
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowercase_       :						Dict     					=	NewType('''DataClass''', Any)
lowercase_       :						Optional[int]     					=	NewType('''DataClassType''', Any)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    List[str]:
  if isinstance(lowerCamelCase__,						lowerCamelCase__				):
    return v
  if v.lower() in ("yes", "true", "t", "y", "1"):
    return True
  elif v.lower() in ("no", "false", "f", "n", "0"):
    return False
  else:
    raise ArgumentTypeError(
        f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							list				)  ->    Callable[[str], Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = {str(lowerCamelCase__				): choice for choice in choices}
  return lambda lowerCamelCase__				: str_to_choice.get(lowerCamelCase__,						lowerCamelCase__				)





def    _lowerCAmelCase    (     *,
    lowerCamelCase__							:							Union[str, List[str]] = None,						lowerCamelCase__							:							str = None,						lowerCamelCase__							:							Any = dataclasses.MISSING,						lowerCamelCase__							:							Callable[[], Any] = dataclasses.MISSING,						lowerCamelCase__							:							dict = None,						**lowerCamelCase__							:							List[Any],						)  ->    dataclasses.Field:
  if metadata is None:
    # Important, don't use as default param in function signature because dict is mutable and shared across function calls
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = {}
  if aliases is not None:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = aliases
  if help is not None:
    _SCREAMING_SNAKE_CASE   :				int							    = help

  return dataclasses.field(metadata=lowerCamelCase__,						default=lowerCamelCase__,						default_factory=lowerCamelCase__,						**lowerCamelCase__				)





class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
     def __init__(     self  ,     snake_case__  ,     **snake_case__				):




       """simple docstring"""


       if "formatter_class" not in kwargs:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = ArgumentDefaultsHelpFormatter
       super().__init__(**snake_case__				)
       if dataclasses.is_dataclass(snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Any							    = [dataclass_types]
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = list(snake_case__				)
       for dtype in self.dataclass_types:
         self._add_dataclass_arguments(snake_case__				)
     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = F'''--{field.name}'''
       _SCREAMING_SNAKE_CASE   :				int							    = field.metadata.copy()
       # field.metadata is not used at all by Data Classes,
       # it is provided as a third-party extension mechanism.
       if isinstance(field.type  ,     snake_case__				):
         raise RuntimeError(
             "Unresolved type detected, which should have been done with the help of "
             "`typing.get_type_hints` method by default"				)

       _SCREAMING_SNAKE_CASE   :				Any							    = kwargs.pop("aliases"  ,     []				)
       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [aliases]

       _SCREAMING_SNAKE_CASE   :				Dict							    = getattr(field.type  ,     "__origin__"  ,     field.type				)
       if origin_type is Union or (hasattr(snake_case__  ,     "UnionType"				) and isinstance(snake_case__  ,     types.UnionType				)):
         if str not in field.type.__args__ and (
             len(field.type.__args__				) != 2 or type(snake_case__				) not in field.type.__args__
         ):
           raise ValueError(
               "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
               " the argument parser only supports one type per argument."
               F''' Problem encountered in field \'{field.name}\'.'''				)
         if type(snake_case__				) not in field.type.__args__:
           # filter `str` in Union
           _SCREAMING_SNAKE_CASE   :				Any							    = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
           _SCREAMING_SNAKE_CASE   :				int							    = getattr(field.type  ,     "__origin__"  ,     field.type				)
         elif bool not in field.type.__args__:
           # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (
               field.type.__args__[0] if isinstance(snake_case__  ,     field.type.__args__[1]				) else field.type.__args__[1]
           )
           _SCREAMING_SNAKE_CASE   :				List[str]							    = getattr(field.type  ,     "__origin__"  ,     field.type				)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
       _SCREAMING_SNAKE_CASE   :				Any							    = {}
       if origin_type is Literal or (isinstance(field.type  ,     snake_case__				) and issubclass(field.type  ,     snake_case__				)):
         if origin_type is Literal:
           _SCREAMING_SNAKE_CASE   :				str							    = field.type.__args__
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = [x.value for x in field.type]

