



"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


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

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

_lowerCAmelCase							:   Tuple				=				25_00_04
_lowerCAmelCase							:   List[str]				=				25_00_20






@require_sentencepiece
@require_tokenizers
class       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					MBartaaTokenizer
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					MBartaaTokenizerFast
      __SCREAMING_SNAKE_CASE		:  str						=					True
      __SCREAMING_SNAKE_CASE		:  Dict						=					True



      def 		snake_case_   (	self	:      Any		):
           super().setUp()

           # We have a SentencePiece fixture for testing
           _UpperCAmelCase     :      Any														=       MBartaaTokenizer(A	,      src_lang="en_XX"	,      tgt_lang="ro_RO"	,      keep_accents=A		)
           tokenizer.save_pretrained(self.tmpdirname		)



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Union[str, Any]														=       "<s>"
           _UpperCAmelCase     :      Any														=       0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(A		)	,      A		)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(A		)	,      A		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      int														=       list(self.get_tokenizer().get_vocab().keys()		)

           self.assertEqual(vocab_keys[0]	,      "<s>"		)
           self.assertEqual(vocab_keys[1]	,      "<pad>"		)
           self.assertEqual(vocab_keys[-1]	,      "<mask>"		)
           self.assertEqual(len(A		)	,      1_0_5_4		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           self.assertEqual(self.get_tokenizer().vocab_size	,      1_0_5_4		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      int														=       MBartaaTokenizer(A	,      src_lang="en_XX"	,      tgt_lang="ro_RO"	,      keep_accents=A		)

           _UpperCAmelCase     :      Union[str, Any]														=       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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]	,      )

           _UpperCAmelCase     :      str														=       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", "."]	,      )
           _UpperCAmelCase     :      List[str]														=       tokenizer.convert_tokens_to_ids(A		)
           self.assertListEqual(
               A	,      [
                   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]
               ]	,      )

           _UpperCAmelCase     :      Dict														=       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>", "."]	,      )



      @slow
      def 		snake_case_   (	self	:      Tuple		):
           # fmt: off
           _UpperCAmelCase     :      Tuple														=       {"input_ids": [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=A	,      model_name="facebook/mbart-large-50"	,      revision="d3913889c59cd5c9e456b269c376325eabad57e2"	,      )



      def 		snake_case_   (	self	:      Tuple		):
           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

           _UpperCAmelCase     :      Optional[Any]														=       (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
                     _UpperCAmelCase     :      Union[str, Any]														=       self.rust_tokenizer_class.from_pretrained(A	,      **A		)
                     _UpperCAmelCase     :      Any														=       self.tokenizer_class.from_pretrained(A	,      **A		)

                     _UpperCAmelCase     :      Any														=       tempfile.mkdtemp()

                     _UpperCAmelCase     :      List[Any]														=       tokenizer_r.save_pretrained(A		)
                     _UpperCAmelCase     :      int														=       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		)		)
                     _UpperCAmelCase     :      Union[str, Any]														=       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
                     _UpperCAmelCase     :      str														=       tokenizer_r.from_pretrained(A		)
                     _UpperCAmelCase     :      Optional[int]														=       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
                     _UpperCAmelCase     :      Optional[Any]														=       tempfile.mkdtemp()

                     _UpperCAmelCase     :      Union[str, Any]														=       tokenizer_r.save_pretrained(A	,      legacy_format=A		)
                     _UpperCAmelCase     :      str														=       tokenizer_p.save_pretrained(A		)

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

                     # Checks everything loads correctly in the same way
                     _UpperCAmelCase     :      List[Any]														=       tokenizer_r.from_pretrained(A		)
                     _UpperCAmelCase     :      List[Any]														=       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
                     _UpperCAmelCase     :      Optional[Any]														=       tempfile.mkdtemp()

                     _UpperCAmelCase     :      Dict														=       tokenizer_r.save_pretrained(A	,      legacy_format=A		)
                     _UpperCAmelCase     :      str														=       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
                     _UpperCAmelCase     :      Dict														=       tokenizer_r.from_pretrained(A		)
                     _UpperCAmelCase     :      Union[str, Any]														=       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       UpperCAmelCase_    (							unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Dict						=					'facebook/mbart-large-50-one-to-many-mmt'
      __SCREAMING_SNAKE_CASE		:  Tuple						=					[
          ' 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		:  List[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.',
      ]
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					[EN_CODE, 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]



