

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


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


if is_torch_available():
					from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a		=256047
a		=256145






@require_sentencepiece
@require_tokenizers
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				NllbTokenizer
							_UpperCAmelCase   :  Tuple										=				NllbTokenizerFast
							_UpperCAmelCase   :  int										=				True
							_UpperCAmelCase   :  Union[str, Any]										=				True
							_UpperCAmelCase   :  Tuple										=				{}
							def    lowerCAmelCase					(		self		:      Dict):
								super().setUp()

								# We have a SentencePiece fixture for testing
								__lowerCamelCase    :    List[str] 	=			NllbTokenizer(SCREAMING_SNAKE_CASE__       ,keep_accents=SCREAMING_SNAKE_CASE__)
								tokenizer.save_pretrained(self.tmpdirname)
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    List[str] 	=			NllbTokenizer(SCREAMING_SNAKE_CASE__       ,keep_accents=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			tokenizer.tokenize('This is a test')
								self.assertListEqual(SCREAMING_SNAKE_CASE__       ,['▁This', '▁is', '▁a', '▁t', 'est'])

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__)       ,[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]       ,)

								__lowerCamelCase    :    List[Any] 	=			tokenizer.tokenize('I was born in 92000, and this is falsé.')
								self.assertListEqual(
								    SCREAMING_SNAKE_CASE__       ,[
								        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',
								        '.',
								    ]       ,)
								__lowerCamelCase    :    str 	=			tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__)
								self.assertListEqual(
								    SCREAMING_SNAKE_CASE__       ,[
								        value + tokenizer.fairseq_offset
								        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
								    ]       ,)

								__lowerCamelCase    :    List[Any] 	=			tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__)
								self.assertListEqual(
								    SCREAMING_SNAKE_CASE__       ,[
								        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    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    List[str] 	=			(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCamelCase    :    List[Any] 	=			self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Any 	=			self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    Union[str, Any] 	=			tempfile.mkdtemp()

										__lowerCamelCase    :    Tuple 	=			tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    List[str] 	=			tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__)

										# 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))
										__lowerCamelCase    :    List[Any] 	=			tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
										self.assertSequenceEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

										# Checks everything loads correctly in the same way
										__lowerCamelCase    :    Dict 	=			tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Any 	=			tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__)

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

										shutil.rmtree(SCREAMING_SNAKE_CASE__)

										# Save tokenizer rust, legacy_format=True
										__lowerCamelCase    :    Tuple 	=			tempfile.mkdtemp()

										__lowerCamelCase    :    Optional[int] 	=			tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__       ,legacy_format=SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    List[Any] 	=			tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__)

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

										# Checks everything loads correctly in the same way
										__lowerCamelCase    :    Any 	=			tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    int 	=			tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__)

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

										shutil.rmtree(SCREAMING_SNAKE_CASE__)

										# Save tokenizer rust, legacy_format=False
										__lowerCamelCase    :    List[Any] 	=			tempfile.mkdtemp()

										__lowerCamelCase    :    List[str] 	=			tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__       ,legacy_format=SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__)

										# 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
										__lowerCamelCase    :    List[Any] 	=			tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Any 	=			tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__)

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

										shutil.rmtree(SCREAMING_SNAKE_CASE__)
							@require_torch
							def    lowerCAmelCase					(		self		:      Dict):
								if not self.test_seqaseq:
									return

								__lowerCamelCase    :    Optional[int] 	=			self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
										# Longer text that will definitely require truncation.
										__lowerCamelCase    :    Dict 	=			[
										    ' 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.',
										]
										__lowerCamelCase    :    Optional[Any] 	=			[
										    '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.',
										]
										try:
											__lowerCamelCase    :    Union[str, Any] 	=			tokenizer.prepare_seqaseq_batch(
											    src_texts=SCREAMING_SNAKE_CASE__       ,tgt_texts=SCREAMING_SNAKE_CASE__       ,max_length=3       ,max_target_length=1_0       ,return_tensors='pt'       ,src_lang='eng_Latn'       ,tgt_lang='ron_Latn'       ,)
										except NotImplementedError:
											return
										self.assertEqual(batch.input_ids.shape[1]       ,3)
										self.assertEqual(batch.labels.shape[1]       ,1_0)
										# max_target_length will default to max_length if not specified
										__lowerCamelCase    :    Tuple 	=			tokenizer.prepare_seqaseq_batch(
										    SCREAMING_SNAKE_CASE__       ,tgt_texts=SCREAMING_SNAKE_CASE__       ,max_length=3       ,return_tensors='pt')
										self.assertEqual(batch.input_ids.shape[1]       ,3)
										self.assertEqual(batch.labels.shape[1]       ,3)

										__lowerCamelCase    :    List[str] 	=			tokenizer.prepare_seqaseq_batch(
										    src_texts=SCREAMING_SNAKE_CASE__       ,max_length=3       ,max_target_length=1_0       ,return_tensors='pt')
										self.assertEqual(batch_encoder_only.input_ids.shape[1]       ,3)
										self.assertEqual(batch_encoder_only.attention_mask.shape[1]       ,3)
										self.assertNotIn('decoder_input_ids'       ,SCREAMING_SNAKE_CASE__)
							@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.')
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								pass


