






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


__UpperCamelCase   :   str      =    get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__UpperCamelCase   :   List[Any]      =    250004
__UpperCamelCase   :   Optional[int]      =    250020


@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[int] =							MBartTokenizer
	__snake_case						:int =							MBartTokenizerFast
	__snake_case						:Union[str, Any] =							True
	__snake_case						:List[str] =							True







	def 			_a (	self   :				Tuple      )		->			Tuple:
		"""simple docstring"""







		super().setUp()

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







	def 			_a (	self   :				Tuple      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	MBartTokenizer(_lowerCAmelCase							,					keep_accents=_lowerCAmelCase      )

		__lowercase   							=	tokenizer.tokenize("""This is a test"""      )
		self.assertListEqual(_lowerCAmelCase							,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]      )

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

		__lowercase   							=	tokenizer.tokenize("""I was born in 92000, and this is falsé."""      )
		self.assertListEqual(
		    _lowerCAmelCase							,					[
		        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""",
		        """.""",
		    ]							,					)
		__lowercase   							=	tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )
		self.assertListEqual(
		    _lowerCAmelCase							,					[
		        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 ^
		    ]							,					)

		__lowercase   							=	tokenizer.convert_ids_to_tokens(_lowerCAmelCase      )
		self.assertListEqual(
		    _lowerCAmelCase							,					[
		        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 			_a (	self   :				Any      )		->			Union[str, Any]:
		"""simple docstring"""







		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

		__lowercase   							=	(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})'      ):
				__lowercase   							=	self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase							,					**_lowerCAmelCase      )
				__lowercase   							=	self.tokenizer_class.from_pretrained(_lowerCAmelCase							,					**_lowerCAmelCase      )

				__lowercase   							=	tempfile.mkdtemp()

				__lowercase   							=	tokenizer_r.save_pretrained(_lowerCAmelCase      )
				__lowercase   							=	tokenizer_p.save_pretrained(_lowerCAmelCase      )

				# 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      )      )
				__lowercase   							=	tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f      )
				self.assertSequenceEqual(_lowerCAmelCase							,					_lowerCAmelCase      )

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

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

				shutil.rmtree(_lowerCAmelCase      )

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

				__lowercase   							=	tokenizer_r.save_pretrained(_lowerCAmelCase							,					legacy_format=_lowerCAmelCase      )
				__lowercase   							=	tokenizer_p.save_pretrained(_lowerCAmelCase      )

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

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

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

				shutil.rmtree(_lowerCAmelCase      )

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

				__lowercase   							=	tokenizer_r.save_pretrained(_lowerCAmelCase							,					legacy_format=_lowerCAmelCase      )
				__lowercase   							=	tokenizer_p.save_pretrained(_lowerCAmelCase      )

				# 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
				__lowercase   							=	tokenizer_r.from_pretrained(_lowerCAmelCase      )
				__lowercase   							=	tokenizer_p.from_pretrained(_lowerCAmelCase      )

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

				shutil.rmtree(_lowerCAmelCase      )


@require_torch
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase     (  unittest.TestCase ):
	__snake_case						:Tuple =							'facebook/mbart-large-en-ro'
	__snake_case						:List[Any] =							[
	    ' 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.',
	]
	__snake_case						:str =							[
	    '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.',
	]
	__snake_case						:Tuple =							[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]







	@classmethod
	def 			_a (	cls   :				Tuple      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase   							=	MBartTokenizer.from_pretrained(
		    cls.checkpoint_name							,					src_lang="""en_XX"""							,					tgt_lang="""ro_RO"""      )
		__lowercase   							=	1
		return cls







	def 			_a (	self   :				Any      )		->			str:
		"""simple docstring"""







		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 			_a (	self   :				List[Any]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
		self.assertListEqual(self.expected_src_tokens							,					_lowerCAmelCase      )







	def 			_a (	self   :				List[str]      )		->			int:
		"""simple docstring"""







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







	def 			_a (	self   :				Union[str, Any]      )		->			List[str]:
		"""simple docstring"""







