code: from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  _SCREAMING_SNAKE_CASE    :				str       =					getLogger(__name__)
  
  
  _SCREAMING_SNAKE_CASE    :				int       =					4
  _SCREAMING_SNAKE_CASE    :				Dict       =					7
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      'float16': torch.floataa,
      'float32': torch.floataa,
  }
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if isinstance(UpperCamelCase__							,torch.Tensor       ):
            return image
       elif isinstance(UpperCamelCase__							,PIL.Image.Image       ):
            snake_case					=       [image]
  
       if isinstance(image[0]							,PIL.Image.Image       ):
            snake_case					=       image[0].size
            snake_case					=       (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
            snake_case					=       [np.array(i.resize((w, h)       )       )[None, :] for i in image]
            snake_case					=       np.concatenate(UpperCamelCase__							,axis=0       )
            snake_case					=       np.array(UpperCamelCase__       ).astype(np.floataa       ) / 255.0
            snake_case					=       image.transpose(0							,3							,1							,2       )
            snake_case					=       2.0 * image - 1.0
            snake_case					=       torch.from_numpy(UpperCamelCase__       )
       elif isinstance(image[0]							,torch.Tensor       ):
            snake_case					=       torch.cat(UpperCamelCase__							,dim=0       )
  
       return image
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = 3_5_0    ,      ):
           super().__init__(
               vae=__snake_case    ,      text_encoder=__snake_case    ,      tokenizer=__snake_case    ,      unet=__snake_case    ,      low_res_scheduler=__snake_case    ,      scheduler=__snake_case    ,      safety_checker=__snake_case    ,      feature_extractor=__snake_case    ,      watermarker=__snake_case    ,      max_noise_level=__snake_case    ,      )
      def __call__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case = 7_5    ,      __snake_case = 9.0    ,      __snake_case = 2_0    ,      __snake_case = None    ,      __snake_case = 1    ,      __snake_case = 0.0    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = "pil"    ,      __snake_case = True    ,      __snake_case = None    ,      __snake_case = 1    ,      ):
           self.check_inputs(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case					)
  
           # 2. Define call parameters
           if prompt is not None and isinstance(__snake_case    ,      __snake_case					):
                snake_case					=       1
           elif prompt is not None and isinstance(__snake_case    ,      __snake_case					):
                snake_case					=       len(__snake_case					)
           else:
                snake_case					=       prompt_embeds.shape[0]
  
           snake_case					=       self._execution_device
           # 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.
           snake_case					=       guidance_scale > 1.0
  
           # 3. Encode input prompt
           snake_case					=       self._encode_prompt(
               __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      prompt_embeds=__snake_case    ,      negative_prompt_embeds=__snake_case    ,      )
  
           snake_case					=       ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]
  
           # 4. Preprocess image
           snake_case					=       preprocess(__snake_case					)
           snake_case					=       image.cpu()
  
           # 5. set timesteps
           self.scheduler.set_timesteps(__snake_case    ,      device=__snake_case					)
           snake_case					=       self.scheduler.timesteps
  
           # 5. Add noise to image
           snake_case					=       torch.tensor([noise_level]    ,      dtype=torch.long    ,      device=__snake_case					)
           snake_case					=       torch.randn(image.shape    ,      generator=__snake_case    ,      device=__snake_case    ,      dtype=__snake_case					)
           snake_case					=       self.low_res_scheduler.add_noise(__snake_case    ,      __snake_case    ,      __snake_case					)
  
           snake_case					=       2 if do_classifier_free_guidance else 1
           snake_case					=       np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
           snake_case					=       np.concatenate([noise_level] * image.shape[0]					)
  
           # 6. Prepare latent variables
           snake_case					=       image.shape[2:]
           snake_case					=       self.prepare_latents(
               batch_size * num_images_per_prompt    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      )
  
           # 7. Check that sizes of image and latents match
           snake_case					=       image.shape[1]
           if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
                raise ValueError(
                    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
                    F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
                    F''' `num_channels_image`: {num_channels_image} '''
                    F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
                    ''' `pipeline.unet` or your `image` input.'''					)
  
           # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
           snake_case					=       self.prepare_extra_step_kwargs(__snake_case    ,      __snake_case					)
  
           snake_case					=       next(
               (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')    ,      '''tensor(float)'''					)
           snake_case					=       ORT_TO_NP_TYPE[timestep_dtype]
  
           # 9. Denoising loop
           snake_case					=       len(__snake_case					) - num_inference_steps * self.scheduler.order
           with self.progress_bar(total=__snake_case					) as progress_bar:
                for i, t in enumerate(__snake_case					):
                     # expand the latents if we are doing classifier free guidance
                     snake_case					=       np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents
  
