




"""simple docstring"""




from typing import TYPE_CHECKING

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


a_            =   {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_            =   ['BartTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_            =   [
       'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BartForCausalLM',
       'BartForConditionalGeneration',
       'BartForQuestionAnswering',
       'BartForSequenceClassification',
       'BartModel',
       'BartPretrainedModel',
       'PretrainedBartModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_            =   [
       'TFBartForConditionalGeneration',
       'TFBartForSequenceClassification',
       'TFBartModel',
       'TFBartPretrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_            =   [
       'FlaxBartDecoderPreTrainedModel',
       'FlaxBartForCausalLM',
       'FlaxBartForConditionalGeneration',
       'FlaxBartForQuestionAnswering',
       'FlaxBartForSequenceClassification',
       'FlaxBartModel',
       'FlaxBartPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

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









"""simple docstring"""




a_            =   1_0_0_0







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return x * x







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = 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

			__lowercase	:       Optional[Any]				=   (b - a) / N_STEPS
			__lowercase	:       Optional[int]				=   function(__UpperCamelCase					) + function(__UpperCamelCase					)

			for i in range(1					,							__UpperCamelCase					):
						__lowercase	:       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()



