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

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[Any]			=    "philschmid/bart-large-cnn-samsum"
					UpperCamelCase_ :	List[Any]			=    (
					    "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."
					)
					UpperCamelCase_ :	int			=    "summarizer"
					UpperCamelCase_ :	Any			=    AutoTokenizer
					UpperCamelCase_ :	Optional[Any]			=    AutoModelForSeqaSeqLM

					UpperCamelCase_ :	Optional[Any]			=    ["text"]
					UpperCamelCase_ :	Dict			=    ["text"]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     str     ):
							return self.pre_processor(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Optional[Any]     ):
							return self.model.generate(**snake_case__     )[0]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Any     ):
							return self.pre_processor.decode(snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )





"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT 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."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




