

'''simple docstring'''




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase         =						get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
      from transformers.models.mbart.modeling_mbart import shift_tokens_right

_lowerCAmelCase         =						250004
_lowerCAmelCase         =						250020






@require_sentencepiece
@require_tokenizers
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MBartTokenizer
 __SCREAMING_SNAKE_CASE					:Tuple      	=					MBartTokenizerFast
 __SCREAMING_SNAKE_CASE					:Dict      	=					True
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					True



 def 			snake_case__  (   self		:   int      ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      __magic_name__						       =    MBartTokenizer(a__			,				keep_accents=a__      )
      tokenizer.save_pretrained(self.tmpdirname      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    MBartTokenizer(a__			,				keep_accents=a__      )

      __magic_name__						       =    tokenizer.tokenize('''This is a test'''      )
      self.assertListEqual(a__			,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(a__      )			,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,				)

      __magic_name__						       =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
      self.assertListEqual(
          a__			,				[
              SPIECE_UNDERLINE + '''I''',
              SPIECE_UNDERLINE + '''was''',
              SPIECE_UNDERLINE + '''b''',
              '''or''',
              '''n''',
              SPIECE_UNDERLINE + '''in''',
              SPIECE_UNDERLINE + '''''',
              '''9''',
              '''2''',
              '''0''',
              '''0''',
              '''0''',
              ''',''',
              SPIECE_UNDERLINE + '''and''',
              SPIECE_UNDERLINE + '''this''',
              SPIECE_UNDERLINE + '''is''',
              SPIECE_UNDERLINE + '''f''',
              '''al''',
              '''s''',
              '''é''',
              '''.''',
          ]			,				)
      __magic_name__						       =    tokenizer.convert_tokens_to_ids(a__      )
      self.assertListEqual(
          a__			,				[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]			,				)

      __magic_name__						       =    tokenizer.convert_ids_to_tokens(a__      )
      self.assertListEqual(
          a__			,				[
              SPIECE_UNDERLINE + '''I''',
              SPIECE_UNDERLINE + '''was''',
              SPIECE_UNDERLINE + '''b''',
              '''or''',
              '''n''',
              SPIECE_UNDERLINE + '''in''',
              SPIECE_UNDERLINE + '''''',
              '''<unk>''',
              '''2''',
              '''0''',
              '''0''',
              '''0''',
              ''',''',
              SPIECE_UNDERLINE + '''and''',
              SPIECE_UNDERLINE + '''this''',
              SPIECE_UNDERLINE + '''is''',
              SPIECE_UNDERLINE + '''f''',
              '''al''',
              '''s''',
              '''<unk>''',
              '''.''',
          ]			,				)



 def 			snake_case__  (   self		:   Any      ):
      if not self.test_slow_tokenizer:
           # as we don't have a slow version, we can't compare the outputs between slow and fast versions
           return

      __magic_name__						       =    (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                __magic_name__						       =    self.rust_tokenizer_class.from_pretrained(a__			,				**a__      )
                __magic_name__						       =    self.tokenizer_class.from_pretrained(a__			,				**a__      )

                __magic_name__						       =    tempfile.mkdtemp()

                __magic_name__						       =    tokenizer_r.save_pretrained(a__      )
                __magic_name__						       =    tokenizer_p.save_pretrained(a__      )

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )
                __magic_name__						       =    tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
                self.assertSequenceEqual(a__			,				a__      )

                # Checks everything loads correctly in the same way
                __magic_name__						       =    tokenizer_r.from_pretrained(a__      )
                __magic_name__						       =    tokenizer_p.from_pretrained(a__      )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(a__			,				a__      )      )
                     # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                     # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                shutil.rmtree(a__      )

                # Save tokenizer rust, legacy_format=True
                __magic_name__						       =    tempfile.mkdtemp()

                __magic_name__						       =    tokenizer_r.save_pretrained(a__			,				legacy_format=a__      )
                __magic_name__						       =    tokenizer_p.save_pretrained(a__      )

                # Checks it save with the same files
                self.assertSequenceEqual(a__			,				a__      )

                # Checks everything loads correctly in the same way
                __magic_name__						       =    tokenizer_r.from_pretrained(a__      )
                __magic_name__						       =    tokenizer_p.from_pretrained(a__      )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(a__			,				a__      )      )

                shutil.rmtree(a__      )

                # Save tokenizer rust, legacy_format=False
                __magic_name__						       =    tempfile.mkdtemp()

                __magic_name__						       =    tokenizer_r.save_pretrained(a__			,				legacy_format=a__      )
                __magic_name__						       =    tokenizer_p.save_pretrained(a__      )

                # Checks it saved the tokenizer.json file
                self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )

                # Checks everything loads correctly in the same way
                __magic_name__						       =    tokenizer_r.from_pretrained(a__      )
                __magic_name__						       =    tokenizer_p.from_pretrained(a__      )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(a__			,				a__      )      )

                shutil.rmtree(a__      )






@require_torch
@require_sentencepiece
@require_tokenizers
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:int      	=					"""facebook/mbart-large-en-ro"""
 __SCREAMING_SNAKE_CASE					:int      	=					[
     """ UN Chief Says There Is No Military Solution in Syria""",
     """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
 ]
 __SCREAMING_SNAKE_CASE					:Dict      	=					[
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
     """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
     """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
     """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
 ]
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]