							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCamelCase    :    str 	=			[AddedToken('<special>'       ,lstrip=SCREAMING_SNAKE_CASE__)]

										__lowerCamelCase    :    Optional[Any] 	=			self.rust_tokenizer_class.from_pretrained(
										    SCREAMING_SNAKE_CASE__       ,additional_special_tokens=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Optional[Any] 	=			tokenizer_r.encode('Hey this is a <special> token')

										__lowerCamelCase    :    List[Any] 	=			tokenizer_r.encode('<special>'       ,add_special_tokens=SCREAMING_SNAKE_CASE__)[0]

										self.assertTrue(special_token_id in r_output)

										if self.test_slow_tokenizer:
											__lowerCamelCase    :    Any 	=			self.rust_tokenizer_class.from_pretrained(
											    SCREAMING_SNAKE_CASE__       ,additional_special_tokens=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,)
											__lowerCamelCase    :    Optional[int] 	=			self.tokenizer_class.from_pretrained(
											    SCREAMING_SNAKE_CASE__       ,additional_special_tokens=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

											__lowerCamelCase    :    str 	=			tokenizer_p.encode('Hey this is a <special> token')

											__lowerCamelCase    :    Optional[int] 	=			tokenizer_cr.encode('Hey this is a <special> token')

											self.assertEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
											self.assertEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
											self.assertTrue(special_token_id in p_output)
											self.assertTrue(special_token_id in cr_output)






@require_torch
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							_UpperCAmelCase   :  List[Any]										=				'''facebook/nllb-200-distilled-600M'''
							_UpperCAmelCase   :  Optional[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.''',
							]
							_UpperCAmelCase   :  Optional[int]										=				[
							    '''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.''',
							]
							_UpperCAmelCase   :  Any										=				[
							    256_047,
							    16_297,
							    134_408,
							    8_165,
							    248_066,
							    14_734,
							    950,
							    1_135,
							    105_721,
							    3_573,
							    83,
							    27_352,
							    108,
							    49_486,
							    2,
							]
							@classmethod
							def    lowerCAmelCase					(		cls		:      int):
								__lowerCamelCase    :    NllbTokenizer 	=			NllbTokenizer.from_pretrained(
								    cls.checkpoint_name       ,src_lang='eng_Latn'       ,tgt_lang='ron_Latn')
								__lowerCamelCase    :    str 	=			1
								return cls
							def    lowerCAmelCase					(		self		:      Tuple):
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']       ,2_5_6_0_0_1)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']       ,2_5_6_0_0_2)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']       ,2_5_6_0_5_7)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    str 	=			self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
								self.assertListEqual(self.expected_src_tokens       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[str]):
								self.assertIn(SCREAMING_SNAKE_CASE__       ,self.tokenizer.all_special_ids)
								# fmt: off
								__lowerCamelCase    :    List[str] 	=			[RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
								# fmt: on

								__lowerCamelCase    :    Dict 	=			self.tokenizer.decode(SCREAMING_SNAKE_CASE__       ,skip_special_tokens=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			self.tokenizer.decode(generated_ids[1:]       ,skip_special_tokens=SCREAMING_SNAKE_CASE__)
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertNotIn(self.tokenizer.eos_token       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Union[str, Any] 	=			['this is gunna be a long sentence ' * 2_0]
								assert isinstance(src_text[0]       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			1_0
								__lowerCamelCase    :    List[Any] 	=			self.tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__       ,truncation=SCREAMING_SNAKE_CASE__).input_ids[0]
								self.assertEqual(ids[-1]       ,2)
								self.assertEqual(ids[0]       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(len(SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[Any]):
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])       ,[2_5_6_2_0_3, 3])
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    int 	=			tempfile.mkdtemp()
								__lowerCamelCase    :    List[Any] 	=			self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Any 	=			NllbTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,SCREAMING_SNAKE_CASE__)
							@require_torch
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Any 	=			self.tokenizer(
								    self.src_text       ,text_target=self.tgt_text       ,padding=SCREAMING_SNAKE_CASE__       ,truncation=SCREAMING_SNAKE_CASE__       ,max_length=len(self.expected_src_tokens)       ,return_tensors='pt'       ,)
								__lowerCamelCase    :    Any 	=			shift_tokens_right(
								    batch['labels']       ,self.tokenizer.pad_token_id       ,self.tokenizer.lang_code_to_id['ron_Latn'])

