





'''simple docstring'''


import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, 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


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


if is_torch_available():
							from transformers.models.plbart.modeling_plbart import shift_tokens_right

_SCREAMING_SNAKE_CASE	 =							5_00_03
_SCREAMING_SNAKE_CASE	 =							5_00_02

@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):
	__lowerCamelCase						:							int         =					PLBartTokenizer
	__lowerCamelCase						:							Optional[Any]         =					None
	__lowerCamelCase						:							Union[str, Any]         =					False




	def 							_snake_case   (	self		)    ->							Optional[int]:
					super().setUp()

					# We have a SentencePiece fixture for testing
					_lowerCAmelCase				=      PLBartTokenizer(_lowerCAmelCase			,     language_codes="base"			,     keep_accents=_lowerCAmelCase		)
					tokenizer.save_pretrained(self.tmpdirname		)




	def 							_snake_case   (	self		)    ->							Union[str, Any]:
					_lowerCAmelCase				=      PLBartTokenizer(_lowerCAmelCase			,     language_codes="base"			,     keep_accents=_lowerCAmelCase		)

					_lowerCAmelCase				=      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]]			,     )

					_lowerCAmelCase				=      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",
					        ".",
					    ]			,     )
					_lowerCAmelCase				=      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]
					    ]			,     )

					_lowerCAmelCase				=      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>",
					        ".",
					    ]			,     )

					_lowerCAmelCase				=      tokenizer.vocab_size
					_lowerCAmelCase				=      [tokenizer.convert_ids_to_tokens(_lowerCAmelCase		) for x in range(end - 4			,     _lowerCAmelCase		)]

					self.assertListEqual(_lowerCAmelCase			,     ["__java__", "__python__", "__en_XX__", "<mask>"]		)

					_lowerCAmelCase				=      "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
					_lowerCAmelCase				=      tokenizer(_lowerCAmelCase		).input_ids
					self.assertEqual(
					    tokenizer.decode(_lowerCAmelCase			,     skip_special_tokens=_lowerCAmelCase			,     clean_up_tokenization_spaces=_lowerCAmelCase		)			,     _lowerCAmelCase			,     )





	def 							_snake_case   (	self		)    ->							Dict:
					_lowerCAmelCase				=      PLBartTokenizer(_lowerCAmelCase			,     language_codes="multi"			,     keep_accents=_lowerCAmelCase		)

					_lowerCAmelCase				=      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]]			,     )

					_lowerCAmelCase				=      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",
					        ".",
					    ]			,     )
					_lowerCAmelCase				=      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]
					    ]			,     )

					_lowerCAmelCase				=      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>",
					        ".",
					    ]			,     )
					_lowerCAmelCase				=      tokenizer.vocab_size
					_lowerCAmelCase				=      [tokenizer.convert_ids_to_tokens(_lowerCAmelCase		) for x in range(end - 7			,     _lowerCAmelCase		)]

					self.assertListEqual(
					    _lowerCAmelCase			,     ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]		)
					_lowerCAmelCase				=      "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
					_lowerCAmelCase				=      tokenizer(_lowerCAmelCase		).input_ids
					self.assertEqual(
					    tokenizer.decode(_lowerCAmelCase			,     skip_special_tokens=_lowerCAmelCase			,     clean_up_tokenization_spaces=_lowerCAmelCase		)			,     _lowerCAmelCase			,     )







@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase_		(		unittest.TestCase		):
	__lowerCamelCase						:							Union[str, Any]         =					"uclanlp/plbart-python-en_XX"
	__lowerCamelCase						:							Any         =					[
	    "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
	    "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
	]
	__lowerCamelCase						:							Optional[Any]         =					[
	    "Returns the maximum value of a b c.",
	    "Sums the values of a b c.",
	]
	__lowerCamelCase						:							Optional[int]         =					[
	    134,
	    5_452,
	    33_460,
	    33_441,
	    33_463,
	    33_465,
	    33_463,
	    33_449,
	    988,
	    20,
	    33_456,
	    19,
	    33_456,
	    771,
	    39,
	    4_258,
	    889,
	    3_318,
	    33_441,
	    33_463,
	    33_465,
	    33_463,
	    33_449,
	    2_471,
	    2,
	    PYTHON_CODE,
	]




