


from __future__ import annotations

import random
from collections.abc import Iterable







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase   ):
       snake_case_			= {literal: None for literal in literals}



 def __str__(      self   ):
       return "{" + " , ".join(self.literals   ) + "}"



 def __len__(      self   ):
       return len(self.literals   )



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       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 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       for literal in self.literals:
             snake_case_			= literal.rstrip('''\''''   ) if literal.endswith('''\''''   ) else literal + '''\''''
             if symbol in self.literals:
                   return True

       self.assign(_UpperCAmelCase   )
       for value in self.literals.values():
             if value in (True, None):
                   return value
       return any(self.literals.values()   )







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase   ):
       snake_case_			= list(_UpperCAmelCase   )





 def __str__(      self   ):
       return "{" + " , ".join(str(_UpperCAmelCase   ) for clause in self.clauses   ) + "}"






def 			__lowerCAmelCase       ()->    Clause:




      """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(SCREAMING_SNAKE_CASE   )
            snake_case_			= random.randint(0   ,   1   )
            if var_complement == 1:
                  var_name += "'"
            if var_name in literals:
                  i -= 1
            else:
                  literals.append(SCREAMING_SNAKE_CASE   )
            i += 1
      return Clause(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       ()->    Formula:




      """simple docstring"""

      snake_case_			= set()
      snake_case_			= random.randint(1   ,   10   )
      while len(SCREAMING_SNAKE_CASE   ) < no_of_clauses:
            clauses.add(generate_clause()   )
      return Formula(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    tuple[list[Clause], list[str]]:




      """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(SCREAMING_SNAKE_CASE   )
      return clauses, symbols_set






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    tuple[list[str], dict[str, bool | None]]:




      """simple docstring"""

      snake_case_			= []
      snake_case_			= {}
      snake_case_			= []

      for clause in clauses:
            if clause.evaluate(SCREAMING_SNAKE_CASE   ):
                  continue
            for literal in clause.literals:
                  literals.append(SCREAMING_SNAKE_CASE   )

      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(SCREAMING_SNAKE_CASE   )
      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 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    tuple[list[str], dict[str, bool | None]]:




      """simple docstring"""

      snake_case_			= []
      for clause in clauses:
            if len(SCREAMING_SNAKE_CASE   ) == 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(SCREAMING_SNAKE_CASE   ) - 1 and n_count == 1:
                        unit_symbols.append(SCREAMING_SNAKE_CASE   )
      snake_case_			= {}
      for i in unit_symbols:
            snake_case_			= i[:2]
            snake_case_			= len(SCREAMING_SNAKE_CASE   ) == 2
      snake_case_			= [i[:2] for i in unit_symbols]

      return unit_symbols, assignment






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    tuple[bool | None, dict[str, bool | None] | None]:




      """simple docstring"""

      snake_case_			= True
      for clause in clauses:
            snake_case_			= clause.evaluate(SCREAMING_SNAKE_CASE   )
            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(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
      except RecursionError:
            print('''raises a RecursionError and is'''   )
            return None, {}
      snake_case_			= None
      if len(SCREAMING_SNAKE_CASE   ) > 0:
            snake_case_			,       snake_case_			= pure_symbols[0], assignment[pure_symbols[0]]

      if p:
            snake_case_			= model
            snake_case_			= value
            snake_case_			= list(SCREAMING_SNAKE_CASE   )
            if p in tmp_symbols:
                  tmp_symbols.remove(SCREAMING_SNAKE_CASE   )
            return dpll_algorithm(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

      snake_case_			,       snake_case_			= find_unit_clauses(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
      snake_case_			= None
      if len(SCREAMING_SNAKE_CASE   ) > 0:
            snake_case_			,       snake_case_			= unit_symbols[0], assignment[unit_symbols[0]]
      if p:
            snake_case_			= model
            snake_case_			= value
            snake_case_			= list(SCREAMING_SNAKE_CASE   )
            if p in tmp_symbols:
                  tmp_symbols.remove(SCREAMING_SNAKE_CASE   )
            return dpll_algorithm(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
      snake_case_			= symbols[0]
      snake_case_			= symbols[1:]
      snake_case_			,       snake_case_			= model, model
      snake_case_			,       snake_case_			= True, False

      return dpll_algorithm(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ) or dpll_algorithm(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase     =       generate_formula()
   print(f'''The formula {formula} is''', end=""" """)

   UpperCAmelCase,				UpperCAmelCase     =       generate_parameters(formula)
   UpperCAmelCase,				UpperCAmelCase     =       dpll_algorithm(clauses, symbols, {})

   if solution:
      print(f'''satisfiable with the assignment {model}.''')
   else:
      print("""not satisfiable.""")



import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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(_UpperCAmelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

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

       snake_case_			= PriorTransformer(**_UpperCAmelCase   )
       # 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
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=_UpperCAmelCase						, do_normalize=_UpperCAmelCase						, do_resize=_UpperCAmelCase						, 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 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

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

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

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''strength''': 0.5,
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

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

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

       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 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

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





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

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