


'''simple docstring'''
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

UpperCAmelCase_ :     Any								=      {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Any=False,				UpperCamelCase__       :					List[Any]=True			):
 if model_type not in MODEL_CLASSES:
  raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

 __A     ,__A     ,__A     ,__A  :		List[str]       =			MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  __A  :		List[Any]       =			cached_file(UpperCamelCase__,				UpperCamelCase__,				force_download=not use_cached_models			)
 __A  :		Dict       =			config_class.from_json_file(UpperCamelCase__			)
 __A  :		int       =			True
 __A  :		List[Any]       =			True
 print(f"""Building TensorFlow model from configuration: {config}"""			)
 __A  :		Tuple       =			model_class(UpperCamelCase__			)

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  __A  :		Optional[Any]       =			cached_file(
      UpperCamelCase__,				UpperCamelCase__,				force_download=not use_cached_models			)
 # Load PyTorch checkpoint in tf2 model:
 __A  :		List[Any]       =			load_pytorch_checkpoint_in_tfa_model(UpperCamelCase__,				UpperCamelCase__			)

 if compare_with_pt_model:
  __A  :		Any       =			tf_model(tf_model.dummy_inputs,				training=UpperCamelCase__			)  # build the network

  __A  :		Any       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
  __A  :		Any       =			pt_model_class.from_pretrained(
      pretrained_model_name_or_path=UpperCamelCase__,				config=UpperCamelCase__,				state_dict=UpperCamelCase__			)

  with torch.no_grad():
   __A  :		int       =			pt_model(**pt_model.dummy_inputs			)

  __A  :		Any       =			pto[0].numpy()
  __A  :		List[str]       =			tfo[0].numpy()
  __A  :		List[str]       =			np.amax(np.abs(np_pt - np_tf			)			)
  print(f"""Max absolute difference between models outputs {diff}"""			)
  assert diff <= 2E-2, f"""Error, model absolute difference is >2e-2: {diff}"""

 # Save pytorch-model
 print(f"""Save TensorFlow model to {tf_dump_path}"""			)
 tf_model.save_weights(UpperCamelCase__,				save_format='h5'			)


def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					Optional[int]=False,				UpperCamelCase__       :					Dict=False,				UpperCamelCase__       :					int=False,				UpperCamelCase__       :					List[Any]=False,				):
 if args_model_type is None:
  __A  :		Any       =			list(MODEL_CLASSES.keys()			)
 else:
  __A  :		List[str]       =			[args_model_type]

 for j, model_type in enumerate(UpperCamelCase__,				start=1			):
  print('=' * 100			)
  print(f""" Converting model type {j}/{len(UpperCamelCase__			)}: {model_type}"""			)
  print('=' * 100			)
  if model_type not in MODEL_CLASSES:
   raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

  __A     ,__A     ,__A     ,__A     ,__A  :		List[str]       =			MODEL_CLASSES[model_type]

  if model_shortcut_names_or_path is None:
   __A  :		Tuple       =			list(aws_model_maps.keys()			)
  if config_shortcut_names_or_path is None:
   __A  :		Optional[Any]       =			model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(UpperCamelCase__,				UpperCamelCase__			),				start=1			):
   print('-' * 100			)
   if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
    if not only_convert_finetuned_models:
     print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""			)
     continue
    __A  :		int       =			model_shortcut_name
   elif only_convert_finetuned_models:
    print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""			)
    continue
   print(
       f"""    Converting checkpoint {i}/{len(UpperCamelCase__			)}: {model_shortcut_name} - model_type {model_type}"""			)
   print('-' * 100			)

   if config_shortcut_name in aws_config_map:
    __A  :		Optional[int]       =			cached_file(UpperCamelCase__,				UpperCamelCase__,				force_download=not use_cached_models			)
   else:
    __A  :		Optional[Any]       =			config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    __A  :		int       =			cached_file(UpperCamelCase__,				UpperCamelCase__,				force_download=not use_cached_models			)
   else:
    __A  :		str       =			model_shortcut_name

   if os.path.isfile(UpperCamelCase__			):
    __A  :		Union[str, Any]       =			'converted_model'

   convert_pt_checkpoint_to_tf(
       model_type=UpperCamelCase__,				pytorch_checkpoint_path=UpperCamelCase__,				config_file=UpperCamelCase__,				tf_dump_path=os.path.join(UpperCamelCase__,				model_shortcut_name + '-tf_model.h5'			),				compare_with_pt_model=UpperCamelCase__,				)
   if remove_cached_files:
    os.remove(UpperCamelCase__			)
    os.remove(UpperCamelCase__			)


if __name__ == "__main__":
 UpperCAmelCase_ :     List[str]								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
 )
 parser.add_argument(
     '--model_type',
     default=None,
     type=str,
     help=(
         f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
         'convert all the models from AWS.'
     ),
 )
 parser.add_argument(
     '--pytorch_checkpoint_path',
     default=None,
     type=str,
     help=(
         'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
         'If not given, will download and convert all the checkpoints from AWS.'
     ),
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     help=(
         'The config json file corresponding to the pre-trained model. \n'
         'This specifies the model architecture. If not given and '
         '--pytorch_checkpoint_path is not given or is a shortcut name '
         'use the configuration associated to the shortcut name on the AWS'
     ),
 )
 parser.add_argument(
     '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
 )
 parser.add_argument(
     '--use_cached_models',
     action='store_true',
     help='Use cached models if possible instead of updating to latest checkpoint versions.',
 )
 parser.add_argument(
     '--remove_cached_files',
     action='store_true',
     help='Remove pytorch models after conversion (save memory when converting in batches).',
 )
 parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
 UpperCAmelCase_ :     List[str]								=      parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )










