





'''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_     (				snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = CanineTokenizer
    UpperCamelCase     = False


    def 			snake_case_(		self       )		->   Any:
        super().setUp()
        _SCREAMING_SNAKE_CASE									=			CanineTokenizer()
        tokenizer.save_pretrained(self.tmpdirname       )


    @cached_property
    def 			snake_case_(		self       )		->   str:
        return CanineTokenizer.from_pretrained("""google/canine-s"""       )


    def 			snake_case_(		self ,		**A       )		->   CanineTokenizer:
        _SCREAMING_SNAKE_CASE									=			self.tokenizer_class.from_pretrained(self.tmpdirname ,		**A       )
        _SCREAMING_SNAKE_CASE									=			1024
        return tokenizer


    @require_torch
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.canine_tokenizer
        _SCREAMING_SNAKE_CASE									=			["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
        # fmt: on
        _SCREAMING_SNAKE_CASE									=			tokenizer(A ,		padding=A ,		return_tensors="""pt"""       )
        self.assertIsInstance(A ,		A       )

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

        self.assertListEqual(A ,		A       )

        self.assertEqual((2, 39) ,		batch.input_ids.shape       )
        self.assertEqual((2, 39) ,		batch.attention_mask.shape       )


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


    @require_torch
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.canine_tokenizer
        _SCREAMING_SNAKE_CASE									=			[
            """What's the weater?""",
            """It's about 25 degrees.""",
        ]
        _SCREAMING_SNAKE_CASE									=			tokenizer(
            text_target=A ,		max_length=32 ,		padding="""max_length""" ,		truncation=A ,		return_tensors="""pt"""       )
        self.assertEqual(32 ,		targets["""input_ids"""].shape[1]       )


    def 			snake_case_(		self       )		->   List[str]:
        # safety check on max_len default value so we are sure the test works
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                self.assertNotEqual(tokenizer.model_max_length ,		42       )

        # Now let's start the test
        _SCREAMING_SNAKE_CASE									=			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
                _SCREAMING_SNAKE_CASE									=			tempfile.mkdtemp()

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

                _SCREAMING_SNAKE_CASE									=			tokenizer.__class__.from_pretrained(A       )
                _SCREAMING_SNAKE_CASE									=			after_tokenizer.encode(A ,		add_special_tokens=A       )
                self.assertListEqual(A ,		A       )

                shutil.rmtree(A       )

        _SCREAMING_SNAKE_CASE									=			self.get_tokenizers(model_max_length=42       )
        for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                # Isolate this from the other tests because we save additional tokens/etc
                _SCREAMING_SNAKE_CASE									=			tempfile.mkdtemp()

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

                _SCREAMING_SNAKE_CASE									=			tokenizer.additional_special_tokens

                # We can add a new special token for Canine as follows:
                _SCREAMING_SNAKE_CASE									=			chr(0Xe007       )
                additional_special_tokens.append(A       )
                tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}       )
                _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
                tokenizer.save_pretrained(A       )

                _SCREAMING_SNAKE_CASE									=			tokenizer.__class__.from_pretrained(A       )
                _SCREAMING_SNAKE_CASE									=			after_tokenizer.encode(A ,		add_special_tokens=A       )
                self.assertListEqual(A ,		A       )
                self.assertIn(A ,		after_tokenizer.additional_special_tokens       )
                self.assertEqual(after_tokenizer.model_max_length ,		42       )

                _SCREAMING_SNAKE_CASE									=			tokenizer.__class__.from_pretrained(A ,		model_max_length=43       )
                self.assertEqual(tokenizer.model_max_length ,		43       )

                shutil.rmtree(A       )


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

                # a special token for Canine can be defined as follows:
                _SCREAMING_SNAKE_CASE									=			0Xe005
                _SCREAMING_SNAKE_CASE									=			chr(A       )

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

                _SCREAMING_SNAKE_CASE									=			tokenizer.decode(ids + encoded_special_token ,		clean_up_tokenization_spaces=A       )
                _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )

                _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
                _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
                self.assertEqual(A ,		input_encoded + special_token_id       )

                _SCREAMING_SNAKE_CASE									=			tokenizer.decode(A ,		skip_special_tokens=A       )
                self.assertTrue(special_token not in decoded       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizers(do_lower_case=A       )
        for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                _SCREAMING_SNAKE_CASE									=			chr(0Xe005       )
                _SCREAMING_SNAKE_CASE									=			chr(0Xe006       )

                # `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=A       )
                # `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]}       )

                _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize(A       )
                _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize(A       )

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


    @require_tokenizers
    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizers(do_lower_case=A       )
        for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                # a special token for Canine can be defined as follows:
                _SCREAMING_SNAKE_CASE									=			0Xe006
                _SCREAMING_SNAKE_CASE									=			chr(A       )

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

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


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			[]
        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(A       )

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

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

                # a special token for Canine can be defined as follows:
                _SCREAMING_SNAKE_CASE									=			0Xe006
                _SCREAMING_SNAKE_CASE									=			chr(A       )

                _SCREAMING_SNAKE_CASE									=			[new_token_a]
                _SCREAMING_SNAKE_CASE									=			[new_token_a]

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

                # 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
                _SCREAMING_SNAKE_CASE									=			tokenizer_class.from_pretrained(A ,		extra_ids=0       )
                self.assertIn(A ,		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]       )       ) ,		)

                _SCREAMING_SNAKE_CASE									=			0Xe007
                _SCREAMING_SNAKE_CASE									=			chr(A       )
                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                _SCREAMING_SNAKE_CASE									=			[AddedToken(A ,		lstrip=A       )]
                _SCREAMING_SNAKE_CASE									=			tokenizer_class.from_pretrained(
                    A ,		additional_special_tokens=A ,		extra_ids=0       )

                self.assertIn(A ,		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 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizers(do_lower_case=A       )
        for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                _SCREAMING_SNAKE_CASE									=			"""hello world"""
                if self.space_between_special_tokens:
                    _SCREAMING_SNAKE_CASE									=			"""[CLS] hello world [SEP]"""
                else:
                    _SCREAMING_SNAKE_CASE									=			input
                _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
                _SCREAMING_SNAKE_CASE									=			tokenizer.decode(A ,		spaces_between_special_tokens=self.space_between_special_tokens       )
                self.assertIn(A ,		[output, output.lower()]       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                _SCREAMING_SNAKE_CASE									=			[
                    """bos_token""",
                    """eos_token""",
                    """unk_token""",
                    """sep_token""",
                    """pad_token""",
                    """cls_token""",
                    """mask_token""",
                ]

        _SCREAMING_SNAKE_CASE									=			"""a"""
        _SCREAMING_SNAKE_CASE									=			ord(A       )

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

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

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

        _SCREAMING_SNAKE_CASE									=			0Xe006
        _SCREAMING_SNAKE_CASE									=			chr(A       )
        setattr(A ,		"""additional_special_tokens_ids""" ,		[additional_special_token_id]       )
        self.assertListEqual(getattr(A ,		"""additional_special_tokens"""       ) ,		[additional_special_token]       )
        self.assertListEqual(getattr(A ,		"""additional_special_tokens_ids"""       ) ,		[additional_special_token_id]       )


    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    def 			snake_case_(		self       )		->   Tuple:
        pass


    def 			snake_case_(		self       )		->   Optional[Any]:
        pass


    def 			snake_case_(		self       )		->   Optional[Any]:
        pass


    def 			snake_case_(		self       )		->   Optional[Any]:
        pass


    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    def 			snake_case_(		self       )		->   Tuple:
        pass





    def 			snake_case_(		self       )		->   Any:
        pass







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