         _SCREAMING_SNAKE_CASE   :				List[str]							    = make_choice_type_function(kwargs["choices"]				)

         if field.default is not dataclasses.MISSING:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = field.default
         else:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = True
       elif field.type is bool or field.type == Optional[bool]:
         # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
         # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
         _SCREAMING_SNAKE_CASE   :				Any							    = copy(snake_case__				)

         # Hack because type=bool in argparse does not behave as we want.
         _SCREAMING_SNAKE_CASE   :				int							    = string_to_bool
         if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
           # Default value is False if we have no default when of type bool.
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = False if field.default is dataclasses.MISSING else field.default
           # This is the value that will get picked if we don't include --field_name in any way
           _SCREAMING_SNAKE_CASE   :				int							    = default
           # This tells argparse we accept 0 or 1 value after --field_name
           _SCREAMING_SNAKE_CASE   :				str							    = "?"
           # This is the value that will get picked if we do --field_name (without value)
           _SCREAMING_SNAKE_CASE   :				Tuple							    = True
       elif isclass(snake_case__				) and issubclass(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				List[str]							    = field.type.__args__[0]
         _SCREAMING_SNAKE_CASE   :				str							    = "+"
         if field.default_factory is not dataclasses.MISSING:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = field.default_factory()
         elif field.default is dataclasses.MISSING:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = True
       else:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = field.type
         if field.default is not dataclasses.MISSING:
           _SCREAMING_SNAKE_CASE   :				Dict							    = field.default
         elif field.default_factory is not dataclasses.MISSING:
           _SCREAMING_SNAKE_CASE   :				Any							    = field.default_factory()
         else:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = True
       parser.add_argument(snake_case__  ,     *snake_case__  ,     **snake_case__				)

       # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
       # Order is important for arguments with the same destination!
       # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
       # here and we do not need those changes/additional keys.
       if field.default is True and (field.type is bool or field.type == Optional[bool]):
         _SCREAMING_SNAKE_CASE   :				int							    = False
         parser.add_argument(F'''--no_{field.name}'''  ,     action="store_false"  ,     dest=field.name  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if hasattr(snake_case__  ,     "_argument_group_name"				):
         _SCREAMING_SNAKE_CASE   :				int							    = self.add_argument_group(dtype._argument_group_name				)
       else:
         _SCREAMING_SNAKE_CASE   :				str							    = self

       try:
         _SCREAMING_SNAKE_CASE   :				Dict[str, type]							    = get_type_hints(snake_case__				)
       except NameError:
         raise RuntimeError(
             F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
             "removing line of `from __future__ import annotations` which opts in Postponed "
             "Evaluation of Annotations (PEP 563)"				)
       except TypeError as ex:
         # Remove this block when we drop Python 3.9 support
         if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(snake_case__				):
           _SCREAMING_SNAKE_CASE   :				int							    = ".".join(map(snake_case__  ,     sys.version_info[:3]				)				)
           raise RuntimeError(
               F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
               "line of `from __future__ import annotations` which opts in union types as "
               "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
               "support Python versions that lower than 3.10, you need to use "
               "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
               "`X | None`."				) from ex
         raise

       for field in dataclasses.fields(snake_case__				):
         if not field.init:
           continue
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = type_hints[field.name]
         self._parse_dataclass_field(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None  ,     snake_case__=False  ,     snake_case__=True  ,     snake_case__=None  ,     snake_case__=None  ,     ):




       """simple docstring"""


       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv				)):
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = []

         if args_filename:
           args_files.append(Path(snake_case__				)				)
         elif look_for_args_file and len(sys.argv				):
           args_files.append(Path(sys.argv[0]				).with_suffix(".args"				)				)

         # args files specified via command line flag should overwrite default args files so we add them last
         if args_file_flag:
           # Create special parser just to extract the args_file_flag values
           _SCREAMING_SNAKE_CASE   :				int							    = ArgumentParser()
           args_file_parser.add_argument(snake_case__  ,     type=snake_case__  ,     action="append"				)

           # Use only remaining args for further parsing (remove the args_file_flag)
           _SCREAMING_SNAKE_CASE   :				int							    = args_file_parser.parse_known_args(args=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Any							    = vars(snake_case__				).get(args_file_flag.lstrip("-"				)  ,     snake_case__				)

           if cmd_args_file_paths:
             args_files.extend([Path(snake_case__				) for p in cmd_args_file_paths]				)

