

import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)







class 						__magic_name__      :

   '''simple docstring'''




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






     """simple docstring"""






     a__  =foo



   def __call__(    self	)							->   int:






     """simple docstring"""






     return self.foo







class 						__magic_name__      (							datasets.Dataset							):

   '''simple docstring'''




   @fingerprint_transform(inplace=lowercase__	)
   def 			_UpperCAmelCase			(    self,   lowercase_,   *lowercase_,   **lowercase_	)							->   Tuple:






     """simple docstring"""






     return DatasetChild(self.data,   fingerprint=lowercase__	)



   @fingerprint_transform(inplace=lowercase__	)
   def 			_UpperCAmelCase			(    self,   lowercase_,   *lowercase_,   **lowercase_	)							->   List[Any]:






     """simple docstring"""






     return DatasetChild(self.data,   fingerprint=lowercase__	)







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =callable



   def __call__(    self,   *lowercase_,   **lowercase_	)							->   List[str]:






     """simple docstring"""






     if self.callable is not None:
       return self.callable(*lowercase__,   **lowercase__	)



   def __getstate__(    self	)							->   Optional[Any]:






     """simple docstring"""






     raise pickle.PicklingError()







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @require_transformers
   @pytest.mark.integration
   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     from transformers import AutoTokenizer

     def encode(lowercase_	):
       return tokenizer(lowercase__	)

     # TODO: add hash consistency tests across sessions
     a__  =AutoTokenizer.from_pretrained('''bert-base-uncased'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(lambda lowercase_	: tokenizer(lowercase__	)	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =AutoTokenizer.from_pretrained('''bert-base-cased'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(lambda lowercase_	: tokenizer(lowercase__	)	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =AutoTokenizer.from_pretrained('''bert-base-uncased'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(lambda lowercase_	: tokenizer(lowercase__	)	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   @require_transformers
   @pytest.mark.integration
   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     from transformers import AutoTokenizer

     a__  =AutoTokenizer.from_pretrained('''gpt2'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     tokenizer('''Hello world !'''	)  # call once to change the tokenizer's cache
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)



   @require_regex
   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     import regex

     a__  =regex.Regex('''foo'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =regex.Regex('''bar'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =regex.Regex('''foo'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     def func():
       return foo

     a__  =[0]
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =[1]
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =[0]
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     def func():
       pass

     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()

     def func():
       pass

     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)



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






     """simple docstring"""






     a__  =mda(datasets.utils.py_utils.dumps(Foo([0]	)	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(Foo([1]	)	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(Foo([0]	)	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__  =mda(datasets.utils.py_utils.dumps(Foo([0]	).__call__	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(Foo([1]	).__call__	)	).hexdigest()
     a__  =mda(datasets.utils.py_utils.dumps(Foo([0]	).__call__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     a__  =(
         '''co_argcount''',
         '''co_kwonlyargcount''',
         '''co_nlocals''',
         '''co_stacksize''',
         '''co_flags''',
         '''co_code''',
         '''co_consts''',
         '''co_names''',
         '''co_varnames''',
         '''co_filename''',
         '''co_name''',
         '''co_firstlineno''',
         '''co_lnotab''',
         '''co_freevars''',
         '''co_cellvars''',
     )

     def _create_code_pyaa(*lowercase_	):
       if hasattr(args[-3],   '''encode'''	):
         a__  =list(lowercase__	)
         a__  =args[-3].encode()  # co_lnotab
         a__  =args[-10].encode()  # co_code
       if hasattr(lowercase__,   '''co_posonlyargcount'''	):
         if len(lowercase__	) == 16:
           return CodeType(*lowercase__	)
         elif len(lowercase__	) == 15:
           return CodeType(args[0],   0,   *args[1:]	)
         return CodeType(args[0],   0,   0,   *args[1:]	)
       elif hasattr(lowercase__,   '''co_kwonlyargcount'''	):
         if len(lowercase__	) == 16:
           return CodeType(args[0],   *args[2:]	)
         elif len(lowercase__	) == 15:
           return CodeType(*lowercase__	)
         return CodeType(args[0],   0,   *args[1:]	)
       if len(lowercase__	) == 16:
         return CodeType(args[0],   *args[3:]	)
       elif len(lowercase__	) == 15:
         return CodeType(args[0],   *args[2:]	)
       return CodeType(*lowercase__	)