		__lowercase   							=	["""this is gunna be a long sentence """ * 20]
		assert isinstance(src_text[0]							,					_lowerCAmelCase      )
		__lowercase   							=	10
		__lowercase   							=	self.tokenizer(_lowerCAmelCase							,					max_length=_lowerCAmelCase							,					truncation=_lowerCAmelCase      ).input_ids[0]
		self.assertEqual(ids[-2]							,					2      )
		self.assertEqual(ids[-1]							,					_lowerCAmelCase      )
		self.assertEqual(len(_lowerCAmelCase      )							,					_lowerCAmelCase      )







	def 			_a (	self   :				Dict      )		->			str:
		"""simple docstring"""







		self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]      )							,					[25_0026, 25_0001]      )







	def 			_a (	self   :				Optional[int]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	tempfile.mkdtemp()
		__lowercase   							=	self.tokenizer.fairseq_tokens_to_ids
		self.tokenizer.save_pretrained(_lowerCAmelCase      )
		__lowercase   							=	MBartTokenizer.from_pretrained(_lowerCAmelCase      )
		self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,					_lowerCAmelCase      )







	@require_torch
	def 			_a (	self   :				List[Any]      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(self.src_text							,					text_target=self.tgt_text							,					padding=_lowerCAmelCase							,					return_tensors="""pt"""      )
		__lowercase   							=	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 			_a (	self   :				Tuple      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(
		    self.src_text							,					text_target=self.tgt_text							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=len(self.expected_src_tokens      )							,					return_tensors="""pt"""							,					)

		__lowercase   							=	shift_tokens_right(batch["""labels"""]							,					self.tokenizer.pad_token_id      )

		self.assertIsInstance(_lowerCAmelCase							,					_lowerCAmelCase      )

		self.assertEqual((2, 14)							,					batch.input_ids.shape      )
		self.assertEqual((2, 14)							,					batch.attention_mask.shape      )
		__lowercase   							=	batch.input_ids.tolist()[0]
		self.assertListEqual(self.expected_src_tokens							,					_lowerCAmelCase      )
		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 			_a (	self   :				Optional[Any]      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(self.src_text							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=3							,					return_tensors="""pt"""      )
		__lowercase   							=	self.tokenizer(
		    text_target=self.tgt_text							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=10							,					return_tensors="""pt"""      )
		__lowercase   							=	targets["""input_ids"""]
		__lowercase   							=	shift_tokens_right(_lowerCAmelCase							,					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 			_a (	self   :				Dict      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer._build_translation_inputs(
		    """A test"""							,					return_tensors="""pt"""							,					src_lang="""en_XX"""							,					tgt_lang="""ar_AR"""      )

		self.assertEqual(
		    nested_simplify(_lowerCAmelCase      )							,					{
		        # 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"""


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__snake_case    					=					'src/transformers'
__snake_case    					=					'docs/source/en'
__snake_case    					=					'.'


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Any  ):
 with open(lowerCamelCase__       ,		"""r"""       ,		encoding="""utf-8"""       ,		newline="""\n"""  ) as f:
  lowercase__       :       List[str]				=						f.readlines()
 # Find the start prompt.
 lowercase__       :       str				=						0
 while not lines[start_index].startswith(lowerCamelCase__  ):
  start_index += 1
 start_index += 1

 lowercase__       :       Optional[Any]				=						start_index
 while not lines[end_index].startswith(lowerCamelCase__  ):
  end_index += 1
 end_index -= 1

 while len(lines[start_index]  ) <= 1:
  start_index += 1
 while len(lines[end_index]  ) <= 1:
  end_index -= 1
 end_index += 1
 return "".join(lines[start_index:end_index]  ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__snake_case    					=					'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__snake_case    					=					re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__snake_case    					=					re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case    					=					re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__snake_case    					=					direct_transformers_import(TRANSFORMERS_PATH)


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]  ):
 lowercase__       :       Union[str, Any]				=						re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""       ,		lowerCamelCase__  )
 return [m.group(0  ) for m in matches]


