





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class   A_	(  lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : str           =							CanineTokenizer
 _lowerCamelCase  : Tuple           =							False




 def      lowercase	(					self       :    List[Any]    ):
      super().setUp()
      _UpperCAmelCase     							=   CanineTokenizer()
      tokenizer.save_pretrained(self.tmpdirname    )




 @cached_property
 def      lowercase	(					self       :    List[str]    ):
      return CanineTokenizer.from_pretrained("google/canine-s"    )




 def      lowercase	(					self       :    Union[str, Any]	,			**snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   self.tokenizer_class.from_pretrained(self.tmpdirname	,			**snake_case_    )
      _UpperCAmelCase     							=   1_0_2_4
      return tokenizer




 @require_torch
 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   self.canine_tokenizer
      _UpperCAmelCase     							=   ["Life is like a box of chocolates.", "You never know what you're gonna get."]
      # fmt: off
      _UpperCAmelCase     							=   [5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
      # fmt: on
      _UpperCAmelCase     							=   tokenizer(snake_case_	,			padding=snake_case_	,			return_tensors="pt"    )
      self.assertIsInstance(snake_case_	,			snake_case_    )

      _UpperCAmelCase     							=   list(batch.input_ids.numpy()[0]    )

      self.assertListEqual(snake_case_	,			snake_case_    )

      self.assertEqual((2, 3_9)	,			batch.input_ids.shape    )
      self.assertEqual((2, 3_9)	,			batch.attention_mask.shape    )




 @require_torch
 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   self.canine_tokenizer
      _UpperCAmelCase     							=   ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
      _UpperCAmelCase     							=   tokenizer(snake_case_	,			padding=snake_case_	,			return_tensors="pt"    )
      # check if input_ids, attention_mask and token_type_ids are returned
      self.assertIn("input_ids"	,			snake_case_    )
      self.assertIn("attention_mask"	,			snake_case_    )
      self.assertIn("token_type_ids"	,			snake_case_    )




 @require_torch
 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.canine_tokenizer
      _UpperCAmelCase     							=   [
          "What's the weater?",
          "It's about 25 degrees.",
      ]
      _UpperCAmelCase     							=   tokenizer(
          text_target=snake_case_	,			max_length=3_2	,			padding="max_length"	,			truncation=snake_case_	,			return_tensors="pt"    )
      self.assertEqual(3_2	,			targets["input_ids"].shape[1]    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      # safety check on max_len default value so we are sure the test works
      _UpperCAmelCase     							=   self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                self.assertNotEqual(tokenizer.model_max_length	,			4_2    )

        # Now let's start the test
      _UpperCAmelCase     							=   self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                # Isolate this from the other tests because we save additional tokens/etc
                _UpperCAmelCase     							=   tempfile.mkdtemp()

                _UpperCAmelCase     							=   " He is very happy, UNwant\u00E9d,running"
                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                tokenizer.save_pretrained(snake_case_    )

                _UpperCAmelCase     							=   tokenizer.__class__.from_pretrained(snake_case_    )
                _UpperCAmelCase     							=   after_tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                self.assertListEqual(snake_case_	,			snake_case_    )

                shutil.rmtree(snake_case_    )

      _UpperCAmelCase     							=   self.get_tokenizers(model_max_length=4_2    )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                # Isolate this from the other tests because we save additional tokens/etc
                _UpperCAmelCase     							=   tempfile.mkdtemp()

                _UpperCAmelCase     							=   " He is very happy, UNwant\u00E9d,running"

                _UpperCAmelCase     							=   tokenizer.additional_special_tokens

                # We can add a new special token for Canine as follows:
                _UpperCAmelCase     							=   chr(0Xe0_07    )
                additional_special_tokens.append(snake_case_    )
                tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}    )
                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                tokenizer.save_pretrained(snake_case_    )