     def create_ipython_func(lowercase_,   lowercase_	):
       def func():
         return returned_obj

       a__  =func.__code__
       # Use _create_code from dill in order to make it work for different python versions
       if datasets.config.PY_VERSION < version.parse('''3.8'''	):
         a__  =_create_code_pyaa(
             *[getattr(lowercase__,   lowercase__	) if k != '''co_filename''' else co_filename for k in code_args_pyaa]	)
       else:
         a__  =code.replace(co_filename=lowercase__	)
       return FunctionType(lowercase__,   func.__globals__,   func.__name__,   func.__defaults__,   func.__closure__	)

     a__,				a__  ='''<ipython-input-2-e0383a102aae>''', [0]
     a__  =mda(datasets.utils.py_utils.dumps(create_ipython_func(lowercase__,   lowercase__	)	)	).hexdigest()
     a__,				a__  ='''<ipython-input-2-e0383a102aae>''', [1]
     a__  =mda(datasets.utils.py_utils.dumps(create_ipython_func(lowercase__,   lowercase__	)	)	).hexdigest()
     a__,				a__  ='''<ipython-input-5-713f6613acf3>''', [0]
     a__  =mda(datasets.utils.py_utils.dumps(create_ipython_func(lowercase__,   lowercase__	)	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)

     a__,				a__  =os.path.join(gettempdir(),   '''ipykernel_12345''',   '''321456789.py'''	), [0]
     a__  =mda(datasets.utils.py_utils.dumps(create_ipython_func(lowercase__,   lowercase__	)	)	).hexdigest()
     a__,				a__  =os.path.join(gettempdir(),   '''ipykernel_12345''',   '''321456789.py'''	), [1]
     a__  =mda(datasets.utils.py_utils.dumps(create_ipython_func(lowercase__,   lowercase__	)	)	).hexdigest()
     a__,				a__  =os.path.join(gettempdir(),   '''ipykernel_12345''',   '''654123987.py'''	), [0]
     a__  =mda(datasets.utils.py_utils.dumps(create_ipython_func(lowercase__,   lowercase__	)	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__,				a__  =[0], [1]

     def func():
       return foo, bar

     a__  ='''__main__'''

     def globalvars_mocka_side_effect(lowercase_,   *lowercase_,   **lowercase_	):
       return {"foo": foo, "bar": bar}

     def globalvars_mocka_side_effect(lowercase_,   *lowercase_,   **lowercase_	):
       return {"bar": bar, "foo": foo}

     with patch('''dill.detect.globalvars''',   side_effect=lowercase__	) as globalvars_mocka:
       a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
       self.assertGreater(globalvars_mocka.call_count,   0	)
     with patch('''dill.detect.globalvars''',   side_effect=lowercase__	) as globalvars_mocka:
       a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
       self.assertGreater(globalvars_mocka.call_count,   0	)
     self.assertEqual(lowercase__,   lowercase__	)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__  =Hasher.hash('''hello'''	)
     a__  =Hasher.hash('''hello'''	)
     a__  =Hasher.hash('''there'''	)
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__  =Hasher.hash(Foo('''hello'''	)	)
     a__  =Hasher.hash(Foo('''hello'''	)	)
     a__  =Hasher.hash(Foo('''there'''	)	)
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  =Hasher()
     for x in ["hello", Foo('''hello'''	)]:
       hasher.update(lowercase__	)
     a__  =hasher.hexdigest()
     a__  =Hasher()
     for x in ["hello", Foo('''hello'''	)]:
       hasher.update(lowercase__	)
     a__  =hasher.hexdigest()
     a__  =Hasher()
     for x in ["there", Foo('''there'''	)]:
       hasher.update(lowercase__	)
     a__  =hasher.hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     with self.assertRaises(pickle.PicklingError	):
       Hasher.hash(UnpicklableCallable(Foo('''hello'''	)	)	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__  ='''abc'''
     a__  =[string, string]  # two strings have the same ids
     a__  =[string, string]
     a__  =json.loads(F"""[\"{string}\", \"{string}\"]"""	)  # two strings have different ids
     self.assertIs(obja[0],   lowercase__	)
     self.assertIs(obja[0],   obja[1]	)
     self.assertIs(obja[0],   lowercase__	)
     self.assertIs(obja[0],   obja[1]	)
     self.assertIsNot(obja[0],   lowercase__	)
     self.assertIsNot(obja[0],   obja[1]	)
     a__  =Hasher.hash(lowercase__	)
     a__  =Hasher.hash(lowercase__	)
     a__  =Hasher.hash(lowercase__	)
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertEqual(lowercase__,   lowercase__	)



