
import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="""%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s""",
    datefmt="""%Y-%m-%d %H:%M:%S""",
    level=os.environ.get("""LOGLEVEL""", """INFO""").upper(),
    stream=sys.stdout,
)

a											=							logging.getLogger(__name__)

a											=							{"""facebook/bart-base""": BartForConditionalGeneration}
a											=							{"""facebook/bart-base""": BartTokenizer}







def   UpperCamelCase_(					):


							"""simple docstring"""





							_lowerCAmelCase :List[str]      	=						argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'      )
							parser.add_argument(
							    '--validation_file'	,    type=__lowerCamelCase	,    default=__lowerCamelCase	,    help='A csv or a json file containing the validation data.'      )
							parser.add_argument(
							    '--max_length'	,    type=__lowerCamelCase	,    default=5	,    help='The maximum total input sequence length after tokenization.'	,    )
							parser.add_argument(
							    '--num_beams'	,    type=__lowerCamelCase	,    default=__lowerCamelCase	,    help=(
							        'Number of beams to use for evaluation. This argument will be '
							        'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
							    )	,    )
							parser.add_argument(
							    '--model_name_or_path'	,    type=__lowerCamelCase	,    help='Path to pretrained model or model identifier from huggingface.co/models.'	,    required=__lowerCamelCase	,    )
							parser.add_argument(
							    '--config_name'	,    type=__lowerCamelCase	,    default=__lowerCamelCase	,    help='Pretrained config name or path if not the same as model_name'	,    )
							parser.add_argument(
							    '--device'	,    type=__lowerCamelCase	,    default='cpu'	,    help='Device where the model will be run'	,    )
							parser.add_argument('--output_file_path'	,    type=__lowerCamelCase	,    default=__lowerCamelCase	,    help='Where to store the final ONNX file.'      )

							_lowerCAmelCase :Tuple      	=						parser.parse_args()

							return args







def   UpperCamelCase_(					__magic_name__      :	Any	,    __magic_name__      :	List[Any]="cpu"      ):


							"""simple docstring"""





							_lowerCAmelCase :int      	=						model_dict[model_name].from_pretrained(__lowerCamelCase      ).to(__lowerCamelCase      )
							_lowerCAmelCase :Tuple      	=						tokenizer_dict[model_name].from_pretrained(__lowerCamelCase      )

							if model_name in ["facebook/bart-base"]:
														_lowerCAmelCase :Optional[int]      	=						0
														_lowerCAmelCase :Any      	=						None
														_lowerCAmelCase :Any      	=						0

							return huggingface_model, tokenizer







def   UpperCamelCase_(					__magic_name__      :	List[Any]	,    __magic_name__      :	Optional[Any]	,    __magic_name__      :	List[Any]	,    __magic_name__      :	Dict	,    __magic_name__      :	str      ):


							"""simple docstring"""





							model.eval()

							_lowerCAmelCase :str      	=						None
							_lowerCAmelCase :Union[str, Any]      	=						torch.jit.script(BARTBeamSearchGenerator(__lowerCamelCase      )      )

							with torch.no_grad():
														_lowerCAmelCase :List[str]      	=						'My friends are cool but they eat too many carbs.'
														_lowerCAmelCase :Union[str, Any]      	=						tokenizer([ARTICLE_TO_SUMMARIZE]	,    max_length=1024	,    return_tensors='pt'      ).to(model.device      )

														_lowerCAmelCase :List[str]      	=						model.generate(
														    inputs['input_ids']	,    attention_mask=inputs['attention_mask']	,    num_beams=__lowerCamelCase	,    max_length=__lowerCamelCase	,    early_stopping=__lowerCamelCase	,    decoder_start_token_id=model.config.decoder_start_token_id	,    )

														torch.onnx.export(
														    __lowerCamelCase	,    (
														        inputs['input_ids'],
														        inputs['attention_mask'],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )	,    __lowerCamelCase	,    opset_version=14	,    input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']	,    output_names=['output_ids']	,    dynamic_axes={
														        'input_ids': {0: 'batch', 1: 'seq'},
														        'output_ids': {0: 'batch', 1: 'seq_out'},
														    }	,    example_outputs=__lowerCamelCase	,    )

														logger.info('Model exported to {}'.format(__lowerCamelCase      )      )

														_lowerCAmelCase :Dict      	=						remove_dup_initializers(os.path.abspath(__lowerCamelCase      )      )

														logger.info('Deduplicated and optimized model written to {}'.format(__lowerCamelCase      )      )

														_lowerCAmelCase :Union[str, Any]      	=						onnxruntime.InferenceSession(__lowerCamelCase      )
														_lowerCAmelCase :int      	=						ort_sess.run(
														    __lowerCamelCase	,    {
														        'input_ids': inputs['input_ids'].cpu().numpy(),
														        'attention_mask': inputs['attention_mask'].cpu().numpy(),
														        'num_beams': np.array(__lowerCamelCase      ),
														        'max_length': np.array(__lowerCamelCase      ),
														        'decoder_start_token_id': np.array(model.config.decoder_start_token_id      ),
														    }	,    )

														np.testing.assert_allclose(summary_ids.cpu().numpy()	,    ort_out[0]	,    rtol=1e-3	,    atol=1e-3      )

														logger.info('Model outputs from torch and ONNX Runtime are similar.'      )
														logger.info('Success.'      )







def   UpperCamelCase_(					):


							"""simple docstring"""





							_lowerCAmelCase :Dict      	=						parse_args()
							_lowerCAmelCase :Optional[Any]      	=						5
							_lowerCAmelCase :str      	=						4

							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,    datefmt='%m/%d/%Y %H:%M:%S'	,    level=logging.INFO	,    )

							logger.setLevel(logging.INFO      )
							transformers.utils.logging.set_verbosity_error()

							_lowerCAmelCase :Optional[Any]      	=						torch.device(args.device      )

							_lowerCAmelCase	,  _lowerCAmelCase :Tuple      	=						load_model_tokenizer(args.model_name_or_path	,    __lowerCamelCase      )

							if model.config.decoder_start_token_id is None:
														raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'      )

							model.to(__lowerCamelCase      )

							if args.max_length:
														_lowerCAmelCase :Optional[int]      	=						args.max_length

							if args.num_beams:
														_lowerCAmelCase :Dict      	=						args.num_beams

							if args.output_file_path:
														_lowerCAmelCase :int      	=						args.output_file_path
							else:
														_lowerCAmelCase :List[Any]      	=						'BART.onnx'

							logger.info('Exporting model to ONNX'      )
							export_and_validate_model(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase      )


if __name__ == "__main__":
						main()




from __future__ import annotations






class     __a			:



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int = 0)->				List[str]:
						__lowerCAmelCase						 =key



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""encrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.encrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""decrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.decrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




