






"""simple docstring"""





UpperCamelCase_						:					List[Any]      				=			1000



def      __lowercase       (     a			:					Optional[int]			)       ->       int:
      return x * x



def      __lowercase       (     a			:					str							,		a			:					Optional[int]							,		a			:					Any							,		a			:					Tuple = 4			)       ->       Dict:
      assert callable(
          _UpperCAmelCase			), f'''the function(object) passed should be callable your input : {function}'''
      assert isinstance(_UpperCAmelCase							,		(float, int)			), f'''a should be float or integer your input : {a}'''
      assert isinstance(function(_UpperCAmelCase			)							,		(float, int)			), (
          "the function should return integer or float return type of your function, "
          f'''{type(_UpperCAmelCase			)}'''
      )
      assert isinstance(_UpperCAmelCase							,		(float, int)			), f'''b should be float or integer your input : {b}'''
      assert (
          isinstance(_UpperCAmelCase							,		_UpperCAmelCase			) and precision > 0
      ), f'''precision should be positive integer your input : {precision}'''

      # just applying the formula of simpson for approximate integration written in
      # mentioned article in first comment of this file and above this function

      __snake_case   :     Any	      =(b - a) / N_STEPS
      __snake_case   :     Any	      =function(_UpperCAmelCase			) + function(_UpperCAmelCase			)

      for i in range(1							,		_UpperCAmelCase			):
            __snake_case   :     Union[str, Any]	      =a + h * i
            result += function(_UpperCAmelCase			) * (4 if i % 2 else 2)

      result *= h / 3
      return round(_UpperCAmelCase							,		_UpperCAmelCase			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()







import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
		from .testing_utils import (
		    floats_tensor,
		    load_hf_numpy,
		    load_image,
		    load_numpy,
		    load_pt,
		    nightly,
		    parse_flag_from_env,
		    print_tensor_test,
		    require_torch_a,
		    require_torch_gpu,
		    skip_mps,
		    slow,
		    torch_all_close,
		    torch_device,
		)
		from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__magic_name__       =     get_logger(__name__)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if version.parse(_UpperCAmelCase				) < version.parse(_UpperCAmelCase				):
										if "dev" in min_version:
															lowercase						   =       (
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															lowercase						   =       f"""This example requires a minimum version of {min_version},"""
										error_message += f""" but the version found is {__version__}.\n"""
										raise ImportError(_UpperCAmelCase				)






