






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def 			__UpperCamelCase				(	self   )				->Tuple:



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




      @property
      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     1
         __a					  =     3
         __a					  =     (32, 32)

         __a					  =     floats_tensor((batch_size, num_channels) + sizes			, rng=random.Random(0   )   ).to(lowerCamelCase   )
         return image




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



         '''simple docstring'''
         torch.manual_seed(0   )
         __a					  =     UNetaDConditionModel(
             block_out_channels=(32, 64)			, layers_per_block=2			, sample_size=32			, in_channels=4			, out_channels=4			, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			, up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			, cross_attention_dim=32			, )
         return model




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         torch.manual_seed(0   )
         __a					  =     AutoencoderKL(
             block_out_channels=[32, 64]			, in_channels=3			, out_channels=3			, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			, up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			, latent_channels=4			, )
         return model




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



         '''simple docstring'''
         torch.manual_seed(0   )
         __a					  =     RobertaSeriesConfig(
             hidden_size=32			, project_dim=32			, intermediate_size=37			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=5006			, )
         return RobertaSeriesModelWithTransformation(lowerCamelCase   )




      @property
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         def extract(*lowerCamelCase			, **lowerCamelCase   ):
            class 				__SCREAMING_SNAKE_CASE							:




                  def __init__(	self   )				->List[str]:



                     '''simple docstring'''
                     __a					  =     torch.ones([0]   )






                  def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->int:



                     '''simple docstring'''
                     self.pixel_values.to(lowerCamelCase   )
                     return self







            return Out()

         return extract




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     'cpu'  # ensure determinism for the device-dependent torch.Generator
         __a					  =     self.dummy_cond_unet
         __a					  =     PNDMScheduler(skip_prk_steps=lowerCamelCase   )
         __a					  =     self.dummy_vae
         __a					  =     self.dummy_text_encoder
         __a					  =     XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
         __a					  =     77

         __a					  =     self.dummy_image.to(lowerCamelCase   )
         __a					  =     init_image / 2 + 0.5

         # make sure here that pndm scheduler skips prk
         __a					  =     AltDiffusionImgaImgPipeline(
             unet=lowerCamelCase			, scheduler=lowerCamelCase			, vae=lowerCamelCase			, text_encoder=lowerCamelCase			, tokenizer=lowerCamelCase			, safety_checker=lowerCamelCase			, feature_extractor=self.dummy_extractor			, )
         __a					  =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor			, do_normalize=lowerCamelCase   )
         __a					  =     alt_pipe.to(lowerCamelCase   )
         alt_pipe.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     'A painting of a squirrel eating a burger'
         __a					  =     torch.Generator(device=lowerCamelCase   ).manual_seed(0   )
         __a					  =     alt_pipe(
             [prompt]			, generator=lowerCamelCase			, guidance_scale=6.0			, num_inference_steps=2			, output_type='np'			, image=lowerCamelCase			, )

         __a					  =     output.images

         __a					  =     torch.Generator(device=lowerCamelCase   ).manual_seed(0   )
         __a					  =     alt_pipe(
             [prompt]			, generator=lowerCamelCase			, guidance_scale=6.0			, num_inference_steps=2			, output_type='np'			, image=lowerCamelCase			, return_dict=lowerCamelCase			, )[0]

         __a					  =     image[0, -3:, -3:, -1]
         __a					  =     image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __a					  =     np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5e-3




      @unittest.skipIf(torch_device != 'cuda'			, 'This test requires a GPU'   )
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         __a					  =     self.dummy_cond_unet
         __a					  =     PNDMScheduler(skip_prk_steps=lowerCamelCase   )
         __a					  =     self.dummy_vae
         __a					  =     self.dummy_text_encoder
         __a					  =     XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
         __a					  =     77

         __a					  =     self.dummy_image.to(lowerCamelCase   )

         # put models in fp16
         __a					  =     unet.half()
         __a					  =     vae.half()
         __a					  =     bert.half()

         # make sure here that pndm scheduler skips prk
         __a					  =     AltDiffusionImgaImgPipeline(
             unet=lowerCamelCase			, scheduler=lowerCamelCase			, vae=lowerCamelCase			, text_encoder=lowerCamelCase			, tokenizer=lowerCamelCase			, safety_checker=lowerCamelCase			, feature_extractor=self.dummy_extractor			, )
         __a					  =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor			, do_normalize=lowerCamelCase   )
         __a					  =     alt_pipe.to(lowerCamelCase   )
         alt_pipe.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     'A painting of a squirrel eating a burger'
         __a					  =     torch.manual_seed(0   )
         __a					  =     alt_pipe(
             [prompt]			, generator=lowerCamelCase			, num_inference_steps=2			, output_type='np'			, image=lowerCamelCase			, ).images

         assert image.shape == (1, 32, 32, 3)






      @unittest.skipIf(torch_device != 'cuda'			, 'This test requires a GPU'   )
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'   )
         # resize to resolution that is divisible by 8 but not 16 or 32
         __a					  =     init_image.resize((760, 504)   )

         __a					  =     'BAAI/AltDiffusion'
         __a					  =     AltDiffusionImgaImgPipeline.from_pretrained(
             lowerCamelCase			, safety_checker=lowerCamelCase			, )
         pipe.to(lowerCamelCase   )
         pipe.set_progress_bar_config(disable=lowerCamelCase   )
         pipe.enable_attention_slicing()

         __a					  =     'A fantasy landscape, trending on artstation'

         __a					  =     torch.manual_seed(0   )
         __a					  =     pipe(
             prompt=lowerCamelCase			, image=lowerCamelCase			, strength=0.75			, guidance_scale=7.5			, generator=lowerCamelCase			, output_type='np'			, )
         __a					  =     output.images[0]

         __a					  =     image[255:258, 383:386, -1]

         assert image.shape == (504, 760, 3)
         __a					  =     np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]   )

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







@slow
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def 			__UpperCamelCase				(	self   )				->int:



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






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



         '''simple docstring'''
         __a					  =     load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'   )
         __a					  =     init_image.resize((768, 512)   )
         __a					  =     load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'   )

         __a					  =     'BAAI/AltDiffusion'
         __a					  =     AltDiffusionImgaImgPipeline.from_pretrained(
             lowerCamelCase			, safety_checker=lowerCamelCase			, )
         pipe.to(lowerCamelCase   )
         pipe.set_progress_bar_config(disable=lowerCamelCase   )
         pipe.enable_attention_slicing()

         __a					  =     'A fantasy landscape, trending on artstation'

         __a					  =     torch.manual_seed(0   )
         __a					  =     pipe(
             prompt=lowerCamelCase			, image=lowerCamelCase			, strength=0.75			, guidance_scale=7.5			, generator=lowerCamelCase			, output_type='np'			, )
         __a					  =     output.images[0]

         assert image.shape == (512, 768, 3)
         # img2img is flaky across GPUs even in fp32, so using MAE here
         assert np.abs(expected_image - image   ).max() < 1e-2







'''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}'"""
 )