                     # concat latents, mask, masked_image_latents in the channel dimension
                     snake_case					=       self.scheduler.scale_model_input(__snake_case    ,      __snake_case					)
                     snake_case					=       np.concatenate([latent_model_input, image]    ,      axis=1					)
  
                     # timestep to tensor
                     snake_case					=       np.array([t]    ,      dtype=__snake_case					)
  
                     # predict the noise residual
                     snake_case					=       self.unet(
                         sample=__snake_case    ,      timestep=__snake_case    ,      encoder_hidden_states=__snake_case    ,      class_labels=noise_level.astype(np.intaa					)    ,      )[0]
  
                     # perform guidance
                     if do_classifier_free_guidance:
                          snake_case					=       np.split(__snake_case    ,      2					)
                     snake_case					=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                     # compute the previous noisy sample x_t -> x_t-1
                     snake_case					=       self.scheduler.step(
                         torch.from_numpy(__snake_case					)    ,      __snake_case    ,      __snake_case    ,      **__snake_case					).prev_sample
  
                     # call the callback, if provided
                     if i == len(__snake_case					) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                          progress_bar.update()
                          if callback is not None and i % callback_steps == 0:
                               callback(__snake_case    ,      __snake_case    ,      __snake_case					)
  
          # 10. Post-processing
           snake_case					=       self.decode_latents(latents.float()					)
  
           # 11. Convert to PIL
           if output_type == "pil":
                snake_case					=       self.numpy_to_pil(__snake_case					)
  
           if not return_dict:
                return (image,)
  
           return ImagePipelineOutput(images=__snake_case					)
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       1 / 0.0_8333 * latents
           snake_case					=       self.vae(latent_sample=__snake_case					)[0]
           snake_case					=       np.clip(image / 2 + 0.5    ,      0    ,      1					)
           snake_case					=       image.transpose((0, 2, 3, 1)					)
           return image
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = None    ,      __snake_case = None    ,      ):
           if prompt is not None and isinstance(__snake_case    ,      __snake_case					):
                snake_case					=       1
           elif prompt is not None and isinstance(__snake_case    ,      __snake_case					):
                snake_case					=       len(__snake_case					)
           else:
                snake_case					=       prompt_embeds.shape[0]
  
           if prompt_embeds is None:
                snake_case					=       self.tokenizer(
                    __snake_case    ,      padding='''max_length'''    ,      max_length=self.tokenizer.model_max_length    ,      truncation=__snake_case    ,      return_tensors='''pt'''    ,      )
                snake_case					=       text_inputs.input_ids
                snake_case					=       self.tokenizer(__snake_case    ,      padding='''longest'''    ,      return_tensors='''pt'''					).input_ids
  
                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    __snake_case    ,      __snake_case					):
                     snake_case					=       self.tokenizer.batch_decode(
                         untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
                     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}'''					)
  
                # no positional arguments to text_encoder
                snake_case					=       self.text_encoder(
                    input_ids=text_input_ids.int().to(__snake_case					)    ,      )
                snake_case					=       prompt_embeds[0]
  
           snake_case					=       prompt_embeds.shape
           # duplicate text embeddings for each generation per prompt, using mps friendly method
           snake_case					=       prompt_embeds.repeat(1    ,      __snake_case					)
           snake_case					=       prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,      __snake_case    ,      -1					)
  
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance and negative_prompt_embeds is None:
                snake_case					=       42
                if negative_prompt is None:
                     snake_case					=       [''''''] * batch_size
                elif type(__snake_case					) is not type(__snake_case					):
                     raise TypeError(
                         F'''`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case					)} !='''
                         F''' {type(__snake_case					)}.'''					)
                elif isinstance(__snake_case    ,      __snake_case					):
                     snake_case					=       [negative_prompt]
                elif batch_size != len(__snake_case					):
                     raise ValueError(
                         F'''`negative_prompt`: {negative_prompt} has batch size {len(__snake_case					)}, but `prompt`:'''
                         F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                         ''' the batch size of `prompt`.'''					)
                else:
                     snake_case					=       negative_prompt
  
                snake_case					=       text_input_ids.shape[-1]
                snake_case					=       self.tokenizer(
                    __snake_case    ,      padding='''max_length'''    ,      max_length=__snake_case    ,      truncation=__snake_case    ,      return_tensors='''pt'''    ,      )
  
