, 3, 64, 64)		,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  image.cpu().permute(0		,2		,3		,1							)[0]
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert('''RGB'''							).resize((2_56, 2_56)							)

             if str(SCREAMING_SNAKE_CASE__							).startswith('''mps'''							):
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.manual_seed(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :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    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  '''cpu'''

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

             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Any		    =  pipe.to(SCREAMING_SNAKE_CASE__							)

             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

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

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

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

             __SCREAMING_SNAKE_CASE       :int		    =  np.array(
                 [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]							)
             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  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




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





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinsky/kandinsky_img2img_frog.npy'''							)

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

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

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  KandinskyImgaImgPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-1'''		,torch_dtype=torch.floataa							)
             __SCREAMING_SNAKE_CASE       :Any		    =  pipeline.to(SCREAMING_SNAKE_CASE__							)

             pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.Generator(device='''cpu'''							).manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :int		    =  pipe_prior(
                 SCREAMING_SNAKE_CASE__		,generator=SCREAMING_SNAKE_CASE__		,num_inference_steps=5		,negative_prompt=''''''		,).to_tuple()

             __SCREAMING_SNAKE_CASE       :Dict		    =  pipeline(
                 SCREAMING_SNAKE_CASE__		,image=SCREAMING_SNAKE_CASE__		,image_embeds=SCREAMING_SNAKE_CASE__		,negative_image_embeds=SCREAMING_SNAKE_CASE__		,generator=SCREAMING_SNAKE_CASE__		,num_inference_steps=1_00		,height=7_68		,width=7_68		,strength=0.2		,output_type='''np'''		,)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  output.images[0]

             assert image.shape == (7_68, 7_68, 3)

             assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str							)      ->				str:
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  (
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __SCREAMING_SNAKE_CASE       :list		    =  []
      for char_count in range(a_							):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]							)
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]							)
      return "".join(a_							)


if __name__ == "__main__":
  print(alternative_string_arrange("AB", "XYZ"), end=" ")