


"""simple docstring"""







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			 =   {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["T5Tokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["T5TokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "T5EncoderModel",
						    "T5ForConditionalGeneration",
						    "T5Model",
						    "T5PreTrainedModel",
						    "load_tf_weights_in_t5",
						    "T5ForQuestionAnswering",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFT5EncoderModel",
						    "TFT5ForConditionalGeneration",
						    "TFT5Model",
						    "TFT5PreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "FlaxT5EncoderModel",
						    "FlaxT5ForConditionalGeneration",
						    "FlaxT5Model",
						    "FlaxT5PreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta import TaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ta_fast import TaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ta import (
												    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TaEncoderModel,
												    TaForConditionalGeneration,
												    TaForQuestionAnswering,
												    TaModel,
												    TaPreTrainedModel,
												    load_tf_weights_in_ta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ta import (
												    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTaEncoderModel,
												    TFTaForConditionalGeneration,
												    TFTaModel,
												    TFTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_ta import (
												    FlaxTaEncoderModel,
												    FlaxTaForConditionalGeneration,
												    FlaxTaModel,
												    FlaxTaPreTrainedModel,
												)


else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase = 100	,			):





						"""simple docstring"""

						UpperCAmelCase__		=    x_start
						UpperCAmelCase__		=    fnc(_UpperCamelCase )
						UpperCAmelCase__		=    0.0

						for _ in range(_UpperCamelCase ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												UpperCAmelCase__		=    (x_end - x_start) / steps + xa
												UpperCAmelCase__		=    fnc(_UpperCamelCase )
												area += abs(fxa + fxa ) * (xa - xa) / 2

												# Increment step
												UpperCAmelCase__		=    xa
												UpperCAmelCase__		=    fxa
						return area


if __name__ == "__main__":






						def 					a__ (       _UpperCamelCase ):





												"""simple docstring"""

												return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						__a			 =   10
						while i <= 10_00_00:
												__a			 =   trapezoidal_area(f, -5, 5, i)
												print(f'''with {i} steps: {area}''')
												i *= 10