      @classmethod
      def 		snake_case_   (	cls	:      Union[str, Any]		):
           _UpperCAmelCase     :      MBartaaTokenizer														=       MBartaaTokenizer.from_pretrained(
               cls.checkpoint_name	,      src_lang="en_XX"	,      tgt_lang="ro_RO"		)
           _UpperCAmelCase     :      Tuple														=       1
           return cls



      def 		snake_case_   (	self	:      int		):
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]	,      2_5_0_0_0_1		)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]	,      2_5_0_0_0_4		)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]	,      2_5_0_0_2_0		)
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]	,      2_5_0_0_3_8		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Any														=       self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
           self.assertListEqual(self.expected_src_tokens	,      A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           self.assertIn(A	,      self.tokenizer.all_special_ids		)
           _UpperCAmelCase     :      Union[str, Any]														=       [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
           _UpperCAmelCase     :      Any														=       self.tokenizer.decode(A	,      skip_special_tokens=A		)
           _UpperCAmelCase     :      List[Any]														=       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	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[str]														=       ["this is gunna be a long sentence " * 2_0]
           assert isinstance(src_text[0]	,      A		)
           _UpperCAmelCase     :      Optional[Any]														=       1_0
           _UpperCAmelCase     :      Optional[Any]														=       self.tokenizer(A	,      max_length=A	,      truncation=A		).input_ids[0]
           self.assertEqual(ids[0]	,      A		)
           self.assertEqual(ids[-1]	,      2		)
           self.assertEqual(len(A		)	,      A		)



      def 		snake_case_   (	self	:      Tuple		):
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]		)	,      [2_5_0_0_5_3, 2_5_0_0_0_1]		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       tempfile.mkdtemp()
           _UpperCAmelCase     :      int														=       self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(A		)
           _UpperCAmelCase     :      Any														=       MBartaaTokenizer.from_pretrained(A		)
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,      A		)



      @require_torch
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.tokenizer(self.src_text	,      text_target=self.tgt_text	,      padding=A	,      return_tensors="pt"		)
           _UpperCAmelCase     :      Optional[Any]														=       shift_tokens_right(batch["labels"]	,      self.tokenizer.pad_token_id		)

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



      @require_torch
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Dict														=       self.tokenizer(
               self.src_text	,      text_target=self.tgt_text	,      padding=A	,      truncation=A	,      max_length=len(self.expected_src_tokens		)	,      return_tensors="pt"	,      )
           _UpperCAmelCase     :      Optional[int]														=       shift_tokens_right(batch["labels"]	,      self.tokenizer.pad_token_id		)

           self.assertIsInstance(A	,      A		)

           self.assertEqual((2, 1_4)	,      batch.input_ids.shape		)
           self.assertEqual((2, 1_4)	,      batch.attention_mask.shape		)
           _UpperCAmelCase     :      int														=       batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens	,      A		)
           self.assertEqual(2	,      batch.decoder_input_ids[0, 0]		)  # decoder_start_token_id
           # 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 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      str														=       self.tokenizer(self.src_text	,      padding=A	,      truncation=A	,      max_length=3	,      return_tensors="pt"		)
           _UpperCAmelCase     :      Union[str, Any]														=       self.tokenizer(
               text_target=self.tgt_text	,      padding=A	,      truncation=A	,      max_length=1_0	,      return_tensors="pt"		)
           _UpperCAmelCase     :      int														=       targets["input_ids"]
           _UpperCAmelCase     :      Optional[Any]														=       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]	,      1_0		)



      @require_torch
      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       self.tokenizer._build_translation_inputs(
               "A test"	,      return_tensors="pt"	,      src_lang="en_XX"	,      tgt_lang="ar_AR"		)

           self.assertEqual(
               nested_simplify(A		)	,      {
                   # en_XX, A, test, EOS
                   "input_ids": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 2_5_0_0_0_1,
               }	,      )





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
