




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors

@require_tokenizers
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    BartTokenizer
      lowerCAmelCase__	:       List[Any]			=    BartTokenizerFast
      lowerCAmelCase__	:       str			=    True
      lowerCAmelCase__	:       List[str]			=    filter_roberta_detectors







      def 					snake_case		(   self    :       Optional[int] ):
          super().setUp()
          __UpperCamelCase     						=  [
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          __UpperCamelCase     						=  dict(zip(snake_case       ,				range(len(snake_case ) ) ) )
          __UpperCamelCase     						=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          __UpperCamelCase     						=  {'''unk_token''': '''<unk>'''}

          __UpperCamelCase     						=  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['''vocab_file'''] )
          __UpperCamelCase     						=  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(snake_case ) + '''\n''' )
          with open(self.merges_file       ,				'''w'''       ,				encoding='''utf-8''' ) as fp:
              fp.write('''\n'''.join(snake_case ) )







      def 					snake_case		(   self    :       Tuple       ,				**snake_case    :       Optional[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       str       ,				**snake_case    :       Optional[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,				**snake_case )







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







      @cached_property
      def 					snake_case		(   self    :       Optional[Any] ):
          return BartTokenizer.from_pretrained('''facebook/bart-large''' )







      @cached_property
      def 					snake_case		(   self    :       Tuple ):
          return BartTokenizerFast.from_pretrained('''facebook/bart-large''' )







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

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				max_length=len(snake_case )       ,				padding=snake_case       ,				return_tensors='''pt''' )
              self.assertIsInstance(snake_case       ,				snake_case )

              self.assertEqual((2, 9)       ,				batch.input_ids.shape )
              self.assertEqual((2, 9)       ,				batch.attention_mask.shape )
              __UpperCamelCase     						=  batch.input_ids.tolist()[0]
              self.assertListEqual(snake_case       ,				snake_case )
              # Test that special tokens are reset







      @require_torch
      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				padding=snake_case       ,				return_tensors='''pt''' )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''       ,				snake_case )
              self.assertIn('''attention_mask'''       ,				snake_case )
              self.assertNotIn('''labels'''       ,				snake_case )
              self.assertNotIn('''decoder_attention_mask'''       ,				snake_case )







      @require_torch
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  [
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(text_target=snake_case       ,				max_length=32       ,				padding='''max_length'''       ,				return_tensors='''pt''' )
              self.assertEqual(32       ,				targets['''input_ids'''].shape[1] )







      @require_torch
      def 					snake_case		(   self    :       Union[str, Any] ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']       ,				padding=snake_case       ,				truncation=snake_case       ,				return_tensors='''pt''' )
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertEqual(batch.input_ids.shape       ,				(2, 1024) )







      @require_torch
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''']
          __UpperCamelCase     						=  [
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				return_tensors='''pt''' )
              __UpperCamelCase     						=  tokenizer(text_target=snake_case       ,				return_tensors='''pt''' )
              __UpperCamelCase     						=  inputs['''input_ids''']
              __UpperCamelCase     						=  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() )







      def 					snake_case		(   self    :       str ):
          pass







      def 					snake_case		(   self    :       Dict ):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                  __UpperCamelCase     						=  self.rust_tokenizer_class.from_pretrained(snake_case       ,				**snake_case )
                  __UpperCamelCase     						=  self.tokenizer_class.from_pretrained(snake_case       ,				**snake_case )
                  __UpperCamelCase     						=  '''A, <mask> AllenNLP sentence.'''
                  __UpperCamelCase     						=  tokenizer_r.encode_plus(snake_case       ,				add_special_tokens=snake_case       ,				return_token_type_ids=snake_case )
                  __UpperCamelCase     						=  tokenizer_p.encode_plus(snake_case       ,				add_special_tokens=snake_case       ,				return_token_type_ids=snake_case )

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['''token_type_ids'''] )       ,				sum(tokens_p['''token_type_ids'''] ) )

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )       ,				sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )       ,				)

                  __UpperCamelCase     						=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  __UpperCamelCase     						=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )

                  # Rust correctly handles the space before the mask while python doesnt
                  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(
                      snake_case       ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                  self.assertSequenceEqual(
                      snake_case       ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )













'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
			    )	)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""					,	type=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

			# positional
			parser.add_argument(
			    """training_script"""					,	type=lowercase					,	help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )					,	)

			# rest from the training program
			parser.add_argument("""training_script_args"""					,	nargs=lowercase	)

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

			# Import training_script as a module.
			__lowerCAmelCase											=				Path(args.training_script	)
			sys.path.append(str(script_fpath.parent.resolve()	)	)
			__lowerCAmelCase											=				script_fpath.stem
			__lowerCAmelCase											=				importlib.import_module(lowercase	)

			# Patch sys.argv
			__lowerCAmelCase											=				[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

			xmp.spawn(mod._mp_fn					,	args=()					,	nprocs=args.num_cores	)


if __name__ == "__main__":
	main()

