"""simple docstring"""





import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


_UpperCamelCase       :						Dict     =   {}
for task, _ in pipeline_test_mapping.items():
       _UpperCamelCase       :						Dict     =   {"pt": None, "tf": None}


# DO **NOT** add item to this set (unless the reason is approved)
_UpperCamelCase       :						List[str]     =   {
    "tests/models/esm/test_modeling_esmfold.py",  # The pipeline test mapping is added to `test_modeling_esm.py`
}





def     a_					(				_lowerCAmelCase  :			Any					):





       '''simple docstring'''

       if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
              return "pt"
       elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
              return "tf"
       elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
              return "flax"
       else:
              return None





def     a_					(				_lowerCAmelCase  :			Optional[int]       ,			_lowerCAmelCase  :			Tuple					):





       '''simple docstring'''

       if PIPELINE_TEST_MAPPING[task].get(_lowerCAmelCase       ,			_lowerCAmelCase					) is not None:
              return PIPELINE_TEST_MAPPING[task][framework]

       lowercase__      :		Any          =     pipeline_test_mapping[task]['test']
       lowercase__      :		Union[str, Any]          =     None

       if framework == "pt":
              lowercase__      :		Tuple          =     getattr(_lowerCAmelCase       ,			'model_mapping'       ,			_lowerCAmelCase					)
       elif framework == "tf":
              lowercase__      :		Union[str, Any]          =     getattr(_lowerCAmelCase       ,			'tf_model_mapping'       ,			_lowerCAmelCase					)

       if mapping is not None:
              lowercase__      :		Optional[Any]          =     dict(mapping.items()					)

       # cache the results
       lowercase__      :		Optional[int]          =     mapping
       return mapping





def     a_					(				_lowerCAmelCase  :			Tuple       ,			_lowerCAmelCase  :			List[str]					):





       '''simple docstring'''

       lowercase__      :		Union[str, Any]          =     get_framework(_lowerCAmelCase					)
       if framework is None:
              return None
       lowercase__      :		int          =     get_mapping_for_task(_lowerCAmelCase       ,			_lowerCAmelCase					)
       if mapping is None:
              return None

       lowercase__      :		Optional[Any]          =     list({model_class.config_class for model_class in test_class.all_model_classes}					)
       if len(_lowerCAmelCase					) != 1:
              raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'					)

       # This could be a list/tuple of model classes, but it's rare.
       lowercase__      :		Optional[Any]          =     mapping.get(config_classes[0]       ,			_lowerCAmelCase					)
       if isinstance(_lowerCAmelCase       ,			(tuple, list)					):
              lowercase__      :		Optional[int]          =     sorted(_lowerCAmelCase       ,			key=lambda _lowerCAmelCase					: x.__name__					)

       return model_class





def     a_					(				_lowerCAmelCase  :			List[str]					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     [(task, get_model_for_pipeline_test(_lowerCAmelCase       ,			_lowerCAmelCase					)) for task in pipeline_test_mapping]
       lowercase__      :		Optional[int]          =     sorted([(task, model) for task, model in mapping if model is not None]       ,			key=lambda _lowerCAmelCase					: x[0]					)

       return dict(_lowerCAmelCase					)





def     a_					(				_lowerCAmelCase  :			Union[str, Any]					):





       '''simple docstring'''

       lowercase__      :		List[Any]          =     get_framework(_lowerCAmelCase					)
       if framework == "pt":
              lowercase__      :		Any          =     'torch'
       lowercase__      :		List[str]          =     '{}'

       lowercase__      :		Tuple          =     get_pipeline_model_mapping(_lowerCAmelCase					)
       if len(_lowerCAmelCase					) == 0:
              return ""

       lowercase__      :		int          =     []
       for task, model_classes in mapping.items():
              if isinstance(_lowerCAmelCase       ,			(tuple, list)					):
                     # A list/tuple of model classes
                     lowercase__      :		List[str]          =     '(' + ', '.join([x.__name__ for x in model_classes]					) + ')'
              else:
                     # A single model class
                     lowercase__      :		Optional[int]          =     model_classes.__name__
              texts.append(f"""\"{task}\": {value}"""					)
       lowercase__      :		int          =     '{' + ', '.join(_lowerCAmelCase					) + '}'
       lowercase__      :		Union[str, Any]          =     f"""pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"""

       return text





def     a_					(				_lowerCAmelCase  :			Any					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     {'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
       if not issubclass(_lowerCAmelCase       ,			unittest.TestCase					):
              return False
       return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0





def     a_					(				_lowerCAmelCase  :			Union[str, Any]					):





