




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase    				=		{
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase    				=		["ImageGPTFeatureExtractor"]
	_lowerCamelCase    				=		["ImageGPTImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase    				=		[
	    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ImageGPTForCausalImageModeling",
	    "ImageGPTForImageClassification",
	    "ImageGPTModel",
	    "ImageGPTPreTrainedModel",
	    "load_tf_weights_in_imagegpt",
	]


if TYPE_CHECKING:
	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
		from .image_processing_imagegpt import ImageGPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_imagegpt import (
		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ImageGPTForCausalImageModeling,
		    ImageGPTForImageClassification,
		    ImageGPTModel,
		    ImageGPTPreTrainedModel,
		    load_tf_weights_in_imagegpt,
		)

else:
	import sys

	_lowerCamelCase    				=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











_lowerCamelCase    				=		10_00




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		float   )						->							float:
    return x * x




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		float ,  __UpperCamelCase    :		float ,  __UpperCamelCase    :		int = 4   )						->							float:
    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

    UpperCAmelCase_											=  (b - a) / N_STEPS
    UpperCAmelCase_											=  function(__UpperCamelCase   ) + function(__UpperCamelCase   )

    for i in range(1 ,  __UpperCamelCase   ):
        UpperCAmelCase_											=  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()





