

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			int      		=       LEDTokenizer
      _lowerCamelCase       :			str      		=       LEDTokenizerFast
      _lowerCamelCase       :			List[Any]      		=       True





      def 		__A	(   self							:				Optional[int]	):
            super().setUp()
            A_						  =				[
                "l",
                "o",
                "w",
                "e",
                "r",
                "s",
                "t",
                "i",
                "d",
                "n",
                "\u0120",
                "\u0120l",
                "\u0120n",
                "\u0120lo",
                "\u0120low",
                "er",
                "\u0120lowest",
                "\u0120newer",
                "\u0120wider",
                "<unk>",
            ]
            A_						  =				dict(zip(UpperCAmelCase						,			range(len(UpperCAmelCase	)	)	)	)
            A_						  =				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
            A_						  =				{"unk_token": "<unk>"}

            A_						  =				os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["vocab_file"]	)
            A_						  =				os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["merges_file"]	)
            with open(self.vocab_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write(json.dumps(UpperCAmelCase	) + "\n"	)
            with open(self.merges_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write("\n".join(UpperCAmelCase	)	)





      def 		__A	(   self							:				Optional[Any]						,			**UpperCAmelCase							:				Optional[int]	):
            kwargs.update(self.special_tokens_map	)
            return self.tokenizer_class.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Tuple						,			**UpperCAmelCase							:				int	):
            kwargs.update(self.special_tokens_map	)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				Dict	):
            return "lower newer", "lower newer"





      @cached_property
      def 		__A	(   self							:				Any	):
            return LEDTokenizer.from_pretrained("allenai/led-base-16384"	)





      @cached_property
      def 		__A	(   self							:				Union[str, Any]	):
            return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"	)





      @require_torch
      def 		__A	(   self							:				str	):
            A_						  =				["A long paragraph for summarization.", "Another paragraph for summarization."]
            A_						  =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  A_						  =				tokenizer(UpperCAmelCase						,			max_length=len(UpperCAmelCase	)						,			padding=UpperCAmelCase						,			return_tensors="pt"	)
                  self.assertIsInstance(UpperCAmelCase						,			UpperCAmelCase	)

                  self.assertEqual((2, 9)						,			batch.input_ids.shape	)
                  self.assertEqual((2, 9)						,			batch.attention_mask.shape	)
                  A_						  =				batch.input_ids.tolist()[0]
                  self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)





      @require_torch
      def 		__A	(   self							:				Optional[int]	):
            A_						  =				["A long paragraph for summarization.", "Another paragraph for summarization."]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  A_						  =				tokenizer(UpperCAmelCase						,			padding=UpperCAmelCase						,			return_tensors="pt"	)
                  self.assertIn("input_ids"						,			UpperCAmelCase	)
                  self.assertIn("attention_mask"						,			UpperCAmelCase	)
                  self.assertNotIn("labels"						,			UpperCAmelCase	)
                  self.assertNotIn("decoder_attention_mask"						,			UpperCAmelCase	)





      @require_torch
      def 		__A	(   self							:				Tuple	):
            A_						  =				[
                "Summary of the text.",
                "Another summary.",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  A_						  =				tokenizer(text_target=UpperCAmelCase						,			max_length=32						,			padding="max_length"						,			return_tensors="pt"	)
                  self.assertEqual(32						,			targets["input_ids"].shape[1]	)





      @require_torch
      def 		__A	(   self							:				List[Any]	):
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  A_						  =				tokenizer(
                      ["I am a small frog" * 1024, "I am a small frog"]						,			padding=UpperCAmelCase						,			truncation=UpperCAmelCase						,			return_tensors="pt"	)
                  self.assertIsInstance(UpperCAmelCase						,			UpperCAmelCase	)
                  self.assertEqual(batch.input_ids.shape						,			(2, 5122)	)





      @require_torch
      def 		__A	(   self							:				Any	):
            A_						  =				["A long paragraph for summarization."]
            A_						  =				[
                "Summary of the text.",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  A_						  =				tokenizer(UpperCAmelCase						,			return_tensors="pt"	)
                  A_						  =				tokenizer(text_target=UpperCAmelCase						,			return_tensors="pt"	)
                  A_						  =				inputs["input_ids"]
                  A_						  =				targets["input_ids"]
                  self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
                  self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
                  self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
                  self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)





