'''simple docstring'''

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def __UpperCamelCase(   _A					:				Namespace    ):
   '''simple docstring'''
   return ConvertCommand(
       args.model_type  ,			args.tf_checkpoint  ,			args.pytorch_dump_output  ,			args.config  ,			args.finetuning_task_name    )


UpperCamelCase__     :	Union[str, 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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      @staticmethod
      def 		lowerCAmelCase__					(				lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				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=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					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,*lowerCamelCase_					,)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  logging.get_logger('''transformers-cli/converting'''							)

         self._logger.info(f'''Loading model {model_type}'''							)
         UpperCAmelCase__							:				List[Any]   				=  model_type
         UpperCAmelCase__							:				List[Any]   				=  tf_checkpoint
         UpperCAmelCase__							:				Tuple   				=  pytorch_dump_output
         UpperCAmelCase__							:				Union[str, Any]   				=  config
         UpperCAmelCase__							:				List[Any]   				=  finetuning_task_name




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''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():
               UpperCAmelCase__							:				Optional[Any]   				=  self._tf_checkpoint
               UpperCAmelCase__							:				Optional[int]   				=  ''''''
            else:
               UpperCAmelCase__							:				List[str]   				=  self._tf_checkpoint
               UpperCAmelCase__							:				List[str]   				=  ''''''
            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'''

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase__     :	Tuple				=			TypeVar('T')


class 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

         self.dq_store.appendleft(lowerCamelCase_							)
         self.key_reference.add(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store							)}'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCamelCase__     :	LRUCache[str | int]				=			LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