   @require_tiktoken
   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     import tiktoken

     a__  =tiktoken.get_encoding('''gpt2'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =tiktoken.get_encoding('''r50k_base'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =tiktoken.get_encoding('''gpt2'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   @require_torch
   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     import torch

     a__  =torch.tensor([1.0]	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =torch.tensor([2.0]	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =torch.tensor([1.0]	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)



   @require_spacy
   @require_spacy_model('''en_core_web_sm'''	)
   @require_spacy_model('''fr_core_news_sm'''	)
   @pytest.mark.integration
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     import spacy

     a__  =spacy.load('''en_core_web_sm'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =spacy.load('''fr_core_news_sm'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     a__  =spacy.load('''en_core_web_sm'''	)
     a__  =mda(datasets.utils.py_utils.dumps(lowercase__	)	).hexdigest()
     self.assertEqual(lowercase__,   lowercase__	)
     self.assertNotEqual(lowercase__,   lowercase__	)





@pytest.mark.integration
def       UpperCAmelCase__    (      _A				:			Any							):





  '''simple docstring'''







  a__  =tmp_path / '''dir1'''
  a__  =tmp_path / '''dir2'''
  dira.mkdir()
  dira.mkdir()
  a__  ='''script.py'''
  a__  =dedent(
      '''\n    from datasets.fingerprint import Hasher\n    def foo():\n        pass\n    print(Hasher.hash(foo))\n    '''							)
  a__  =dira / script_filename
  a__  =dira / script_filename
  with script_patha.open('''w'''							) as f:
    f.write(SCREAMING_SNAKE_CASE_							)
  with script_patha.open('''w'''							) as f:
    f.write(SCREAMING_SNAKE_CASE_							)
  a__  =subprocess.check_output(['''python''', str(SCREAMING_SNAKE_CASE_							)]							)
  a__  =subprocess.check_output(['''python''', str(SCREAMING_SNAKE_CASE_							)]							)
  assert fingerprinta == fingerprinta




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  ={'''a''': [0, 1, 2]}
  a__  =DatasetChild(InMemoryTable.from_pydict(SCREAMING_SNAKE_CASE_							)							)
  a__  =dataset.funca()._fingerprint
  assert expected_fingerprint == dataset.funca()._fingerprint
  assert expected_fingerprint != dataset.funca()._fingerprint

  with Pool(2							) as p:
    assert expected_fingerprint == p.apply_async(dataset.funca							).get()._fingerprint
    assert expected_fingerprint != p.apply_async(dataset.funca							).get()._fingerprint




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  ={'''a''': [0, 1, 2]}







  class 						__magic_name__      (							datasets.Dataset							):

     '''simple docstring'''




     @fingerprint_transform(inplace=lowercase__	)
     def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Any:






       """simple docstring"""






       return DummyDatasetChild(self.data,   fingerprint=lowercase__	)





  a__  =DummyDatasetChild(InMemoryTable.from_pydict(SCREAMING_SNAKE_CASE_							)							).func()







  class 						__magic_name__      (							datasets.Dataset							):