      @require_torch
      def 		__A	(   self							:				List[str]	):
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  A_						  =				["Summary of the text.", "Another summary."]
                  A_						  =				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                  A_						  =				tokenizer(UpperCAmelCase						,			padding=UpperCAmelCase	)
                  A_						  =				[[0] * len(UpperCAmelCase	) for x in encoded_output["input_ids"]]
                  A_						  =				tokenizer.pad(UpperCAmelCase	)
                  self.assertSequenceEqual(outputs["global_attention_mask"]						,			UpperCAmelCase	)





      def 		__A	(   self							:				str	):
            pass







      def 		__A	(   self							:				Tuple	):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				self.tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				"A, <mask> AllenNLP sentence."
                        A_						  =				tokenizer_r.encode_plus(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			return_token_type_ids=UpperCAmelCase	)
                        A_						  =				tokenizer_p.encode_plus(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			return_token_type_ids=UpperCAmelCase	)
                        self.assertEqual(sum(tokens_r["token_type_ids"]	)						,			sum(tokens_p["token_type_ids"]	)	)
                        self.assertEqual(
                            sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						,			sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						,			)

                        A_						  =				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
                        A_						  =				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)
                        self.assertSequenceEqual(tokens_p["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                        self.assertSequenceEqual(tokens_r["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

                        self.assertSequenceEqual(
                            UpperCAmelCase						,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
                        self.assertSequenceEqual(
                            UpperCAmelCase						,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)







'''simple docstring'''







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,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

__lowerCAmelCase									=   {'facebook/bart-base': BartForConditionalGeneration}
__lowerCAmelCase									=   {'facebook/bart-base': BartTokenizer}





def 		_UpperCAmelCase (				):
				a_		:	int									=   argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''   )
				parser.add_argument(
				    '''--validation_file'''    ,		type=__A    ,		default=__A    ,		help='''A csv or a json file containing the validation data.'''   )
				parser.add_argument(
				    '''--max_length'''    ,		type=__A    ,		default=5    ,		help='''The maximum total input sequence length after tokenization.'''    ,		)
				parser.add_argument(
				    '''--num_beams'''    ,		type=__A    ,		default=__A    ,		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=__A    ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,		required=__A    ,		)
				parser.add_argument(
				    '''--config_name'''    ,		type=__A    ,		default=__A    ,		help='''Pretrained config name or path if not the same as model_name'''    ,		)
				parser.add_argument(
				    '''--device'''    ,		type=__A    ,		default='''cpu'''    ,		help='''Device where the model will be run'''    ,		)
				parser.add_argument('''--output_file_path'''    ,		type=__A    ,		default=__A    ,		help='''Where to store the final ONNX file.'''   )

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

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

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

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

				with torch.no_grad():
								a_		:	Any									=   '''My friends are cool but they eat too many carbs.'''
								a_		:	Dict									=   tokenizer([ARTICLE_TO_SUMMARIZE]    ,		max_length=10_24    ,		return_tensors='''pt'''   ).to(model.device   )

								a_		:	Dict									=   model.generate(
								    inputs['''input_ids''']    ,		attention_mask=inputs['''attention_mask''']    ,		num_beams=__A    ,		max_length=__A    ,		early_stopping=__A    ,		decoder_start_token_id=model.config.decoder_start_token_id    ,		)

								torch.onnx.export(
								    __A    ,		(
								        inputs['''input_ids'''],
								        inputs['''attention_mask'''],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    )    ,		__A    ,		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=__A    ,		)

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

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   ort_sess.run(
								    __A    ,		{
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(__A   ),
								        '''max_length''': np.array(__A   ),
								        '''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 (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[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()

				a_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   load_model_tokenizer(args.model_name_or_path    ,		__A   )

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

				model.to(__A   )

				if args.max_length:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

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


if __name__ == "__main__":
		main()