'''simple docstring'''
from math import pi, sqrt, tan


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if side_length < 0:
  raise ValueError('surface_area_cube() only accepts non-negative values'			)
 return 6 * side_length**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if length < 0 or breadth < 0 or height < 0:
  raise ValueError('surface_area_cuboid() only accepts non-negative values'			)
 return 2 * ((length * breadth) + (breadth * height) + (length * height))


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if radius < 0:
  raise ValueError('surface_area_sphere() only accepts non-negative values'			)
 return 4 * pi * radius**2


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if radius < 0:
  raise ValueError('surface_area_hemisphere() only accepts non-negative values'			)
 return 3 * pi * radius**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius < 0 or height < 0:
  raise ValueError('surface_area_cone() only accepts non-negative values'			)
 return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      'surface_area_conical_frustum() only accepts non-negative values'			)
 __A  :		Optional[Any]       =			(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius < 0 or height < 0:
  raise ValueError('surface_area_cylinder() only accepts non-negative values'			)
 return 2 * pi * radius * (height + radius)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError('surface_area_torus() only accepts non-negative values'			)
 if torus_radius < tube_radius:
  raise ValueError(
      'surface_area_torus() does not support spindle or self intersecting tori'			)
 return 4 * pow(UpperCamelCase__,				2			) * torus_radius * tube_radius


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if length < 0 or width < 0:
  raise ValueError('area_rectangle() only accepts non-negative values'			)
 return length * width


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if side_length < 0:
  raise ValueError('area_square() only accepts non-negative values'			)
 return side_length**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if base < 0 or height < 0:
  raise ValueError('area_triangle() only accepts non-negative values'			)
 return (base * height) / 2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if sidea < 0 or sidea < 0 or sidea < 0:
  raise ValueError('area_triangle_three_sides() only accepts non-negative values'			)
 elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
  raise ValueError('Given three sides do not form a triangle'			)
 __A  :		int       =			(sidea + sidea + sidea) / 2
 __A  :		List[Any]       =			sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)			)
 return area


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if base < 0 or height < 0:
  raise ValueError('area_parallelogram() only accepts non-negative values'			)
 return base * height


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if basea < 0 or basea < 0 or height < 0:
  raise ValueError('area_trapezium() only accepts non-negative values'			)
 return 1 / 2 * (basea + basea) * height


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if radius < 0:
  raise ValueError('area_circle() only accepts non-negative values'			)
 return pi * radius**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius_x < 0 or radius_y < 0:
  raise ValueError('area_ellipse() only accepts non-negative values'			)
 return pi * radius_x * radius_y


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if diagonal_a < 0 or diagonal_a < 0:
  raise ValueError('area_rhombus() only accepts non-negative values'			)
 return 1 / 2 * diagonal_a * diagonal_a


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					float			):
 if not isinstance(UpperCamelCase__,				UpperCamelCase__			) or sides < 3:
  raise ValueError(
      'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'			)
 elif length < 0:
  raise ValueError(
      'area_reg_polygon() only accepts non-negative values as \
length of a side'			)
 return (sides * length**2) / (4 * tan(pi / sides			))
 return (sides * length**2) / (4 * tan(pi / sides			))


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

 print('[DEMO] Areas of various geometric shapes: \n')
 print(f'''Rectangle: {area_rectangle(1_0, 2_0) = }''')
 print(f'''Square: {area_square(1_0) = }''')
 print(f'''Triangle: {area_triangle(1_0, 1_0) = }''')
 print(f'''Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }''')
 print(f'''Parallelogram: {area_parallelogram(1_0, 2_0) = }''')
 print(f'''Rhombus: {area_rhombus(1_0, 2_0) = }''')
 print(f'''Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }''')
 print(f'''Circle: {area_circle(2_0) = }''')
 print(f'''Ellipse: {area_ellipse(1_0, 2_0) = }''')
 print('\nSurface Areas of various geometric shapes: \n')
 print(f'''Cube: {surface_area_cube(2_0) = }''')
 print(f'''Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }''')
 print(f'''Sphere: {surface_area_sphere(2_0) = }''')
 print(f'''Hemisphere: {surface_area_hemisphere(2_0) = }''')
 print(f'''Cone: {surface_area_cone(1_0, 2_0) = }''')
 print(f'''Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }''')
 print(f'''Cylinder: {surface_area_cylinder(1_0, 2_0) = }''')
 print(f'''Torus: {surface_area_torus(2_0, 1_0) = }''')
 print(f'''Equilateral Triangle: {area_reg_polygon(3, 1_0) = }''')
 print(f'''Square: {area_reg_polygon(4, 1_0) = }''')
 print(f'''Reqular Pentagon: {area_reg_polygon(5, 1_0) = }''')