         _SCREAMING_SNAKE_CASE   :				List[Any]							    = []
         for args_file in args_files:
           if args_file.exists():
             file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
         _SCREAMING_SNAKE_CASE   :				Any							    = file_args + args if args is not None else file_args + sys.argv[1:]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.parse_known_args(args=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
       for dtype in self.dataclass_types:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = {f.name for f in dataclasses.fields(snake_case__				) if f.init}
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = {k: v for k, v in vars(snake_case__				).items() if k in keys}
         for k in keys:
           delattr(snake_case__  ,     snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = dtype(**snake_case__				)
         outputs.append(snake_case__				)
       if len(namespace.__dict__				) > 0:
         # additional namespace.
         outputs.append(snake_case__				)
       if return_remaining_strings:
         return (*outputs, remaining_args)
       else:
         if remaining_args:
           raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''				)

         return (*outputs,)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = set(args.keys()				)
       _SCREAMING_SNAKE_CASE   :				str							    = []
       for dtype in self.dataclass_types:
         _SCREAMING_SNAKE_CASE   :				str							    = {f.name for f in dataclasses.fields(snake_case__				) if f.init}
         _SCREAMING_SNAKE_CASE   :				str							    = {k: v for k, v in args.items() if k in keys}
         unused_keys.difference_update(inputs.keys()				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = dtype(**snake_case__				)
         outputs.append(snake_case__				)
       if not allow_extra_keys and unused_keys:
         raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(snake_case__				)}'''				)
       return tuple(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False				):




       """simple docstring"""


       with open(Path(snake_case__				)  ,     encoding="utf-8"				) as open_json_file:
         _SCREAMING_SNAKE_CASE   :				int							    = json.loads(open_json_file.read()				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.parse_dict(snake_case__  ,     allow_extra_keys=snake_case__				)
       return tuple(snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = False				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.parse_dict(yaml.safe_load(Path(snake_case__				).read_text()				)  ,     allow_extra_keys=snake_case__				)
       return tuple(snake_case__				)





"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    Optional[Any]:
  _SCREAMING_SNAKE_CASE   :				Any							    = [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]				)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			StableDiffusionLatentUpscalePipeline
     A__      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         """height""",
         """width""",
         """cross_attention_kwargs""",
         """negative_prompt_embeds""",
         """prompt_embeds""",
     }
     A__      =			PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
     A__      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     A__      =			frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     A__      =			frozenset([]  )

     A__      =			True
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = 1
       _SCREAMING_SNAKE_CASE   :				int							    = 4
       _SCREAMING_SNAKE_CASE   :				str							    = (16, 16)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = floats_tensor((batch_size, num_channels) + sizes  ,     rng=random.Random(0				)				).to(snake_case__				)
       return image
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       torch.manual_seed(0				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = UNetaDConditionModel(
           act_fn="gelu"  ,     attention_head_dim=8  ,     norm_num_groups=snake_case__  ,     block_out_channels=[32, 32, 64, 64]  ,     time_cond_proj_dim=160  ,     conv_in_kernel=1  ,     conv_out_kernel=1  ,     cross_attention_dim=32  ,     down_block_types=(
               "KDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
           )  ,     in_channels=8  ,     mid_block_type=snake_case__  ,     only_cross_attention=snake_case__  ,     out_channels=5  ,     resnet_time_scale_shift="scale_shift"  ,     time_embedding_type="fourier"  ,     timestep_post_act="gelu"  ,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")  ,     )
       _SCREAMING_SNAKE_CASE   :				Any							    = AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]  ,     in_channels=3  ,     out_channels=3  ,     down_block_types=[
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
           ]  ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]  ,     latent_channels=4  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = EulerDiscreteScheduler(prediction_type="sample"				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = CLIPTextConfig(
           bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=32  ,     intermediate_size=37  ,     layer_norm_eps=1E-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1000  ,     hidden_act="quick_gelu"  ,     projection_dim=512  ,     )
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = CLIPTextModel(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				str							    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

       _SCREAMING_SNAKE_CASE   :				int							    = {
           "unet": model.eval(),
           "vae": vae.eval(),
           "scheduler": scheduler,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
       }

       return components
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__=0				):




