





'''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    (UpperCAmelCase__					,		unittest.TestCase							):
     lowerCAmelCase					=       KandinskyImgaImgPipeline
     lowerCAmelCase					=       ["prompt", "image_embeds", "negative_image_embeds", "image"]
     lowerCAmelCase					=       [
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     lowerCAmelCase					=       [
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowerCAmelCase					=       False


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return 32


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return 32


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.time_input_dim


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.time_input_dim * 4


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return 100


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						: 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						,	)

          __A						: Dict      =       MultilingualCLIP(_UpperCAmelCase)
          __A						: Optional[int]      =       text_encoder.eval()

          return text_encoder


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)

          __A						: Optional[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,
          }

          __A						: Optional[int]      =       UNetaDConditionModel(**_UpperCAmelCase)
          return model


     @property
     def 							SCREAMING_SNAKE_CASE  (						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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.dummy_text_encoder
          __A						: List[Any]      =       self.dummy_tokenizer
          __A						: Optional[int]      =       self.dummy_unet
          __A						: str      =       self.dummy_movq

          __A						: int      =       {
              """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						: Union[str, Any]      =       DDIMScheduler(**_UpperCAmelCase)

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

          return components


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase=0):

          '''simple docstring'''






          __A						: List[Any]      =       floats_tensor((1, self.cross_attention_dim)						,	rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
          __A						: List[Any]      =       floats_tensor((1, self.cross_attention_dim)						,	rng=random.Random(seed + 1)).to(_UpperCAmelCase)
          # create init_image
          __A						: Union[str, Any]      =       floats_tensor((1, 3, 64, 64)						,	rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
          __A						: Dict      =       image.cpu().permute(0						,	2						,	3						,	1)[0]
          __A						: Union[str, Any]      =       Image.fromarray(np.uinta(_UpperCAmelCase)).convert('RGB').resize((256, 256))

          if str(_UpperCAmelCase).startswith('mps'):
               __A						: Optional[Any]      =       torch.manual_seed(_UpperCAmelCase)
          else:
               __A						: Tuple      =       torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						: Dict      =       {
              """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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       """cpu"""

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

          __A						: Optional[Any]      =       self.pipeline_class(**_UpperCAmelCase)
          __A						: int      =       pipe.to(_UpperCAmelCase)

          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						: str      =       pipe(**self.get_dummy_inputs(_UpperCAmelCase))
          __A						: Optional[Any]      =       output.images

          __A						: Tuple      =       pipe(
              **self.get_dummy_inputs(_UpperCAmelCase)						,	return_dict=_UpperCAmelCase						,	)[0]

          __A						: str      =       image[0, -3:, -3:, -1]
          __A						: List[Any]      =       image_from_tuple[0, -3:, -3:, -1]

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

          __A						: 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  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

          __A						: int      =       KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'						,	torch_dtype=torch.floataa)
          pipe_prior.to(_UpperCAmelCase)

          __A						: Optional[int]      =       KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'						,	torch_dtype=torch.floataa)
          __A						: int      =       pipeline.to(_UpperCAmelCase)

          pipeline.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						: Tuple      =       torch.Generator(device='cpu').manual_seed(0)
          __A						: Any      =       pipe_prior(
              _UpperCAmelCase						,	generator=_UpperCAmelCase						,	num_inference_steps=5						,	negative_prompt=''						,	).to_tuple()

          __A						: int      =       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						: Optional[Any]      =       output.images[0]

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

          assert_mean_pixel_difference(_UpperCAmelCase						,	_UpperCAmelCase)



'''simple docstring'''







from __future__ import annotations

from typing import TypedDict







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str
      __UpperCamelCase       :  int


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			list[str]:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )

       return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase  )  )]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			BWTTransformDict:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )
       if not s:
              raise ValueError("""The parameter s must not be empty."""  )

       _a    : List[Any]     					=all_rotations(_UpperCAmelCase  )
       rotations.sort()  # sort the list of rotations in alphabetically order
       # make a string composed of the last char of each rotation
       _a    : BWTTransformDict     					={
           "bwt_string": "".join([word[-1] for word in rotations]  ),
           "idx_original_string": rotations.index(_UpperCAmelCase  ),
       }
       return response


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	int  )  ->			str:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter bwt_string type must be str."""  )
       if not bwt_string:
              raise ValueError("""The parameter bwt_string must not be empty."""  )
       try:
              _a    : List[str]     					=int(_UpperCAmelCase  )
       except ValueError:
              raise TypeError(
                  """The parameter idx_original_string type must be int or passive"""
                  """ of cast to int."""  )
       if idx_original_string < 0:
              raise ValueError("""The parameter idx_original_string must not be lower than 0."""  )
       if idx_original_string >= len(_UpperCAmelCase  ):
              raise ValueError(
                  """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""  )

       _a    : Optional[int]     					=[""""""] * len(_UpperCAmelCase  )
       for _ in range(len(_UpperCAmelCase  )  ):
              for i in range(len(_UpperCAmelCase  )  ):
                     _a    : int     					=bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   A__:    Any					    =       '''Provide a string that I will generate its BWT transform: '''
   A__:    Union[str, Any]					    =       input(entry_msg).strip()
   A__:    Optional[int]					    =       bwt_transform(s)
   print(
       F"Burrows Wheeler transform for string '{s}' results "
       F"in '{result['bwt_string']}'"
   )
   A__:    Union[str, Any]					    =       reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
   print(
       F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
       F"we get original string '{original_string}'"
   )