       '''simple docstring'''

       lowercase__      :		Tuple          =     [x for x in get_test_classes(_lowerCAmelCase					) if is_valid_test_class(_lowerCAmelCase					)]

       lowercase__      :		Tuple          =     None
       for test_class in test_classes:
              # If a test class has defined `pipeline_model_mapping`, let's take it
              if getattr(_lowerCAmelCase       ,			'pipeline_model_mapping'       ,			_lowerCAmelCase					) is not None:
                     lowercase__      :		List[str]          =     test_class
                     break
    # Take the test class with the shortest name (just a heuristic)
       if target_test_class is None and len(_lowerCAmelCase					) > 0:
              lowercase__      :		List[Any]          =     sorted(_lowerCAmelCase       ,			key=lambda _lowerCAmelCase					: (len(x.__name__					), x.__name__)					)[0]

       return target_test_class





def     a_					(				_lowerCAmelCase  :			Optional[int]       ,			_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			Union[str, Any]					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     start_idx
       for idx, line in enumerate(lines[start_idx:]					):
              lowercase__      :		int          =     len(_lowerCAmelCase					) - len(line.lstrip()					)
              if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
                     lowercase__      :		Any          =     start_idx + idx
              elif idx > 0 and indent <= indent_level:
                     # Outside the definition block of `pipeline_model_mapping`
                     break

       return end_idx





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str=False					):





       '''simple docstring'''

       if getattr(_lowerCAmelCase       ,			'pipeline_model_mapping'       ,			_lowerCAmelCase					) is not None:
              if not overwrite:
                     return "", -1

       lowercase__      :		Tuple          =     get_pipeline_model_mapping_string(_lowerCAmelCase					)
       if len(_lowerCAmelCase					) == 0:
              return "", -1
       lowercase__      :		int          =     line_to_add + '\n'

       # The code defined the class `test_class`
       lowercase__   ,				lowercase__      :		Union[str, Any]          =     inspect.getsourcelines(_lowerCAmelCase					)
       # `inspect` gives the code for an object, including decorator(s) if any.
       # We (only) need the exact line of the class definition.
       for idx, line in enumerate(_lowerCAmelCase					):
              if line.lstrip().startswith('class '					):
                     lowercase__      :		List[str]          =     class_lines[idx:]
                     class_start_line_no += idx
                     break
       lowercase__      :		Optional[Any]          =     class_start_line_no + len(_lowerCAmelCase					) - 1

       # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
       # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
       # block that appears in a `test_class`.
       lowercase__      :		Optional[Any]          =     None
       # The indent level of the line at `class_lines[start_idx]` (if defined)
       lowercase__      :		Any          =     0
       # To record if `pipeline_model_mapping` is found in `test_class`.
       lowercase__      :		Optional[int]          =     None
       for idx, line in enumerate(_lowerCAmelCase					):
              if line.strip().startswith('all_model_classes = '					):
                     lowercase__      :		Union[str, Any]          =     len(_lowerCAmelCase					) - len(line.lstrip()					)
                     lowercase__      :		str          =     idx
              elif line.strip().startswith('all_generative_model_classes = '					):
                     lowercase__      :		Dict          =     len(_lowerCAmelCase					) - len(line.lstrip()					)
                     lowercase__      :		Dict          =     idx
              elif line.strip().startswith('pipeline_model_mapping = '					):
                     lowercase__      :		Optional[Any]          =     len(_lowerCAmelCase					) - len(line.lstrip()					)
                     lowercase__      :		Optional[int]          =     idx
                     lowercase__      :		Optional[Any]          =     line
                     break

       if start_idx is None:
              return "", -1
       # Find the ending index (inclusive) of the above found block.
       lowercase__      :		List[Any]          =     find_block_ending(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)

       # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
       # `is_timm_available()` instead of `is_torch_available()`.
       # Keep leading and trailing whitespaces
       lowercase__      :		List[Any]          =     re.compile(R'\s(is_\S+?_available\(\))\s'					)
       for line in class_lines[start_idx : end_idx + 1]:
              lowercase__      :		Any          =     r.search(_lowerCAmelCase					)
              if backend_condition is not None:
                     # replace the leading and trailing whitespaces to the space character " ".
                     lowercase__      :		List[str]          =     ' ' + backend_condition[0][1:-1] + ' '
                     lowercase__      :		Dict          =     r.sub(_lowerCAmelCase       ,			_lowerCAmelCase					)
                     break