                _UpperCAmelCase     							=   tokenizer.__class__.from_pretrained(snake_case_    )
                _UpperCAmelCase     							=   after_tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                self.assertListEqual(snake_case_	,			snake_case_    )
                self.assertIn(snake_case_	,			after_tokenizer.additional_special_tokens    )
                self.assertEqual(after_tokenizer.model_max_length	,			4_2    )

                _UpperCAmelCase     							=   tokenizer.__class__.from_pretrained(snake_case_	,			model_max_length=4_3    )
                self.assertEqual(tokenizer.model_max_length	,			4_3    )

                shutil.rmtree(snake_case_    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   self.get_tokenizers(do_lower_case=snake_case_    )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                _UpperCAmelCase							,  _UpperCAmelCase     							=   self.get_clean_sequence(snake_case_    )

                # a special token for Canine can be defined as follows:
                _UpperCAmelCase     							=   0Xe0_05
                _UpperCAmelCase     							=   chr(snake_case_    )

                tokenizer.add_special_tokens({"cls_token": special_token}    )
                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                self.assertEqual(len(snake_case_    )	,			1    )

                _UpperCAmelCase     							=   tokenizer.decode(ids + encoded_special_token	,			clean_up_tokenization_spaces=snake_case_    )
                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )

                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                self.assertEqual(snake_case_	,			input_encoded + special_token_id    )

                _UpperCAmelCase     							=   tokenizer.decode(snake_case_	,			skip_special_tokens=snake_case_    )
                self.assertTrue(special_token not in decoded    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   self.get_tokenizers(do_lower_case=snake_case_    )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                _UpperCAmelCase     							=   chr(0Xe0_05    )
                _UpperCAmelCase     							=   chr(0Xe0_06    )

                # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                tokenizer.add_tokens([SPECIAL_TOKEN_1]	,			special_tokens=snake_case_    )
                # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}    )

                _UpperCAmelCase     							=   tokenizer.tokenize(snake_case_    )
                _UpperCAmelCase     							=   tokenizer.tokenize(snake_case_    )

                self.assertEqual(len(snake_case_    )	,			1    )
                self.assertEqual(len(snake_case_    )	,			1    )
                self.assertEqual(token_a[0]	,			snake_case_    )
                self.assertEqual(token_a[0]	,			snake_case_    )




 @require_tokenizers
 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_tokenizers(do_lower_case=snake_case_    )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                # a special token for Canine can be defined as follows:
                _UpperCAmelCase     							=   0Xe0_06
                _UpperCAmelCase     							=   chr(snake_case_    )

                _UpperCAmelCase     							=   AddedToken(snake_case_	,			lstrip=snake_case_    )
                tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}    )

                with tempfile.TemporaryDirectory() as tmp_dir_name:
                     tokenizer.save_pretrained(snake_case_    )
                     tokenizer.from_pretrained(snake_case_    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   []
      if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

      if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

      for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(snake_case_    )

                with open(os.path.join(snake_case_	,			"special_tokens_map.json"    )	,			encoding="utf-8"    ) as json_file:
                     _UpperCAmelCase     							=   json.load(snake_case_    )

                with open(os.path.join(snake_case_	,			"tokenizer_config.json"    )	,			encoding="utf-8"    ) as json_file:
                     _UpperCAmelCase     							=   json.load(snake_case_    )

                # a special token for Canine can be defined as follows:
                _UpperCAmelCase     							=   0Xe0_06
                _UpperCAmelCase     							=   chr(snake_case_    )

                _UpperCAmelCase     							=   [new_token_a]
                _UpperCAmelCase     							=   [new_token_a]

                with open(os.path.join(snake_case_	,			"special_tokens_map.json"    )	,			"w"	,			encoding="utf-8"    ) as outfile:
                     json.dump(snake_case_	,			snake_case_    )
                with open(os.path.join(snake_case_	,			"tokenizer_config.json"    )	,			"w"	,			encoding="utf-8"    ) as outfile:
                     json.dump(snake_case_	,			snake_case_    )

