"""simple docstring"""







from math import isqrt, loga
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      list[int]:

	'''simple docstring'''





	__snake_case      :							Any						=			[True] * max_number
	for i in range(2      ,							isqrt(max_number - 1  ) + 1  ):
		if is_prime[i]:
			for j in range(i**2      ,							UpperCAmelCase_      ,							UpperCAmelCase_  ):
				__snake_case      :							Any						=			False

	return [i for i in range(2      ,							UpperCAmelCase_  ) if is_prime[i]]
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int = 80_08_00      ,							UpperCAmelCase_   :    int = 80_08_00  )							->      int:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			degree * loga(UpperCAmelCase_  )
	__snake_case      :							str						=			int(UpperCAmelCase_  )
	__snake_case      :							Optional[int]						=			calculate_prime_numbers(UpperCAmelCase_  )

	__snake_case      :							str						=			0
	__snake_case      :							Union[str, Any]						=			0
	__snake_case      :							Dict						=			len(UpperCAmelCase_  ) - 1
	while left < right:
		while (
		    prime_numbers[right] * loga(prime_numbers[left]  )
		    + prime_numbers[left] * loga(prime_numbers[right]  )
		    > upper_bound
		):
			right -= 1
		hybrid_integers_count += right - left
		left += 1

	return hybrid_integers_count


if __name__ == "__main__":
	print(f'''{solution() = }''')






"""simple docstring"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Namespace  )							->      Union[str, Any]:

	'''simple docstring'''





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


_a					:					str=			"\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 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			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=_A     ,  required=_A     ,  help='Model\'s type.')
					train_parser.add_argument(
					    '--tf_checkpoint'     ,  type=_A     ,  required=_A     ,  help='TensorFlow checkpoint path or folder.')
					train_parser.add_argument(
					    '--pytorch_dump_output'     ,  type=_A     ,  required=_A     ,  help='Path to the PyTorch saved model output.')
					train_parser.add_argument('--config'     ,  type=_A     ,  default=''     ,  help='Configuration file path or folder.')
					train_parser.add_argument(
					    '--finetuning_task_name'     ,  type=_A     ,  default=_A     ,  help='Optional fine-tuning task name if the TF model was a finetuned model.'     ,  )
					train_parser.set_defaults(func=_A)



				def __init__(self    :				List[str]     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  *_A    :				Any     ,  )   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			logging.get_logger('transformers-cli/converting')

					self._logger.info(f"Loading model {model_type}")
					__snake_case      :							List[str]						=			model_type
					__snake_case      :							int						=			tf_checkpoint
					__snake_case      :							Optional[int]						=			pytorch_dump_output
					__snake_case      :							Optional[Any]						=			config
					__snake_case      :							Optional[Any]						=			finetuning_task_name






				def  _lowercase					(self    :				List[str])   ->     str:
					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(_A)

						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(_A)

						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(_A)

						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(_A)

						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(_A)

						if "ckpt" in self._tf_checkpoint.lower():
							__snake_case      :							Union[str, Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						else:
							__snake_case      :							Optional[Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _A     ,  self._config     ,  self._pytorch_dump_output     ,  _A)
					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(_A)

						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(_A)

						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]')