       if def_line is None:
              # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
              # `all_model_classes` or `all_generative_model_classes`.
              lowercase__      :		Union[str, Any]          =     end_idx
       else:
              # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
              lowercase__      :		int          =     start_idx - 1
              # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
              for idx in range(_lowerCAmelCase       ,			end_idx + 1					):
                     # These lines are going to be removed before writing to the test file.
                     lowercase__      :		Optional[Any]          =     None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
       lowercase__      :		Tuple          =     [x.__name__ for x in test_class.__bases__]
       if "PipelineTesterMixin" not in parent_classes:
              # Put `PipelineTesterMixin` just before `unittest.TestCase`
              lowercase__      :		str          =     [x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
              if "TestCase" in parent_classes:
                     # Here we **assume** the original string is always with `unittest.TestCase`.
                     _parent_classes.append('unittest.TestCase'					)
              lowercase__      :		Tuple          =     ', '.join(_parent_classes					)
              for idx, line in enumerate(_lowerCAmelCase					):
                     # Find the ending of the declaration of `test_class`
                     if line.strip().endswith('):'					):
                            # mark the lines of the declaration of `test_class` to be removed
                            for _idx in range(idx + 1					):
                                   lowercase__      :		Union[str, Any]          =     None  # noqa
                            break
        # Add the new, one-line, class declaration for `test_class`
              lowercase__      :		Union[str, Any]          =     f"""class {test_class.__name__}({parent_classes}):\n"""

       # Add indentation
       lowercase__      :		Optional[int]          =     ' ' * indent_level + line_to_add
       # Insert `pipeline_model_mapping` to `class_lines`.
       # (The line at `target_idx` should be kept by definition!)
       lowercase__      :		int          =     class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
       # Remove the lines that are marked to be removed
       lowercase__      :		Union[str, Any]          =     [x for x in class_lines if x is not None]

       # Move from test class to module (in order to write to the test file)
       lowercase__      :		Dict          =     inspect.getsourcelines(inspect.getmodule(_lowerCAmelCase					)					)[0]
       # Be careful with the 1-off between line numbers and array indices
       lowercase__      :		Optional[Any]          =     module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
       lowercase__      :		List[Any]          =     ''.join(_lowerCAmelCase					)

       lowercase__      :		List[Any]          =     inspect.getsourcefile(_lowerCAmelCase					)
       with open(_lowerCAmelCase       ,			'w'       ,			encoding='UTF-8'       ,			newline='\n'					) as fp:
              fp.write(_lowerCAmelCase					)

       return line_to_add





def     a_					(				_lowerCAmelCase  :			Any       ,			_lowerCAmelCase  :			Dict=False					):





       '''simple docstring'''

       lowercase__      :		str          =     find_test_class(_lowerCAmelCase					)
       if test_class:
              add_pipeline_model_mapping(_lowerCAmelCase       ,			overwrite=_lowerCAmelCase					)


if __name__ == "__main__":
       _UpperCamelCase       :						List[str]     =   argparse.ArgumentParser()
       parser.add_argument(
           "--test_file", type=str, help="A path to the test file, starting with the repository's `tests` directory."
       )
       parser.add_argument(
           "--all",
           action="store_true",
           help="If to check and modify all test files.",
       )
       parser.add_argument(
           "--overwrite",
           action="store_true",
           help="If to overwrite a test class if it has already defined `pipeline_model_mapping`.",
       )
       _UpperCamelCase       :						Any     =   parser.parse_args()

       if not args.all and not args.test_file:
              raise ValueError("Please specify either `test_file` or pass `--all` to check/modify all test files.")
       elif args.all and args.test_file:
              raise ValueError("Only one of `--test_file` and `--all` could be specified.")

       _UpperCamelCase       :						Tuple     =   []
       if args.test_file:
              _UpperCamelCase       :						int     =   [args.test_file]
       else:
              _UpperCamelCase       :						Optional[Any]     =   os.path.join("tests", "models", "**", "test_modeling_*.py")
              for test_file in glob.glob(pattern):
                     # `Flax` is not concerned at this moment
                     if not test_file.startswith("test_modeling_flax_"):
                            test_files.append(test_file)

       for test_file in test_files:
              if test_file in TEST_FILE_TO_IGNORE:
                     print(f'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
                     continue
              add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)


"""simple docstring"""





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 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

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

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

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

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

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

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

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

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

             lowercase__      :		int          =     self.prepare_latents(a		,      a		,      a		,      a		,      a							)

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    # j - model_input index, k - base index
                    for k in range(2							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

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

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        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'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