                # if hasattr(uncond_input, "attention_mask"):
                #     attention_mask = uncond_input.attention_mask.to(device)
                # else:
                #     attention_mask = None
  
                snake_case					=       self.text_encoder(
                    input_ids=uncond_input.input_ids.int().to(__snake_case					)    ,      )
                snake_case					=       uncond_embeddings[0]
  
           if do_classifier_free_guidance:
                snake_case					=       uncond_embeddings.shape[1]
                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                snake_case					=       uncond_embeddings.repeat(1    ,      __snake_case					)
                snake_case					=       uncond_embeddings.reshape(batch_size * num_images_per_prompt    ,      __snake_case    ,      -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
                snake_case					=       np.concatenate([uncond_embeddings, prompt_embeds]					)
  
           return prompt_embeds
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       {literal: None for literal in literals}
      def __str__( self					):
           return "{" + " , ".join(self.literals					) + "}"
      def __len__( self					):
           return len(self.literals					)
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal[:2]
                if symbol in model:
                     snake_case					=       model[symbol]
                else:
                     continue
                if value is not None:
                     # Complement assignment if literal is in complemented form
                     if literal.endswith('''\''''					):
                          snake_case					=       not value
                snake_case					=       value
  
  
  
  
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal.rstrip('''\''''					) if literal.endswith('''\''''					) else literal + '''\''''
                if symbol in self.literals:
                     return True
  
           self.assign(__snake_case					)
           for value in self.literals.values():
                if value in (True, None):
                     return value
           return any(self.literals.values()					)
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       list(__snake_case					)
  
  
  
  
      def __str__( self					):
           return "{" + " , ".join(str(__snake_case					) for clause in self.clauses					) + "}"
  
  
  
  
  
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       random.randint(1							,5       )
       snake_case					=       '''A'''
       snake_case					=       0
       while i < no_of_literals:
            snake_case					=       random.randint(1							,5       )
            snake_case					=       base_var + str(UpperCamelCase_       )
            snake_case					=       random.randint(0							,1       )
            if var_complement == 1:
                 var_name += "'"
            if var_name in literals:
                 i -= 1
            else:
                 literals.append(UpperCamelCase_       )
            i += 1
       return Clause(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       set()
       snake_case					=       random.randint(1							,10       )
       while len(UpperCamelCase_       ) < no_of_clauses:
            clauses.add(generate_clause()       )
       return Formula(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       formula.clauses
       snake_case					=       []
       for clause in formula.clauses:
            for literal in clause.literals:
                 snake_case					=       literal[:2]
                 if symbol not in symbols_set:
                      symbols_set.append(UpperCamelCase_       )
       return clauses, symbols_set
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       {}
       snake_case					=       []
  
       for clause in clauses:
            if clause.evaluate(UpperCamelCase_       ):
                 continue
            for literal in clause.literals:
                 literals.append(UpperCamelCase_       )
  
       for s in symbols:
            snake_case					=       s + '''\''''
            if (s in literals and sym not in literals) or (
                s not in literals and sym in literals
            ):
                 pure_symbols.append(UpperCamelCase_       )
       for p in pure_symbols:
            snake_case					=       None
       for s in pure_symbols:
            snake_case					=       s + '''\''''
            if s in literals:
                 snake_case					=       True
            elif sym in literals:
                 snake_case					=       False
       return pure_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       for clause in clauses:
            if len(UpperCamelCase_       ) == 1:
                 unit_symbols.append(list(clause.literals.keys()       )[0]       )
            else:
                 snake_case				,						snake_case					=       0, 0
                 for literal, value in clause.literals.items():
                      if value is False:
                           f_count += 1
                      elif value is None:
                           snake_case					=       literal
                           n_count += 1
                 if f_count == len(UpperCamelCase_       ) - 1 and n_count == 1:
                      unit_symbols.append(UpperCamelCase_       )
       snake_case					=       {}
       for i in unit_symbols:
            snake_case					=       i[:2]
            snake_case					=       len(UpperCamelCase_       ) == 2
       snake_case					=       [i[:2] for i in unit_symbols]
  
       return unit_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       True
       for clause in clauses:
            snake_case					=       clause.evaluate(UpperCamelCase_       )
            if clause_check is False:
                 return False, None
            elif clause_check is None:
                 snake_case					=       False
                 continue
  
       if check_clause_all_true:
            return True, model
  
       try:
            snake_case				,						snake_case					=       find_pure_symbols(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       except RecursionError:
            print('''raises a RecursionError and is'''       )
            return None, {}
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       pure_symbols[0], assignment[pure_symbols[0]]
  
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       snake_case				,						snake_case					=       find_unit_clauses(UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       unit_symbols[0], assignment[unit_symbols[0]]
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       symbols[0]
       snake_case					=       symbols[1:]
       snake_case				,						snake_case					=       model, model
       snake_case				,						snake_case					=       True, False
  
       return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ) or dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
   _SCREAMING_SNAKE_CASE    :				Dict       =					generate_formula()
   print(f'''The formula {formula} is''', end=" ")
  
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Any       =					generate_parameters(formula)
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					dpll_algorithm(clauses, symbols, {})
  
   if solution:
    print(f'''satisfiable with the assignment {model}.''')
   else:
    print("not satisfiable.")
  
  
  
  
  