	@classmethod
	def 							_snake_case   (	cls		)    ->							int:
					_lowerCAmelCase				=      PLBartTokenizer.from_pretrained(
					    cls.checkpoint_name			,     language_codes="base"			,     src_lang="python"			,     tgt_lang="en_XX"		)
					_lowerCAmelCase				=      1
					return cls




	def 							_snake_case   (	self		)    ->							int:
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]			,     50001		)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]			,     50002		)
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]			,     50003		)




	def 							_snake_case   (	self		)    ->							List[str]:
					_lowerCAmelCase				=      self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
					self.assertListEqual(self.expected_src_tokens			,     _lowerCAmelCase		)




	def 							_snake_case   (	self		)    ->							Optional[Any]:
					self.assertIn(_lowerCAmelCase			,     self.tokenizer.all_special_ids		)
					_lowerCAmelCase				=      [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
					_lowerCAmelCase				=      self.tokenizer.decode(_lowerCAmelCase			,     skip_special_tokens=_lowerCAmelCase		)
					_lowerCAmelCase				=      self.tokenizer.decode(generated_ids[1:]			,     skip_special_tokens=_lowerCAmelCase		)
					self.assertEqual(_lowerCAmelCase			,     _lowerCAmelCase		)
					self.assertNotIn(self.tokenizer.eos_token			,     _lowerCAmelCase		)




	def 							_snake_case   (	self		)    ->							int:
					_lowerCAmelCase				=      ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
					self.assertIsInstance(src_text[0]			,     _lowerCAmelCase		)
					_lowerCAmelCase				=      10
					_lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							List[Any]:
					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]		)			,     [50004, 50001]		)




	def 							_snake_case   (	self		)    ->							Tuple:
					_lowerCAmelCase				=      tempfile.mkdtemp()
					_lowerCAmelCase				=      self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(_lowerCAmelCase		)
					_lowerCAmelCase				=      PLBartTokenizer.from_pretrained(_lowerCAmelCase		)
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,     _lowerCAmelCase		)




	@require_torch
	def 							_snake_case   (	self		)    ->							Tuple:
					_lowerCAmelCase				=      self.tokenizer(self.src_text			,     text_target=self.tgt_text			,     padding=_lowerCAmelCase			,     return_tensors="pt"		)
					_lowerCAmelCase				=      shift_tokens_right(batch["labels"]			,     self.tokenizer.pad_token_id		)

					# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
					self.assertEqual(batch.input_ids[1][-2:].tolist()			,     [2, PYTHON_CODE]		)
					self.assertEqual(batch.decoder_input_ids[1][0]			,     _lowerCAmelCase		)
					self.assertEqual(batch.decoder_input_ids[1][-1]			,     2		)
					self.assertEqual(batch.labels[1][-2:].tolist()			,     [2, EN_CODE]		)




	@require_torch
	def 							_snake_case   (	self		)    ->							Any:
					_lowerCAmelCase				=      self.tokenizer(
					    self.src_text			,     text_target=self.tgt_text			,     padding=_lowerCAmelCase			,     truncation=_lowerCAmelCase			,     max_length=len(self.expected_src_tokens		)			,     return_tensors="pt"			,     )
					_lowerCAmelCase				=      shift_tokens_right(batch["labels"]			,     self.tokenizer.pad_token_id		)

					self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)

					self.assertEqual((2, 26)			,     batch.input_ids.shape		)
					self.assertEqual((2, 26)			,     batch.attention_mask.shape		)
					_lowerCAmelCase				=      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, PYTHON_CODE]		)




	def 							_snake_case   (	self		)    ->							str:
					_lowerCAmelCase				=      self.tokenizer(self.src_text			,     padding=_lowerCAmelCase			,     truncation=_lowerCAmelCase			,     max_length=3			,     return_tensors="pt"		)
					_lowerCAmelCase				=      self.tokenizer(
					    text_target=self.tgt_text			,     padding=_lowerCAmelCase			,     truncation=_lowerCAmelCase			,     max_length=10			,     return_tensors="pt"		)
					_lowerCAmelCase				=      targets["input_ids"]
					_lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Optional[Any]:
					_lowerCAmelCase				=      self.tokenizer._build_translation_inputs(
					    "A test"			,     return_tensors="pt"			,     src_lang="en_XX"			,     tgt_lang="java"		)