     '''simple docstring'''




     @fingerprint_transform(inplace=lowercase__,   version='''1.0.0'''	)
     def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






       """simple docstring"""






       return DummyDatasetChild(self.data,   fingerprint=lowercase__	)





  a__  =DummyDatasetChild(InMemoryTable.from_pydict(SCREAMING_SNAKE_CASE_							)							).func()







  class 						__magic_name__      (							datasets.Dataset							):

     '''simple docstring'''




     @fingerprint_transform(inplace=lowercase__,   version='''2.0.0'''	)
     def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Dict:






       """simple docstring"""






       return DummyDatasetChild(self.data,   fingerprint=lowercase__	)





  a__  =DummyDatasetChild(InMemoryTable.from_pydict(SCREAMING_SNAKE_CASE_							)							).func()

  assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}							) == 3




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =Hasher()
  hasher.update(lambda _A							: x							)



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 0.93,   lowercase_ = True,   )							->   Any:






     """simple docstring"""






     a__  =mixing_coeff
     a__  =leapfrog_steps

     super().__init__()
     self.register_modules(
         vae=lowercase_,   text_encoder=lowercase_,   tokenizer=lowercase_,   unet=lowercase_,   scheduler=lowercase_,   )

     a__  =2 ** (len(self.vae.config.block_out_channels	) - 1)
     a__  =VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   int:






     """simple docstring"""






     a__  =self.tokenizer(
         lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

     a__  =self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

     a__  =prompt_embeds.to(dtype=self.text_encoder.dtype,   device=self.device	)

     if do_classifier_free_guidance:
       a__  ='''''' if negative_prompt is None else negative_prompt

       a__  =self.tokenizer(
           lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

       a__  =self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

       a__  =torch.cat([negative_prompt_embeds, prompt_embeds]	)

     return prompt_embeds



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     a__  =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     a__  =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Dict:






     """simple docstring"""






     a__  =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     a__  =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =int(lowercase_	)

     a__  =self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

     a__  =(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
     a__  =a_t * base + b_t * model_output

     return model_input, next_model_input.to(base.dtype	)



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """simple docstring"""






     a__  =1 / self.vae.config.scaling_factor * latents
     a__  =self.vae.decode(lowercase_	).sample
     a__  =(image / 2 + 0.5).clamp(0,   1	)
     return image



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   )							->   str:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =image.to(device=self.device,   dtype=text_embeds.dtype	)
     a__  =self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

     a__  =self.vae.config.scaling_factor * latent

     a__  =[latent.clone(), latent.clone()]

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       a__  =self.noise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

       # j - model_input index, k - base index
       for j in range(2	):
         a__  =j ^ 1

         if self.leapfrog_steps:
           if i % 2 == 0:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.noise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 3.0,   lowercase_ = 50,   lowercase_ = 0.8,   lowercase_ = None,   lowercase_ = None,   lowercase_ = "pil",   )							->   List[Any]:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =self.image_processor.preprocess(lowercase_	)

     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)
     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)

     self.scheduler.set_timesteps(lowercase_,   self.device	)

     a__  =num_inference_steps - int(num_inference_steps * strength	)
     a__  =self.scheduler.timesteps[t_limit:]
     a__  =fwd_timesteps.flip(0	)

     a__  =self.prepare_latents(lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       # j - model_input index, k - base index
       for k in range(2	):
         a__  =k ^ 1

         if self.leapfrog_steps:
           if i % 2 == 1:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.denoise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

       a__  =self.denoise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

     # either one is fine
     a__  =coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       a__  =(
           F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
           '''`pil`, `np`, `pt`, `latent`'''
       )
       deprecate('''Unsupported output_type''',   '''1.0.0''',   lowercase_,   standard_warn=lowercase_	)
       a__  ='''np'''

     if output_type == "latent":
       a__  =final_latent
     else:
       a__  =self.decode_latents(lowercase_	)
       a__  =self.image_processor.postprocess(lowercase_,   output_type=lowercase_	)

     return image
