'''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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image







class 				_lowercase     (				__UpperCAmelCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					'''Salesforce/blip-image-captioning-base'''
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					(
     '''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
     '''image to caption, and returns a text that contains the description in English.'''
 )
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					'''image_captioner'''
 _SCREAMING_SNAKE_CASE     :       str      	=					AutoModelForVisionaSeq

 _SCREAMING_SNAKE_CASE     :       str      	=					['''image''']
 _SCREAMING_SNAKE_CASE     :       str      	=					['''text''']
 def __init__(      self   :	Any      ,       *SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    List[str]:
  requires_backends(self      ,       ["""vision"""]							)
  super().__init__(*__SCREAMING_SNAKE_CASE      ,       **__SCREAMING_SNAKE_CASE							)
 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	"Image"							)  ->    Optional[Any]:
  return self.pre_processor(images=__SCREAMING_SNAKE_CASE      ,       return_tensors="""pt"""							)
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    List[str]:
  return self.model.generate(**__SCREAMING_SNAKE_CASE							)





 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    Any:
  return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE      ,       skip_special_tokens=__SCREAMING_SNAKE_CASE							)[0].strip()











import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				BigBirdConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)

    if is_trivia_qa:
        __a						       =				BigBirdForQuestionAnswering(_UpperCAmelCase				)
    else:
        __a						       =				BigBirdForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_big_bird(_UpperCAmelCase   , _UpperCAmelCase   , is_trivia_qa=_UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Tuple      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--big_bird_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained BERT model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
 )
 __snake_case						:Any      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
 )