					self.assertEqual(
					    nested_simplify(_lowerCAmelCase		)			,     {
					        # A, test, EOS, en_XX
					        "input_ids": [[150, 242, 2, 50003]],
					        "attention_mask": [[1, 1, 1, 1]],
					        # java
					        "forced_bos_token_id": 50001,
					    }			,     )









'''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
_SCREAMING_SNAKE_CASE	 =							"src/transformers"
_SCREAMING_SNAKE_CASE	 =							"docs/source/en"
_SCREAMING_SNAKE_CASE	 =							"."


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    with open(SCREAMING_SNAKE_CASE_		,       "r"		,       encoding="utf-8"		,       newline="\n"    ) as f:
        _lowerCAmelCase				=      f.readlines()
    # Find the start prompt.
    _lowerCAmelCase				=      0
    while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_    ):
        start_index += 1
    start_index += 1

    _lowerCAmelCase				=      start_index
    while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_    ):
        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 |
_SCREAMING_SNAKE_CASE	 =							"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE	 =							re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_SCREAMING_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.
_SCREAMING_SNAKE_CASE	 =							re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,       SCREAMING_SNAKE_CASE_    )
    return [m.group(0    ) for m in matches]


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      2 if text == "✅" or text == "❌" else len(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      (width - text_length) // 2
    _lowerCAmelCase				=      width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    _lowerCAmelCase				=      {
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    _lowerCAmelCase				=      {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				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      collections.defaultdict(SCREAMING_SNAKE_CASE_    )

    # Let's lookup through all transformers object (once).
    for attr_name in dir(SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      None
        if attr_name.endswith("Tokenizer"    ):
            _lowerCAmelCase				=      slow_tokenizers
            _lowerCAmelCase				=      attr_name[:-9]
        elif attr_name.endswith("TokenizerFast"    ):
            _lowerCAmelCase				=      fast_tokenizers
            _lowerCAmelCase				=      attr_name[:-13]
        elif _re_tf_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
            _lowerCAmelCase				=      tf_models
            _lowerCAmelCase				=      _re_tf_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]
        elif _re_flax_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
            _lowerCAmelCase				=      flax_models
            _lowerCAmelCase				=      _re_flax_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]
        elif _re_pt_models.match(SCREAMING_SNAKE_CASE_    ) is not None:
            _lowerCAmelCase				=      pt_models
            _lowerCAmelCase				=      _re_pt_models.match(SCREAMING_SNAKE_CASE_    ).groups()[0]

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

    # Let's build that table!
    _lowerCAmelCase				=      list(model_name_to_config.keys()    )
    model_names.sort(key=str.lower    )
    _lowerCAmelCase				=      ["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				=      [len(SCREAMING_SNAKE_CASE_    ) + 2 for c in columns]
    _lowerCAmelCase				=      max([len(SCREAMING_SNAKE_CASE_    ) for name in model_names]    ) + 2

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

    _lowerCAmelCase				=      {True: "✅", False: "❌"}
    for name in model_names:
        _lowerCAmelCase				=      model_name_to_prefix[name]
        _lowerCAmelCase				=      [
            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(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ) for l, w in zip(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )]    ) + "|\n"
    return table


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]=False    ):



    '''simple docstring'''





    _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      _find_text_in_file(
        filename=os.path.join(SCREAMING_SNAKE_CASE_		,       "index.md"    )		,       start_prompt="<!--This table is updated automatically from the auto modules"		,       end_prompt="<!-- End table-->"		,       )
    _lowerCAmelCase				=      get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(SCREAMING_SNAKE_CASE_		,       "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__":
       _SCREAMING_SNAKE_CASE	 =							argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       _SCREAMING_SNAKE_CASE	 =							parser.parse_args()

       check_model_table(args.fix_and_overwrite)


