
"""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 A_	(SCREAMING_SNAKE_CASE_				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		KandinskyImgaImgPipeline
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		["prompt", "image_embeds", "negative_image_embeds", "image"]
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		[
      "prompt",
      "negative_prompt",
      "image_embeds",
      "negative_image_embeds",
      "image",
  ]
  SCREAMING_SNAKE_CASE__			:						Dict       =		[
      "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__			:						Union[str, Any]       =		False







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 32







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 32







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return self.time_input_dim







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return self.time_input_dim * 4







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 100







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
     return tokenizer







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )
     UpperCAmelCase_					:					List[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				,   )

     UpperCAmelCase_					:					int			     =    MultilingualCLIP(a__  )
     UpperCAmelCase_					:					Union[str, Any]			     =    text_encoder.eval()

     return text_encoder







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     torch.manual_seed(0  )

     UpperCAmelCase_					:					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,
     }

     UpperCAmelCase_					:					Optional[Any]			     =    UNetaDConditionModel(**a__  )
     return model







  @property
  def        UpperCamelCase__					(		self  ):

     """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  ):

     """simple docstring"""
     torch.manual_seed(0  )
     UpperCAmelCase_					:					str			     =    VQModel(**self.dummy_movq_kwargs  )
     return model







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.dummy_text_encoder
     UpperCAmelCase_					:					List[Any]			     =    self.dummy_tokenizer
     UpperCAmelCase_					:					int			     =    self.dummy_unet
     UpperCAmelCase_					:					Dict			     =    self.dummy_movq

     UpperCAmelCase_					:					Tuple			     =    {
         """num_train_timesteps""": 1000,
         """beta_schedule""": """linear""",
         """beta_start""": 0.0_00_85,
         """beta_end""": 0.0_12,
         """clip_sample""": False,
         """set_alpha_to_one""": False,
         """steps_offset""": 0,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
     }

     UpperCAmelCase_					:					Optional[Any]			     =    DDIMScheduler(**a__  )

     UpperCAmelCase_					:					List[Any]			     =    {
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }

     return components







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_=0  ):

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

     if str(a__  ).startswith("mps"  ):
        UpperCAmelCase_					:					List[Any]			     =    torch.manual_seed(a__  )
     else:
        UpperCAmelCase_					:					Tuple			     =    torch.Generator(device=a__  ).manual_seed(a__  )
     UpperCAmelCase_					:					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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    """cpu"""

     UpperCAmelCase_					:					int			     =    self.get_dummy_components()

     UpperCAmelCase_					:					int			     =    self.pipeline_class(**a__  )
     UpperCAmelCase_					:					Optional[int]			     =    pipe.to(a__  )

     pipe.set_progress_bar_config(disable=a__  )

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

     UpperCAmelCase_					:					Tuple			     =    pipe(
         **self.get_dummy_inputs(a__  )				,   return_dict=a__				,   )[0]

     UpperCAmelCase_					:					Dict			     =    image[0, -3:, -3:, -1]
     UpperCAmelCase_					:					List[Any]			     =    image_from_tuple[0, -3:, -3:, -1]

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

     UpperCAmelCase_					:					str			     =    np.array(
         [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]  )
     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 A_	(unittest.TestCase      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







  def        UpperCamelCase__					(		self  ):

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

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

     UpperCAmelCase_					:					int			     =    KandinskyPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1-prior"				,   torch_dtype=torch.floataa  )
     pipe_prior.to(a__  )

     UpperCAmelCase_					:					Tuple			     =    KandinskyImgaImgPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1"				,   torch_dtype=torch.floataa  )
     UpperCAmelCase_					:					Any			     =    pipeline.to(a__  )

     pipeline.set_progress_bar_config(disable=a__  )

     UpperCAmelCase_					:					Any			     =    torch.Generator(device="cpu"  ).manual_seed(0  )
     UpperCAmelCase_					:					Dict			     =    pipe_prior(
         a__				,   generator=a__				,   num_inference_steps=5				,   negative_prompt=""				,   ).to_tuple()

     UpperCAmelCase_					:					Union[str, Any]			     =    pipeline(
         a__				,   image=a__				,   image_embeds=a__				,   negative_image_embeds=a__				,   generator=a__				,   num_inference_steps=100				,   height=768				,   width=768				,   strength=0.2				,   output_type="np"				,   )

     UpperCAmelCase_					:					Dict			     =    output.images[0]

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

     assert_mean_pixel_difference(a__				,   a__  )



"""simple docstring"""




from __future__ import annotations

_a      :		List[str]           =   10





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     1
  _lowerCAmelCase						:       Union[str, Any]  			=     max(_lowerCamelCase	)
  while placement <= max_digit:
    # declare and initialize empty buckets
    _lowerCAmelCase						:       list[list]  			=     [[] for _ in range(_lowerCamelCase	)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      _lowerCAmelCase						:       Tuple  			=     int((i / placement) % RADIX	)
      buckets[tmp].append(_lowerCamelCase	)
    # put each buckets' contents into list_of_ints
    _lowerCAmelCase						:       List[str]  			=     0
    for b in range(_lowerCamelCase	):
      for i in buckets[b]:
        _lowerCAmelCase						:       Any  			=     i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
     import doctest

     doctest.testmod()




