


'''simple docstring'''






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 	_A				(lowerCAmelCase__  :Namespace		)   ->  Optional[int]:




     '''simple docstring'''



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


a_					:    Dict          =    "\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     (     _SCREAMING_SNAKE_CASE       ):
 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__						)					->							Optional[Any]:
      _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=__magic_name__      ,				required=__magic_name__      ,				help='Model\'s type.'						)
      train_parser.add_argument(
          '--tf_checkpoint'      ,				type=__magic_name__      ,				required=__magic_name__      ,				help='TensorFlow checkpoint path or folder.'						)
      train_parser.add_argument(
          '--pytorch_dump_output'      ,				type=__magic_name__      ,				required=__magic_name__      ,				help='Path to the PyTorch saved model output.'						)
      train_parser.add_argument('--config'      ,				type=__magic_name__      ,				default=''      ,				help='Configuration file path or folder.'						)
      train_parser.add_argument(
          '--finetuning_task_name'      ,				type=__magic_name__      ,				default=__magic_name__      ,				help='Optional fine-tuning task name if the TF model was a finetuned model.'      ,				)
      train_parser.set_defaults(func=__magic_name__						)
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				*__magic_name__      ,				)					->							Dict:
      _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 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      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(__magic_name__						)

           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(__magic_name__						)

           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(__magic_name__						)

           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(__magic_name__						)

           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(__magic_name__						)

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

           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(__magic_name__						)

           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'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _lowerCAmelCase 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

