



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)  # pylint: disable=invalid-name


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					WhisperForConditionalGeneration			,						__lowerCAmelCase					:					WhisperProcessor			,						__lowerCAmelCase					:					AutoencoderKL			,						__lowerCAmelCase					:					CLIPTextModel			,						__lowerCAmelCase					:					CLIPTokenizer			,						__lowerCAmelCase					:					UNetaDConditionModel			,						__lowerCAmelCase					:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,						__lowerCAmelCase					:					StableDiffusionSafetyChecker			,						__lowerCAmelCase					:					CLIPImageProcessor			,						)   ->	Dict:
    super().__init__()

    if safety_checker is None:
       logger.warning(
           f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
           ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
           ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
           ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
           ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
           ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''      )

    self.register_modules(
        speech_model=__lowerCAmelCase			,						speech_processor=__lowerCAmelCase			,						vae=__lowerCAmelCase			,						text_encoder=__lowerCAmelCase			,						tokenizer=__lowerCAmelCase			,						unet=__lowerCAmelCase			,						scheduler=__lowerCAmelCase			,						feature_extractor=__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Optional[Union[str, int]] = "auto"      )   ->	List[str]:
    if slice_size == "auto":
       _A      =       self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(__lowerCAmelCase      )

 def     snake_case_      (    self					:					int      )   ->	List[Any]:
    self.enable_attention_slicing(__lowerCAmelCase      )

 @torch.no_grad()
 def __call__(    self					:					Tuple			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Any=1_60_00			,						__lowerCAmelCase					:					int = 5_12			,						__lowerCAmelCase					:					int = 5_12			,						__lowerCAmelCase					:					int = 50			,						__lowerCAmelCase					:					float = 7.5			,						__lowerCAmelCase					:					Optional[Union[str, List[str]]] = None			,						__lowerCAmelCase					:					Optional[int] = 1			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					Optional[torch.Generator] = None			,						__lowerCAmelCase					:					Optional[torch.FloatTensor] = None			,						__lowerCAmelCase					:					Optional[str] = "pil"			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,						__lowerCAmelCase					:					int = 1			,						**__lowerCAmelCase					:					Optional[int]			,						)   ->	Optional[Any]:
    _A      =       self.speech_processor.feature_extractor(
        __lowerCAmelCase			,						return_tensors='''pt'''			,						sampling_rate=__lowerCAmelCase      ).input_features.to(self.device      )
    _A      =       self.speech_model.generate(__lowerCAmelCase			,						max_length=48_00_00      )

    _A      =       self.speech_processor.tokenizer.batch_decode(__lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						normalize=__lowerCAmelCase      )[
        0
    ]

    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
       _A      =       1
    elif isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
       _A      =       len(__lowerCAmelCase      )
    else:
       raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase      )}'''      )

    if height % 8 != 0 or width % 8 != 0:
       raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''      )

    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) or callback_steps <= 0)
    ):
       raise ValueError(
           f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
           f''' {type(__lowerCAmelCase      )}.'''      )

    # get prompt text embeddings
    _A      =       self.tokenizer(
        __lowerCAmelCase			,						padding='''max_length'''			,						max_length=self.tokenizer.model_max_length			,						return_tensors='''pt'''			,						)
    _A      =       text_inputs.input_ids

    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       _A      =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
       logger.warning(
           '''The following part of your input was truncated because CLIP can only handle sequences up to'''
           f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''      )
       _A      =       text_input_ids[:, : self.tokenizer.model_max_length]
    _A      =       self.text_encoder(text_input_ids.to(self.device      )      )[0]

    # duplicate text embeddings for each generation per prompt, using mps friendly method
    _A  ,    _A  ,    _A      =       text_embeddings.shape
    _A      =       text_embeddings.repeat(1			,						__lowerCAmelCase			,						1      )
    _A      =       text_embeddings.view(bs_embed * num_images_per_prompt			,						__lowerCAmelCase			,						-1      )

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
    _A      =       guidance_scale > 1.0
    # get unconditional embeddings for classifier free guidance
    if do_classifier_free_guidance:
       _A      =       42
       if negative_prompt is None:
          _A      =       [''''''] * batch_size
       elif type(__lowerCAmelCase      ) is not type(__lowerCAmelCase      ):
          raise TypeError(
              f'''`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCAmelCase      )} !='''
              f''' {type(__lowerCAmelCase      )}.'''      )
       elif isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          _A      =       [negative_prompt]
       elif batch_size != len(__lowerCAmelCase      ):
          raise ValueError(
              f'''`negative_prompt`: {negative_prompt} has batch size {len(__lowerCAmelCase      )}, but `prompt`:'''
              f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
              ''' the batch size of `prompt`.'''      )
       else:
          _A      =       negative_prompt

