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#
# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class  UpperCamelCase_      (			lowerCAmelCase_				):



	'''simple docstring'''




	lowerCAmelCase													= '''philschmid/bart-large-cnn-samsum'''
	lowerCAmelCase													= (
	    '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
	    '''and returns a summary of the text.'''
	)
	lowerCAmelCase													= '''summarizer'''
	lowerCAmelCase													= AutoTokenizer
	lowerCAmelCase													= AutoModelForSeqaSeqLM

	lowerCAmelCase													= ['''text''']
	lowerCAmelCase													= ['''text''']




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Any:
								return self.pre_processor(_snake_case       ,			return_tensors='pt'       ,			truncation=_snake_case					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Any:
								return self.model.generate(**_snake_case					)[0]






	def 							_UpperCamelCase			(       self       ,			a					)       ->    str:
								return self.pre_processor.decode(_snake_case       ,			skip_special_tokens=_snake_case       ,			clean_up_tokenization_spaces=_snake_case					)










import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(A		,					A		,					A      )

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''      )
       torch.save(model.state_dict()		,					A      )


if __name__ == "__main__":
 a_   :Union[str, Any]        =      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(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