								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

								self.assertEqual((2, 1_5)       ,batch.input_ids.shape)
								self.assertEqual((2, 1_5)       ,batch.attention_mask.shape)
								__lowerCamelCase    :    Dict 	=			batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,batch.decoder_input_ids[0, 0])  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens       ,[EN_CODE])
								self.assertEqual(self.tokenizer.suffix_tokens       ,[self.tokenizer.eos_token_id])
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Dict 	=			self.tokenizer(self.src_text       ,padding=SCREAMING_SNAKE_CASE__       ,truncation=SCREAMING_SNAKE_CASE__       ,max_length=3       ,return_tensors='pt')
								__lowerCamelCase    :    List[Any] 	=			self.tokenizer(
								    text_target=self.tgt_text       ,padding=SCREAMING_SNAKE_CASE__       ,truncation=SCREAMING_SNAKE_CASE__       ,max_length=1_0       ,return_tensors='pt')
								__lowerCamelCase    :    Optional[Any] 	=			targets['input_ids']
								__lowerCamelCase    :    Optional[Any] 	=			shift_tokens_right(
								    SCREAMING_SNAKE_CASE__       ,self.tokenizer.pad_token_id       ,decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]       ,)

								self.assertEqual(batch.input_ids.shape[1]       ,3)
								self.assertEqual(batch.decoder_input_ids.shape[1]       ,1_0)
							@require_torch
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Dict 	=			self.tokenizer._build_translation_inputs(
								    'A test'       ,return_tensors='pt'       ,src_lang='eng_Latn'       ,tgt_lang='fra_Latn')

								self.assertEqual(
								    nested_simplify(SCREAMING_SNAKE_CASE__)       ,{
								        # A, test, EOS, en_XX
								        'input_ids': [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 2_5_6_0_5_7,
								    }       ,)


							@require_torch
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			True
								__lowerCamelCase    :    List[str] 	=			self.tokenizer(
								    'UN Chief says there is no military solution in Syria'       ,src_lang='eng_Latn'       ,tgt_lang='fra_Latn')
								self.assertEqual(
								    inputs.input_ids       ,[1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7])

								__lowerCamelCase    :    Any 	=			False
								__lowerCamelCase    :    Union[str, Any] 	=			self.tokenizer(
								    'UN Chief says there is no military solution in Syria'       ,src_lang='eng_Latn'       ,tgt_lang='fra_Latn')
								self.assertEqual(
								    inputs.input_ids       ,[2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2])







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
a		="""src/transformers"""
a		="""docs/source/en"""
a		="""."""






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

	__lowerCamelCase    :    int 	=			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 |
a		="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
a		=re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a		=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.
a		=re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    int 	=			re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,			lowerCamelCase__      )
	return [m.group(0      ) for m in matches]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    int 	=			2 if text == '✅' or text == '❌' else len(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			(width - text_length) // 2
	__lowerCamelCase    :    List[Any] 	=			width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def        SCREAMING_SNAKE_CASE__	(  )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__lowerCamelCase    :    List[str] 	=			{
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	__lowerCamelCase    :    Dict 	=			{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.
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    List[str] 	=			collections.defaultdict(lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			collections.defaultdict(lowerCamelCase__      )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(lowerCamelCase__      ):
		__lowerCamelCase    :    List[Any] 	=			None
		if attr_name.endswith('Tokenizer'      ):
			__lowerCamelCase    :    Dict 	=			slow_tokenizers
			__lowerCamelCase    :    List[Any] 	=			attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'      ):
			__lowerCamelCase    :    Union[str, Any] 	=			fast_tokenizers
			__lowerCamelCase    :    str 	=			attr_name[:-1_3]
		elif _re_tf_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    List[str] 	=			tf_models
			__lowerCamelCase    :    Optional[int] 	=			_re_tf_models.match(lowerCamelCase__      ).groups()[0]
		elif _re_flax_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    List[Any] 	=			flax_models
			__lowerCamelCase    :    Optional[Any] 	=			_re_flax_models.match(lowerCamelCase__      ).groups()[0]
		elif _re_pt_models.match(lowerCamelCase__      ) is not None:
			__lowerCamelCase    :    Optional[int] 	=			pt_models
			__lowerCamelCase    :    Any 	=			_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():
					__lowerCamelCase    :    List[Any] 	=			True
					break
				# Try again after removing the last word in the name
				__lowerCamelCase    :    str 	=			''.join(camel_case_split(lowerCamelCase__      )[:-1]      )

    # Let's build that table!
	__lowerCamelCase    :    str 	=			list(model_name_to_config.keys()      )
	model_names.sort(key=str.lower      )
	__lowerCamelCase    :    Union[str, 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).
	__lowerCamelCase    :    List[Any] 	=			[len(lowerCamelCase__      ) + 2 for c in columns]
	__lowerCamelCase    :    int 	=			max([len(lowerCamelCase__      ) for name in model_names]      ) + 2

	# Build the table per se
	__lowerCamelCase    :    Union[str, 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"

	__lowerCamelCase    :    List[str] 	=			{True: '✅', False: '❌'}
	for name in model_names:
		__lowerCamelCase    :    Optional[int] 	=			model_name_to_prefix[name]
		__lowerCamelCase    :    Any 	=			[
		    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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=False      )  ->					Any:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[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-->'     ,			)
	__lowerCamelCase    :    List[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__":
					a		=argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					a		=parser.parse_args()

					check_model_table(args.fix_and_overwrite)