       _A      =       text_input_ids.shape[-1]
       _A      =       self.tokenizer(
           __lowerCAmelCase			,						padding='''max_length'''			,						max_length=__lowerCAmelCase			,						truncation=__lowerCAmelCase			,						return_tensors='''pt'''			,						)
       _A      =       self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       _A      =       uncond_embeddings.shape[1]
       _A      =       uncond_embeddings.repeat(1			,						__lowerCAmelCase			,						1      )
       _A      =       uncond_embeddings.view(batch_size * num_images_per_prompt			,						__lowerCAmelCase			,						-1      )

       # For classifier free guidance, we need to do two forward passes.
       # Here we concatenate the unconditional and text embeddings into a single batch
       # to avoid doing two forward passes
       _A      =       torch.cat([uncond_embeddings, text_embeddings]      )

    # get the initial random noise unless the user supplied it

    # Unlike in other pipelines, latents need to be generated in the target device
    # for 1-to-1 results reproducibility with the CompVis implementation.
    # However this currently doesn't work in `mps`.
    _A      =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
    _A      =       text_embeddings.dtype
    if latents is None:
       if self.device.type == "mps":
          # randn does not exist on mps
          _A      =       torch.randn(__lowerCAmelCase			,						generator=__lowerCAmelCase			,						device='''cpu'''			,						dtype=__lowerCAmelCase      ).to(
              self.device      )
       else:
          _A      =       torch.randn(__lowerCAmelCase			,						generator=__lowerCAmelCase			,						device=self.device			,						dtype=__lowerCAmelCase      )
    else:
       if latents.shape != latents_shape:
          raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''      )
       _A      =       latents.to(self.device      )

    # set timesteps
    self.scheduler.set_timesteps(__lowerCAmelCase      )

    # Some schedulers like PNDM have timesteps as arrays
    # It's more optimized to move all timesteps to correct device beforehand
    _A      =       self.scheduler.timesteps.to(self.device      )

    # scale the initial noise by the standard deviation required by the scheduler
    _A      =       latents * self.scheduler.init_noise_sigma

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    _A      =       '''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
    _A      =       {}
    if accepts_eta:
       _A      =       eta

    for i, t in enumerate(self.progress_bar(__lowerCAmelCase      )      ):
       # expand the latents if we are doing classifier free guidance
       _A      =       torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
       _A      =       self.scheduler.scale_model_input(__lowerCAmelCase			,						__lowerCAmelCase      )

       # predict the noise residual
       _A      =       self.unet(__lowerCAmelCase			,						__lowerCAmelCase			,						encoder_hidden_states=__lowerCAmelCase      ).sample

       # perform guidance
       if do_classifier_free_guidance:
          _A  ,    _A      =       noise_pred.chunk(2      )
          _A      =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

       # compute the previous noisy sample x_t -> x_t-1
       _A      =       self.scheduler.step(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						**__lowerCAmelCase      ).prev_sample

       # call the callback, if provided
       if callback is not None and i % callback_steps == 0:
          callback(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

    _A      =       1 / 0.1_8215 * latents
    _A      =       self.vae.decode(__lowerCAmelCase      ).sample

    _A      =       (image / 2 + 0.5).clamp(0			,						1      )

    # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
    _A      =       image.cpu().permute(0			,						2			,						3			,						1      ).float().numpy()

    if output_type == "pil":
       _A      =       self.numpy_to_pil(__lowerCAmelCase      )

    if not return_dict:
       return image

    return StableDiffusionPipelineOutput(images=__lowerCAmelCase			,						nsfw_content_detected=__lowerCAmelCase      )








UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   if p_len > t_len:
      return False

   _A      =       0
   _A      =       0
   _A      =       1

   # Calculating the hash of pattern and substring of text
   for i in range(_snake_case				):
      _A      =       (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      _A      =       (ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _A      =       (modulus_power * alphabet_size) % modulus

   for i in range(0	,   t_len - p_len + 1				):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      _A      =       (
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False

def 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



