


'''simple docstring'''






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 						a__							(				a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= KandinskyVaaPriorPipeline
							lowercase__     :  List[str]      		= ["prompt"]
							lowercase__     :  Union[str, Any]      		= ["prompt", "negative_prompt"]
							lowercase__     :  Optional[int]      		= [
							    "num_images_per_prompt",
							    "generator",
							    "num_inference_steps",
							    "latents",
							    "negative_prompt",
							    "guidance_scale",
							    "output_type",
							    "return_dict",
							]
							lowercase__     :  str      		= False







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														return 32







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														return 32







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return self.time_input_dim







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														return self.time_input_dim * 4







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														return 1_00







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
														return tokenizer







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      CLIPTextConfig(
														    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=self.text_embedder_hidden_size   ,       projection_dim=self.text_embedder_hidden_size   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=10_00   ,       )
														return CLIPTextModelWithProjection(lowerCamelCase_  )







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														torch.manual_seed(0  )

														lowerCAmelCase__			  =      {
														    '''num_attention_heads''': 2,
														    '''attention_head_dim''': 12,
														    '''embedding_dim''': self.text_embedder_hidden_size,
														    '''num_layers''': 1,
														}

														lowerCAmelCase__			  =      PriorTransformer(**lowerCamelCase_  )
														# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
														lowerCAmelCase__			  =      nn.Parameter(torch.ones(model.clip_std.shape  )  )
														return model







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      CLIPVisionConfig(
														    hidden_size=self.text_embedder_hidden_size   ,       image_size=2_24   ,       projection_dim=self.text_embedder_hidden_size   ,       intermediate_size=37   ,       num_attention_heads=4   ,       num_channels=3   ,       num_hidden_layers=5   ,       patch_size=14   ,       )

														lowerCAmelCase__			  =      CLIPVisionModelWithProjection(lowerCamelCase_  )
														return model







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      CLIPImageProcessor(
														    crop_size=2_24   ,       do_center_crop=lowerCamelCase_   ,       do_normalize=lowerCamelCase_   ,       do_resize=lowerCamelCase_   ,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,       resample=3   ,       size=2_24   ,       )

														return image_processor







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      self.dummy_prior
														lowerCAmelCase__			  =      self.dummy_image_encoder
														lowerCAmelCase__			  =      self.dummy_text_encoder
														lowerCAmelCase__			  =      self.dummy_tokenizer
														lowerCAmelCase__			  =      self.dummy_image_processor

														lowerCAmelCase__			  =      UnCLIPScheduler(
														    variance_type='''fixed_small_log'''   ,       prediction_type='''sample'''   ,       num_train_timesteps=10_00   ,       clip_sample=lowerCamelCase_   ,       clip_sample_range=10.0   ,       )

														lowerCAmelCase__			  =      {
														    '''prior''': prior,
														    '''image_encoder''': image_encoder,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''scheduler''': scheduler,
														    '''image_processor''': image_processor,
														}

														return components







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=0  )			->				int:
														if str(lowerCamelCase_  ).startswith('''mps'''  ):
																					lowerCAmelCase__			  =      torch.manual_seed(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''prompt''': '''horse''',
														    '''generator''': generator,
														    '''guidance_scale''': 4.0,
														    '''num_inference_steps''': 2,
														    '''output_type''': '''np''',
														}
														return inputs







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      '''cpu'''

														lowerCAmelCase__			  =      self.get_dummy_components()

														lowerCAmelCase__			  =      self.pipeline_class(**lowerCamelCase_  )
														lowerCAmelCase__			  =      pipe.to(lowerCamelCase_  )

														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      pipe(**self.get_dummy_inputs(lowerCamelCase_  )  )
														lowerCAmelCase__			  =      output.image_embeds

														lowerCAmelCase__			  =      pipe(
														    **self.get_dummy_inputs(lowerCamelCase_  )   ,       return_dict=lowerCamelCase_   ,       )[0]

														lowerCAmelCase__			  =      image[0, -10:]
														lowerCAmelCase__			  =      image_from_tuple[0, -10:]

														assert image.shape == (1, 32)

														lowerCAmelCase__			  =      np.array(
														    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2







							@skip_mps
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      torch_device == '''cpu'''
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      False

														self._test_inference_batch_single_identical(
														    test_max_difference=lowerCamelCase_   ,       relax_max_difference=lowerCamelCase_   ,       test_mean_pixel_difference=lowerCamelCase_   ,       )







							@skip_mps
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      torch_device == '''cpu'''
														lowerCAmelCase__			  =      False

														self._test_attention_slicing_forward_pass(
														    test_max_difference=lowerCamelCase_   ,       test_mean_pixel_difference=lowerCamelCase_   ,       )







'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      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					  =     int(SCREAMING_SNAKE_CASE__     )
   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(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   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}'"""
 )