





'''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










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]       )