       """simple docstring"""


       if str(snake_case__				).startswith("mps"				):
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = torch.manual_seed(snake_case__				)
       else:
         _SCREAMING_SNAKE_CASE   :				Dict							    = torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = {
           "prompt": "A painting of a squirrel eating a burger",
           "image": self.dummy_image.cpu(),
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }
       return inputs
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = "cpu"

       _SCREAMING_SNAKE_CASE   :				Any							    = self.get_dummy_components()
       _SCREAMING_SNAKE_CASE   :				str							    = self.pipeline_class(**snake_case__				)
       pipe.to(snake_case__				)
       pipe.set_progress_bar_config(disable=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				str							    = self.get_dummy_inputs(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = pipe(**snake_case__				).images
       _SCREAMING_SNAKE_CASE   :				str							    = image[0, -3:, -3:, -1]

       self.assertEqual(image.shape  ,     (1, 256, 256, 3)				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = np.array(
           [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = np.abs(image_slice.flatten() - expected_slice				).max()
       self.assertLessEqual(snake_case__  ,     1E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_attention_slicing_forward_pass(expected_max_diff=7E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_cpu_offload_forward_pass(expected_max_diff=3E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_inference_batch_single_identical(expected_max_diff=7E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_save_load_local(expected_max_difference=3E-3				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().test_save_load_optional_components(expected_max_difference=3E-3				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = [
           "DDIMScheduler",
           "DDPMScheduler",
           "PNDMScheduler",
           "HeunDiscreteScheduler",
           "EulerAncestralDiscreteScheduler",
           "KDPM2DiscreteScheduler",
           "KDPM2AncestralDiscreteScheduler",
           "DPMSolverSDEScheduler",
       ]
       _SCREAMING_SNAKE_CASE   :				Any							    = self.get_dummy_components()
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.pipeline_class(**snake_case__				)

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=snake_case__				)

       pipe.to(snake_case__				)
       pipe.set_progress_bar_config(disable=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				str							    = self.get_dummy_inputs(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 2

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
       for scheduler_enum in KarrasDiffusionSchedulers:
         if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

         _SCREAMING_SNAKE_CASE   :				List[str]							    = getattr(snake_case__  ,     scheduler_enum.name				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = scheduler_cls.from_config(pipe.scheduler.config				)
         _SCREAMING_SNAKE_CASE   :				int							    = pipe(**snake_case__				)[0]
         outputs.append(snake_case__				)

       assert check_same_shape(snake_case__				)
@require_torch_gpu
@slow
class 			UpperCamelCase      (							unittest.TestCase  ):
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = torch.manual_seed(33				)

       _SCREAMING_SNAKE_CASE   :				str							    = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"  ,     torch_dtype=torch.floataa				)
       pipe.to("cuda"				)

       _SCREAMING_SNAKE_CASE   :				int							    = StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"  ,     torch_dtype=torch.floataa				)
       upscaler.to("cuda"				)

       _SCREAMING_SNAKE_CASE   :				str							    = "a photo of an astronaut high resolution, unreal engine, ultra realistic"

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = pipe(snake_case__  ,     generator=snake_case__  ,     output_type="latent"				).images

       _SCREAMING_SNAKE_CASE   :				Tuple							    = upscaler(
           prompt=snake_case__  ,     image=snake_case__  ,     num_inference_steps=20  ,     guidance_scale=0  ,     generator=snake_case__  ,     output_type="np"  ,     ).images[0]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"				)
       assert np.abs((expected_image - image).mean()				) < 5E-2




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = torch.manual_seed(33				)

       _SCREAMING_SNAKE_CASE   :				str							    = StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"  ,     torch_dtype=torch.floataa				)
       upscaler.to("cuda"				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

       _SCREAMING_SNAKE_CASE   :				List[str]							    = load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = upscaler(
           prompt=snake_case__  ,     image=snake_case__  ,     num_inference_steps=20  ,     guidance_scale=0  ,     generator=snake_case__  ,     output_type="np"  ,     ).images[0]

       _SCREAMING_SNAKE_CASE   :				int							    = load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"				)
       assert np.abs((expected_image - image).max()				) < 5E-2



