





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


a__      :     Union[str, Any]         =		NewType('DataClass', Any)
a__      :     int         =		NewType('DataClassType', Any)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[Any]:
     '''simple docstring'''


     if isinstance(_UpperCAmelCase  ,						_UpperCAmelCase				):
          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      UpperCAmelCase_    (    _UpperCAmelCase							:list				)	->				Callable[[str], Any]:
     '''simple docstring'''


     A_          =       {str(_UpperCAmelCase				): choice for choice in choices}
     return lambda _UpperCAmelCase				: str_to_choice.get(_UpperCAmelCase  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    *,
    _UpperCAmelCase							:Union[str, List[str]] = None  ,						_UpperCAmelCase							:str = None  ,						_UpperCAmelCase							:Any = dataclasses.MISSING  ,						_UpperCAmelCase							:Callable[[], Any] = dataclasses.MISSING  ,						_UpperCAmelCase							:dict = None  ,						**_UpperCAmelCase							:List[Any]  ,						)	->				dataclasses.Field:
     '''simple docstring'''


     if metadata is None:
          # Important, don't use as default param in function signature because dict is mutable and shared across function calls
          A_          =       {}
     if aliases is not None:
          A_          =       aliases
     if help is not None:
          A_          =       help

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





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Iterable[DataClassType]


  def __init__(							self ,__snake_case ,**__snake_case ):



       """simple docstring"""





       if "formatter_class" not in kwargs:
            A_          =       ArgumentDefaultsHelpFormatter
       super().__init__(**__snake_case )
       if dataclasses.is_dataclass(__snake_case ):
            A_          =       [dataclass_types]
       A_          =       list(__snake_case )
       for dtype in self.dataclass_types:
            self._add_dataclass_arguments(__snake_case )


  @staticmethod
  def 						__UpperCAmelCase     (							__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       f'--{field.name}'
       A_          =       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''' )

       A_          =       kwargs.pop('''aliases''' ,[] )
       if isinstance(__snake_case ,__snake_case ):
            A_          =       [aliases]

       A_          =       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
                 A_          =       field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                 A_          =       getattr(field.type ,'''__origin__''' ,field.type )
            elif bool not in field.type.__args__:
                 # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                 A_          =       (
                     field.type.__args__[0] if isinstance(__snake_case ,field.type.__args__[1] ) else field.type.__args__[1]
                 )
                 A_          =       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)
       A_          =       {}
       if origin_type is Literal or (isinstance(field.type ,__snake_case ) and issubclass(field.type ,__snake_case )):
            if origin_type is Literal:
                 A_          =       field.type.__args__
            else:
                 A_          =       [x.value for x in field.type]

            A_          =       make_choice_type_function(kwargs['''choices'''] )

            if field.default is not dataclasses.MISSING:
                 A_          =       field.default
            else:
                 A_          =       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
            A_          =       copy(__snake_case )

            # Hack because type=bool in argparse does not behave as we want.
            A_          =       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.
                 A_          =       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
                 A_          =       default
                 # This tells argparse we accept 0 or 1 value after --field_name
                 A_          =       '''?'''
                 # This is the value that will get picked if we do --field_name (without value)
                 A_          =       True
       elif isclass(__snake_case ) and issubclass(__snake_case ,__snake_case ):
            A_          =       field.type.__args__[0]
            A_          =       '''+'''
            if field.default_factory is not dataclasses.MISSING:
                 A_          =       field.default_factory()
            elif field.default is dataclasses.MISSING:
                 A_          =       True
       else:
            A_          =       field.type
            if field.default is not dataclasses.MISSING:
                 A_          =       field.default
            elif field.default_factory is not dataclasses.MISSING:
                 A_          =       field.default_factory()
            else:
                 A_          =       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]):
            A_          =       False
            parser.add_argument(f'--no_{field.name}' ,action='''store_false''' ,dest=field.name ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       if hasattr(__snake_case ,'''_argument_group_name''' ):
            A_          =       self.add_argument_group(dtype._argument_group_name )
       else:
            A_          =       self

       try:
            A_          =       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, 1_0) and "unsupported operand type(s) for |" in str(__snake_case ):
                 A_          =       '''.'''.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
            A_          =       type_hints[field.name]
            self._parse_dataclass_field(__snake_case ,__snake_case )


  def 						__UpperCAmelCase     (							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 )):
            A_          =       []

            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
                 A_          =       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)
                 A_   ,    A_          =       args_file_parser.parse_known_args(args=__snake_case )
                 A_          =       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] )

            A_          =       []
            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
            A_          =       file_args + args if args is not None else file_args + sys.argv[1:]
       A_   ,    A_          =       self.parse_known_args(args=__snake_case )
       A_          =       []
       for dtype in self.dataclass_types:
            A_          =       {f.name for f in dataclasses.fields(__snake_case ) if f.init}
            A_          =       {k: v for k, v in vars(__snake_case ).items() if k in keys}
            for k in keys:
                 delattr(__snake_case ,__snake_case )
            A_          =       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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = False ):



       """simple docstring"""





       A_          =       set(args.keys() )
       A_          =       []
       for dtype in self.dataclass_types:
            A_          =       {f.name for f in dataclasses.fields(__snake_case ) if f.init}
            A_          =       {k: v for k, v in args.items() if k in keys}
            unused_keys.difference_update(inputs.keys() )
            A_          =       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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = False ):



       """simple docstring"""





       with open(Path(__snake_case ) ,encoding='''utf-8''' ) as open_json_file:
            A_          =       json.loads(open_json_file.read() )
       A_          =       self.parse_dict(__snake_case ,allow_extra_keys=__snake_case )
       return tuple(__snake_case )




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = False ):



       """simple docstring"""





       A_          =       self.parse_dict(yaml.safe_load(Path(__snake_case ).read_text() ) ,allow_extra_keys=__snake_case )
       return tuple(__snake_case )










import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





@slow
@require_torch_gpu
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

       A_          =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       A_          =       torch.manual_seed(0 )
       A_          =       pipe(
           image=__snake_case ,generator=__snake_case ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

       A_          =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       A_          =       np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