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							int  ):
 lowercase__       :       Any				=						2 if text == """✅""" or text == """❌""" else len(lowerCamelCase__  )
 lowercase__       :       int				=						(width - text_length) // 2
 lowercase__       :       List[str]				=						width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent


def    _lowerCamelCase   (     ):
 lowercase__       :       List[str]				=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase__       :       Optional[int]				=						{
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowercase__       :       str				=						{name: config.replace("""Config"""       ,		""""""  ) for name, config in model_name_to_config.items()}

 # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
 lowercase__       :       Tuple				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Optional[Any]				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Dict				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						collections.defaultdict(lowerCamelCase__  )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(lowerCamelCase__  ):
  lowercase__       :       Optional[Any]				=						None
  if attr_name.endswith("""Tokenizer"""  ):
   lowercase__       :       List[Any]				=						slow_tokenizers
   lowercase__       :       List[str]				=						attr_name[:-9]
  elif attr_name.endswith("""TokenizerFast"""  ):
   lowercase__       :       Tuple				=						fast_tokenizers
   lowercase__       :       str				=						attr_name[:-13]
  elif _re_tf_models.match(lowerCamelCase__  ) is not None:
   lowercase__       :       Tuple				=						tf_models
   lowercase__       :       List[Any]				=						_re_tf_models.match(lowerCamelCase__  ).groups()[0]
  elif _re_flax_models.match(lowerCamelCase__  ) is not None:
   lowercase__       :       List[Any]				=						flax_models
   lowercase__       :       List[Any]				=						_re_flax_models.match(lowerCamelCase__  ).groups()[0]
  elif _re_pt_models.match(lowerCamelCase__  ) is not None:
   lowercase__       :       Union[str, Any]				=						pt_models
   lowercase__       :       Optional[int]				=						_re_pt_models.match(lowerCamelCase__  ).groups()[0]

  if lookup_dict is not None:
   while len(lowerCamelCase__  ) > 0:
    if attr_name in model_name_to_prefix.values():
     lowercase__       :       Tuple				=						True
     break
    # Try again after removing the last word in the name
    lowercase__       :       Union[str, Any]				=						"""""".join(camel_case_split(lowerCamelCase__  )[:-1]  )

    # Let's build that table!
 lowercase__       :       Union[str, Any]				=						list(model_name_to_config.keys()  )
 model_names.sort(key=str.lower  )
 lowercase__       :       List[Any]				=						["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
 # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
 lowercase__       :       str				=						[len(lowerCamelCase__  ) + 2 for c in columns]
 lowercase__       :       Tuple				=						max([len(lowerCamelCase__  ) for name in model_names]  ) + 2

 # Build the table per se
 lowercase__       :       List[Any]				=						"""|""" + """|""".join([_center_text(lowerCamelCase__       ,		lowerCamelCase__  ) for c, w in zip(lowerCamelCase__       ,		lowerCamelCase__  )]  ) + """|\n"""
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]  ) + "|\n"

 lowercase__       :       List[Any]				=						{True: """✅""", False: """❌"""}
 for name in model_names:
  lowercase__       :       int				=						model_name_to_prefix[name]
  lowercase__       :       List[str]				=						[
      name,
      check[slow_tokenizers[prefix]],
      check[fast_tokenizers[prefix]],
      check[pt_models[prefix]],
      check[tf_models[prefix]],
      check[flax_models[prefix]],
  ]
  table += "|" + "|".join([_center_text(lowerCamelCase__       ,		lowerCamelCase__  ) for l, w in zip(lowerCamelCase__       ,		lowerCamelCase__  )]  ) + "|\n"
 return table


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]=False  ):
 lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						_find_text_in_file(
     filename=os.path.join(lowerCamelCase__       ,		"""index.md"""  )       ,		start_prompt="""<!--This table is updated automatically from the auto modules"""       ,		end_prompt="""<!-- End table-->"""       ,		)
 lowercase__       :       Union[str, Any]				=						get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(lowerCamelCase__       ,		"""index.md"""  )       ,		"""w"""       ,		encoding="""utf-8"""       ,		newline="""\n"""  ) as f:
    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]  )
  else:
   raise ValueError(
       """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""  )


if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   __snake_case    					=					parser.parse_args()

   check_model_table(args.fix_and_overwrite)