                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                # "special_tokens_map.json" files
                _UpperCAmelCase     							=   tokenizer_class.from_pretrained(snake_case_	,			extra_ids=0    )
                self.assertIn(snake_case_	,			tokenizer_without_change_in_init.additional_special_tokens    )
                # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                self.assertEqual(
                    [new_token_a]	,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]    )    )	,			)

                _UpperCAmelCase     							=   0Xe0_07
                _UpperCAmelCase     							=   chr(snake_case_    )
                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                _UpperCAmelCase     							=   [AddedToken(snake_case_	,			lstrip=snake_case_    )]
                _UpperCAmelCase     							=   tokenizer_class.from_pretrained(
                    snake_case_	,			additional_special_tokens=snake_case_	,			extra_ids=0    )

                self.assertIn(snake_case_	,			tokenizer.additional_special_tokens    )
                # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                self.assertEqual(
                    [new_token_a]	,			tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]    )    )    )




 @require_tokenizers
 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self.get_tokenizers(do_lower_case=snake_case_    )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                _UpperCAmelCase     							=   "hello world"
                if self.space_between_special_tokens:
                     _UpperCAmelCase     							=   "[CLS] hello world [SEP]"
                else:
                     _UpperCAmelCase     							=   input
                _UpperCAmelCase     							=   tokenizer.encode(snake_case_	,			add_special_tokens=snake_case_    )
                _UpperCAmelCase     							=   tokenizer.decode(snake_case_	,			spaces_between_special_tokens=self.space_between_special_tokens    )
                self.assertIn(snake_case_	,			[output, output.lower()]    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                _UpperCAmelCase     							=   [
                    "bos_token",
                    "eos_token",
                    "unk_token",
                    "sep_token",
                    "pad_token",
                    "cls_token",
                    "mask_token",
                ]

      _UpperCAmelCase     							=   "a"
      _UpperCAmelCase     							=   ord(snake_case_    )

      for attr in attributes_list:
           setattr(snake_case_	,			attr + "_id"	,			snake_case_    )
           self.assertEqual(getattr(snake_case_	,			snake_case_    )	,			snake_case_    )
           self.assertEqual(getattr(snake_case_	,			attr + "_id"    )	,			snake_case_    )

           setattr(snake_case_	,			attr + "_id"	,			snake_case_    )
           self.assertEqual(getattr(snake_case_	,			snake_case_    )	,			snake_case_    )
           self.assertEqual(getattr(snake_case_	,			attr + "_id"    )	,			snake_case_    )

      setattr(snake_case_	,			"additional_special_tokens_ids"	,			[]    )
      self.assertListEqual(getattr(snake_case_	,			"additional_special_tokens"    )	,			[]    )
      self.assertListEqual(getattr(snake_case_	,			"additional_special_tokens_ids"    )	,			[]    )

      _UpperCAmelCase     							=   0Xe0_06
      _UpperCAmelCase     							=   chr(snake_case_    )
      setattr(snake_case_	,			"additional_special_tokens_ids"	,			[additional_special_token_id]    )
      self.assertListEqual(getattr(snake_case_	,			"additional_special_tokens"    )	,			[additional_special_token]    )
      self.assertListEqual(getattr(snake_case_	,			"additional_special_tokens_ids"    )	,			[additional_special_token_id]    )




 def      lowercase	(					self       :    Any    ):
      pass




 def      lowercase	(					self       :    List[Any]    ):
      pass




 def      lowercase	(					self       :    Union[str, Any]    ):
      pass




 def      lowercase	(					self       :    List[Any]    ):
      pass




 def      lowercase	(					self       :    List[Any]    ):
      pass




 def      lowercase	(					self       :    int    ):
      pass




 def      lowercase	(					self       :    int    ):
      pass




 def      lowercase	(					self       :    Optional[Any]    ):
      pass











'''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 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     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(__lowercase			,     __lowercase			,     __lowercase       )

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

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

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				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.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				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,
  )