 @classmethod
 def 			snake_case__  (   cls		:   Tuple      ):
      __magic_name__						       =    MBartTokenizer.from_pretrained(
          cls.checkpoint_name			,				src_lang='''en_XX'''			,				tgt_lang='''ro_RO'''      )
      __magic_name__						       =    1
      return cls



 def 			snake_case__  (   self		:   int      ):
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']			,				25_0001      )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']			,				25_0004      )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']			,				25_0020      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens			,				a__      )



 def 			snake_case__  (   self		:   Dict      ):
      self.assertIn(a__			,				self.tokenizer.all_special_ids      )
      __magic_name__						       =    [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
      __magic_name__						       =    self.tokenizer.decode(a__			,				skip_special_tokens=a__      )
      __magic_name__						       =    self.tokenizer.decode(generated_ids[1:]			,				skip_special_tokens=a__      )
      self.assertEqual(a__			,				a__      )
      self.assertNotIn(self.tokenizer.eos_token			,				a__      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    ['''this is gunna be a long sentence ''' * 20]
      assert isinstance(src_text[0]			,				a__      )
      __magic_name__						       =    10
      __magic_name__						       =    self.tokenizer(a__			,				max_length=a__			,				truncation=a__      ).input_ids[0]
      self.assertEqual(ids[-2]			,				2      )
      self.assertEqual(ids[-1]			,				a__      )
      self.assertEqual(len(a__      )			,				a__      )



 def 			snake_case__  (   self		:   Dict      ):
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      )			,				[25_0026, 25_0001]      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    tempfile.mkdtemp()
      __magic_name__						       =    self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(a__      )
      __magic_name__						       =    MBartTokenizer.from_pretrained(a__      )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,				a__      )



 @require_torch
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.tokenizer(self.src_text			,				text_target=self.tgt_text			,				padding=a__			,				return_tensors='''pt'''      )
      __magic_name__						       =    shift_tokens_right(batch['''labels''']			,				self.tokenizer.pad_token_id      )

      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
      assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
      assert batch.decoder_input_ids[1][-1] == 2
      assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.tokenizer(
          self.src_text			,				text_target=self.tgt_text			,				padding=a__			,				truncation=a__			,				max_length=len(self.expected_src_tokens      )			,				return_tensors='''pt'''			,				)

      __magic_name__						       =    shift_tokens_right(batch['''labels''']			,				self.tokenizer.pad_token_id      )

      self.assertIsInstance(a__			,				a__      )

      self.assertEqual((2, 14)			,				batch.input_ids.shape      )
      self.assertEqual((2, 14)			,				batch.attention_mask.shape      )
      __magic_name__						       =    batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens			,				a__      )
      self.assertEqual(2			,				batch.decoder_input_ids[0, -1]      )  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens			,				[]      )
      self.assertEqual(self.tokenizer.suffix_tokens			,				[self.tokenizer.eos_token_id, EN_CODE]      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.tokenizer(self.src_text			,				padding=a__			,				truncation=a__			,				max_length=3			,				return_tensors='''pt'''      )
      __magic_name__						       =    self.tokenizer(
          text_target=self.tgt_text			,				padding=a__			,				truncation=a__			,				max_length=10			,				return_tensors='''pt'''      )
      __magic_name__						       =    targets['''input_ids''']
      __magic_name__						       =    shift_tokens_right(a__			,				self.tokenizer.pad_token_id      )

      self.assertEqual(batch.input_ids.shape[1]			,				3      )
      self.assertEqual(batch.decoder_input_ids.shape[1]			,				10      )



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.tokenizer._build_translation_inputs(
          '''A test'''			,				return_tensors='''pt'''			,				src_lang='''en_XX'''			,				tgt_lang='''ar_AR'''      )

      self.assertEqual(
          nested_simplify(a__      )			,				{
              # A, test, EOS, en_XX
              '''input_ids''': [[62, 3034, 2, 25_0004]],
              '''attention_mask''': [[1, 1, 1, 1]],
              # ar_AR
              '''forced_bos_token_id''': 25_0001,
          }			,				)









'''simple docstring'''




from random import randint, random




def 						UpperCamelCase	(			a		,						a		,						a		,						a = False		,						a = False		,						a = 5		,						)				->     list:






     '''simple docstring'''
     __magic_name__						       =    [[-1] * number_of_cells]  # Create a highway without any car
     __magic_name__						       =    0
     __magic_name__						       =    max(a		,						0			)
     while i < number_of_cells:
          __magic_name__						       =    (
              randint(0		,						a			) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1		,						max_speed * 2			) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    0
     __magic_name__						       =    highway_now[car_index + 1 :]
     for cell in range(len(a			)			):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a		,						-1			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     # Beforce calculations, the highway is empty
     __magic_name__						       =    [-1] * number_of_cells

     for car_index in range(a			):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               __magic_name__						       =    min(highway_now[car_index] + 1		,						a			)
               # Number of empty cell before the next car
               __magic_name__						       =    get_distance(a		,						a			) - 1
               # We can't have the car causing an accident
               __magic_name__						       =    min(next_highway[car_index]		,						a			)
               if random() < probability:
                    # Randomly, a driver will slow down
                    __magic_name__						       =    max(next_highway[car_index] - 1		,						0			)
     return next_highway




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(highway[0]			)

     for i in range(a			):
          __magic_name__						       =    update(highway[i]		,						a		,						a			)
          __magic_name__						       =    [-1] * number_of_cells

          for car_index in range(a			):
               __magic_name__						       =    next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    __magic_name__						       =    (car_index + speed) % number_of_cells
                    # Commit the change of position
                    __magic_name__						       =    speed
          highway.append(a			)

     return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()






