





def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Tuple					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   (boundary[1] - boundary[0]) / steps
						__SCREAMING_SNAKE_CASE:				str           =   boundary[0]
						__SCREAMING_SNAKE_CASE:				Tuple           =   boundary[1]
						__SCREAMING_SNAKE_CASE:				Any           =   make_points(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE					)
						__SCREAMING_SNAKE_CASE:				List[Any]           =   0.0
						y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE					)
						for i in x_i:
												# print(i)
												y += h * f(_SCREAMING_SNAKE_CASE					)
						y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE					)
						return y






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   int					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   a + h
						while x < (b - h):
												yield x
												__SCREAMING_SNAKE_CASE:				Tuple           =   x + h






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple					)      ->    Any:  # enter your function here

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   (x - 0) * (x - 0)
						return y






def 		lowerCAmelCase  (	)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   0.0  # Lower bound of integration
						__SCREAMING_SNAKE_CASE:				Any           =   1.0  # Upper bound of integration
						__SCREAMING_SNAKE_CASE:				List[Any]           =   10.0  # define number of steps or resolution
						__SCREAMING_SNAKE_CASE:				List[str]           =   [a, b]  # define boundary of integration
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   method_a(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE					)
						print(F"""y = {y}"""					)


if __name__ == "__main__":
							main()










from typing import TYPE_CHECKING

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


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





