



"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__ = 100       ,							):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			x_start
  _SCREAMING_SNAKE_CASE        =			fnc(lowercase__    )
  _SCREAMING_SNAKE_CASE        =			0.0

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

    # Increment step
    _SCREAMING_SNAKE_CASE        =			xa
    _SCREAMING_SNAKE_CASE        =			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:""")
    lowerCamelCase				:     Dict								=  1_0
    while i <= 1_0_0_0_0_0:
        lowerCamelCase				:     Optional[Any]								=  trapezoidal_area(f, -5, 5, i)
        print(F'''with {i} steps: {area}''')
        i *= 1_0




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

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


