




from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

_lowerCamelCase      :						Dict							=  6_378_137.0
_lowerCamelCase      :						Union[str, Any]							=  6_356_752.314_245
_lowerCamelCase      :						List[Any]							=  6378137







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			float:



       """simple docstring"""

       A__ 					=       (AXIS_A - AXIS_B) / AXIS_A
       # Parametric latitudes
       # https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)

       # Compute central angle between two points
       # using haversine theta. sigma =  haversine_distance / equatorial radius
       A__ 					=       haversine_distance(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		) / EQUATORIAL_RADIUS

       # Intermediate P and Q values
       A__ 					=       (b_lata + b_lata) / 2
       A__ 					=       (b_lata - b_lata) / 2

       # Intermediate X value
       # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
       A__ 					=       (sin(lowercase_		) ** 2) * (cos(lowercase_		) ** 2)
       A__ 					=       cos(sigma / 2		) ** 2
       A__ 					=       (sigma - sin(lowercase_		)) * (x_numerator / x_demonimator)

       # Intermediate Y value
       # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
       A__ 					=       (cos(lowercase_		) ** 2) * (sin(lowercase_		) ** 2)
       A__ 					=       sin(sigma / 2		) ** 2
       A__ 					=       (sigma + sin(lowercase_		)) * (y_numerator / y_denominator)

       return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
  import doctest

  doctest.testmod()






'''simple docstring'''







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       lowercase					(_A       ):







       """simple docstring"""







       return ConvertCommand(
           args.model_type ,    args.tf_checkpoint ,    args.pytorch_dump_output ,    args.config ,    args.finetuning_task_name       )


lowerCAmelCase     :  Optional[int]											=    """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @staticmethod
     def a						(			snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							parser.add_parser(
                'convert'   ,						help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'   ,						)
            train_parser.add_argument('--model_type'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Model\'s type.' )
            train_parser.add_argument(
                '--tf_checkpoint'   ,						type=snake_case__   ,						required=snake_case__   ,						help='TensorFlow checkpoint path or folder.' )
            train_parser.add_argument(
                '--pytorch_dump_output'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Path to the PyTorch saved model output.' )
            train_parser.add_argument('--config'   ,						type=snake_case__   ,						default=''   ,						help='Configuration file path or folder.' )
            train_parser.add_argument(
                '--finetuning_task_name'   ,						type=snake_case__   ,						default=snake_case__   ,						help='Optional fine-tuning task name if the TF model was a finetuned model.'   ,						)
            train_parser.set_defaults(func=snake_case__ )







     def __init__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						*snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							logging.get_logger('transformers-cli/converting' )

            self._logger.info(F'Loading model {model_type}' )
            _lowerCAmelCase     :				Any       =							model_type
            _lowerCAmelCase     :				Any       =							tf_checkpoint
            _lowerCAmelCase     :				List[str]       =							pytorch_dump_output
            _lowerCAmelCase     :				int       =							config
            _lowerCAmelCase     :				str       =							finetuning_task_name







     def a						(			self ):

            '''simple docstring'''

            if self._model_type == "albert":
                   try:
                          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "bert":
                   try:
                          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "funnel":
                   try:
                          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "t5":
                   try:
                          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "gpt":
                   from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                       convert_openai_checkpoint_to_pytorch,
                   )

                   convert_openai_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "transfo_xl":
                   try:
                          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                              convert_transfo_xl_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   if "ckpt" in self._tf_checkpoint.lower():
                          _lowerCAmelCase     :				List[Any]       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Optional[int]       =							''
                   else:
                          _lowerCAmelCase     :				int       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Dict       =							''
                   convert_transfo_xl_checkpoint_to_pytorch(
                       snake_case__   ,						self._config   ,						self._pytorch_dump_output   ,						snake_case__ )
            elif self._model_type == "gpt2":
                   try:
                          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                              convert_gpta_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "xlnet":
                   try:
                          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                              convert_xlnet_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_xlnet_checkpoint_to_pytorch(
                       self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output   ,						self._finetuning_task_name )
            elif self._model_type == "xlm":
                   from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                       convert_xlm_checkpoint_to_pytorch,
                   )

                   convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "lxmert":
                   from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                       convert_lxmert_checkpoint_to_pytorch,
                   )

                   convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "rembert":
                   from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                       convert_rembert_tf_checkpoint_to_pytorch,
                   )

                   convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            else:
                   raise ValueError(
                       '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )

