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








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.")




