



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__  :  Union[str, Any]       = {
    '''configuration_marian''': ['''MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarianConfig''', '''MarianOnnxConfig'''],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Union[str, Any]       = ['''MarianTokenizer''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Tuple       = [
						    '''MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MarianForCausalLM''',
						    '''MarianModel''',
						    '''MarianMTModel''',
						    '''MarianPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  str       = ['''TFMarianModel''', '''TFMarianMTModel''', '''TFMarianPreTrainedModel''']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Optional[int]       = ['''FlaxMarianModel''', '''FlaxMarianMTModel''', '''FlaxMarianPreTrainedModel''']

if TYPE_CHECKING:
						from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_marian import MarianTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_marian import (
												    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MarianForCausalLM,
												    MarianModel,
												    MarianMTModel,
												    MarianPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
						import sys

						A__  :  str       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










A__  :  str       = 1000


def       UpperCamelCase(  __UpperCamelCase							:		float			):
		return x * x


def       UpperCamelCase(  __UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		float					,__UpperCamelCase							:		float					,__UpperCamelCase							:		int = 4			):
		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

		lowerCAmelCase_      :					List[str]     =			(b - a) / N_STEPS
		lowerCAmelCase_      :					str     =			function(__UpperCamelCase			) + function(__UpperCamelCase			)

		for i in range(1					,__UpperCamelCase			):
				lowerCAmelCase_      :					Optional[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()





