




'''simple docstring'''


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 		snake_case_   (UpperCamelCase	:  Namespace				):





							'''simple docstring'''


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


_snake_case				:				List[Any]       							=		'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'

class       A   (		_a   ):







							@staticmethod
							def        __lowerCAmelCase      (  lowerCAmelCase_	:  ArgumentParser )      ->							Any:


														"""simple docstring"""


														_a     					=    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=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''Model\'s type.''' )
														train_parser.add_argument(
														    '''--tf_checkpoint'''							,      type=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''TensorFlow checkpoint path or folder.''' )
														train_parser.add_argument(
														    '''--pytorch_dump_output'''							,      type=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''Path to the PyTorch saved model output.''' )
														train_parser.add_argument('''--config'''							,      type=lowerCAmelCase_							,      default=''''''							,      help='''Configuration file path or folder.''' )
														train_parser.add_argument(
														    '''--finetuning_task_name'''							,      type=lowerCAmelCase_							,      default=lowerCAmelCase_							,      help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,      )
														train_parser.set_defaults(func=lowerCAmelCase_ )







							def __init__(  self	:  int							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  Optional[Any]							,      )      ->							List[str]:


														"""simple docstring"""


														_a     					=    logging.get_logger('''transformers-cli/converting''' )

														self._logger.info(F'Loading model {model_type}' )
														_a     					=    model_type
														_a     					=    tf_checkpoint
														_a     					=    pytorch_dump_output
														_a     					=    config
														_a     					=    finetuning_task_name







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Dict:


														"""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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					if "ckpt" in self._tf_checkpoint.lower():
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					else:
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					convert_transfo_xl_checkpoint_to_pytorch(
																					    lowerCAmelCase_							,      self._config							,      self._pytorch_dump_output							,      lowerCAmelCase_ )
														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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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]''' )







'''simple docstring'''


def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  str=0				):





							'''simple docstring'''


							return sorted(UpperCamelCase			,						key=lambda UpperCamelCase				: x[column]				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Any			,						UpperCamelCase	:  Union[str, Any]=float('''inf'''				)				):





							'''simple docstring'''


							for i in range(points_counts - 1				):
														for j in range(i + 1			,						UpperCamelCase				):
																					_a     					=    euclidean_distance_sqr(points[i]			,						points[j]				)
																					if current_dis < min_dis:
																												_a     					=    current_dis
							return min_dis
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  List[str]=float('''inf'''				)				):





							'''simple docstring'''


							for i in range(min(6			,						points_counts - 1				)			,						UpperCamelCase				):
														for j in range(max(0			,						i - 6				)			,						UpperCamelCase				):
																					_a     					=    euclidean_distance_sqr(points[i]			,						points[j]				)
																					if current_dis < min_dis:
																												_a     					=    current_dis
							return min_dis
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							if points_counts <= 3:
														return dis_between_closest_pair(UpperCamelCase			,						UpperCamelCase				)

							# recursion
							_a     					=    points_counts // 2
							_a     					=    closest_pair_of_points_sqr(
							    UpperCamelCase			,						points_sorted_on_y[:mid]			,						UpperCamelCase				)
							_a     					=    closest_pair_of_points_sqr(
							    UpperCamelCase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
							_a     					=    min(UpperCamelCase			,						UpperCamelCase				)
							_a     					=    []
							for point in points_sorted_on_x:
														if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
																					cross_strip.append(UpperCamelCase				)

							_a     					=    dis_between_closest_in_strip(
							    UpperCamelCase			,						len(UpperCamelCase				)			,						UpperCamelCase				)
							return min(UpperCamelCase			,						UpperCamelCase				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  List[str]				):





							'''simple docstring'''


							_a     					=    column_based_sort(UpperCamelCase			,						column=0				)
							_a     					=    column_based_sort(UpperCamelCase			,						column=1				)
							return (
							    closest_pair_of_points_sqr(
							        UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)
							) ** 0.5


if __name__ == "__main__":
				_snake_case				:				int       							=		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
				print('Distance:', closest_pair_of_points(points, len(points)))

