


'''simple docstring'''
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 					lowerCAmelCase_     (    UpperCAmelCase			,       unittest.TestCase  ):
       __UpperCAmelCase				=BartTokenizer
       __UpperCAmelCase				=BartTokenizerFast
       __UpperCAmelCase				=True
       __UpperCAmelCase				=filter_roberta_detectors






       def      UpperCamelCase	(   self      )->			List[str]:
            super().setUp()
            _A				=       [
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            _A				=       dict(zip(_UpperCamelCase ,    range(len(_UpperCamelCase      )      )      )      )
            _A				=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            _A				=       {'unk_token': '<unk>'}

            _A				=       os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['vocab_file']      )
            _A				=       os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['merges_file']      )
            with open(self.vocab_file ,    'w' ,    encoding='utf-8'      ) as fp:
                 fp.write(json.dumps(_UpperCamelCase      ) + '\n'      )
            with open(self.merges_file ,    'w' ,    encoding='utf-8'      ) as fp:
                 fp.write('\n'.join(_UpperCamelCase      )      )






       def      UpperCamelCase	(   self ,    **_UpperCamelCase      )->			Dict:
            kwargs.update(self.special_tokens_map      )
            return self.tokenizer_class.from_pretrained(self.tmpdirname ,    **_UpperCamelCase      )






       def      UpperCamelCase	(   self ,    **_UpperCamelCase      )->			Any:
            kwargs.update(self.special_tokens_map      )
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,    **_UpperCamelCase      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Optional[int]:
            return "lower newer", "lower newer"






       @cached_property
       def      UpperCamelCase	(   self      )->			Optional[Any]:
            return BartTokenizer.from_pretrained('facebook/bart-large'      )






       @cached_property
       def      UpperCamelCase	(   self      )->			List[Any]:
            return BartTokenizerFast.from_pretrained('facebook/bart-large'      )






       @require_torch
       def      UpperCamelCase	(   self      )->			Optional[int]:
            _A				=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            _A				=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _A				=       tokenizer(_UpperCamelCase ,    max_length=len(_UpperCamelCase      ) ,    padding=_UpperCamelCase ,    return_tensors='pt'      )
                 self.assertIsInstance(_UpperCamelCase ,    _UpperCamelCase      )

                 self.assertEqual((2, 9) ,    batch.input_ids.shape      )
                 self.assertEqual((2, 9) ,    batch.attention_mask.shape      )
                 _A				=       batch.input_ids.tolist()[0]
                 self.assertListEqual(_UpperCamelCase ,    _UpperCamelCase      )
                 # Test that special tokens are reset






       @require_torch
       def      UpperCamelCase	(   self      )->			Dict:
            _A				=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _A				=       tokenizer(_UpperCamelCase ,    padding=_UpperCamelCase ,    return_tensors='pt'      )
                 # check if input_ids are returned and no labels
                 self.assertIn('input_ids' ,    _UpperCamelCase      )
                 self.assertIn('attention_mask' ,    _UpperCamelCase      )
                 self.assertNotIn('labels' ,    _UpperCamelCase      )
                 self.assertNotIn('decoder_attention_mask' ,    _UpperCamelCase      )






       @require_torch
       def      UpperCamelCase	(   self      )->			Optional[int]:
            _A				=       [
                'Summary of the text.',
                'Another summary.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _A				=       tokenizer(text_target=_UpperCamelCase ,    max_length=32 ,    padding='max_length' ,    return_tensors='pt'      )
                 self.assertEqual(32 ,    targets['input_ids'].shape[1]      )






       @require_torch
       def      UpperCamelCase	(   self      )->			Tuple:
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _A				=       tokenizer(
                     ['I am a small frog' * 1024, 'I am a small frog'] ,    padding=_UpperCamelCase ,    truncation=_UpperCamelCase ,    return_tensors='pt'      )
                 self.assertIsInstance(_UpperCamelCase ,    _UpperCamelCase      )
                 self.assertEqual(batch.input_ids.shape ,    (2, 1024)      )






       @require_torch
       def      UpperCamelCase	(   self      )->			Tuple:
            _A				=       ['A long paragraph for summarization.']
            _A				=       [
                'Summary of the text.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                 _A				=       tokenizer(_UpperCamelCase ,    return_tensors='pt'      )
                 _A				=       tokenizer(text_target=_UpperCamelCase ,    return_tensors='pt'      )
                 _A				=       inputs['input_ids']
                 _A				=       targets['input_ids']
                 self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
                 self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
                 self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
                 self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )






       def      UpperCamelCase	(   self      )->			List[str]:
            pass






       def      UpperCamelCase	(   self      )->			Optional[Any]:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                      _A				=       self.rust_tokenizer_class.from_pretrained(_UpperCamelCase ,    **_UpperCamelCase      )
                      _A				=       self.tokenizer_class.from_pretrained(_UpperCamelCase ,    **_UpperCamelCase      )
                      _A				=       'A, <mask> AllenNLP sentence.'
                      _A				=       tokenizer_r.encode_plus(_UpperCamelCase ,    add_special_tokens=_UpperCamelCase ,    return_token_type_ids=_UpperCamelCase      )
                      _A				=       tokenizer_p.encode_plus(_UpperCamelCase ,    add_special_tokens=_UpperCamelCase ,    return_token_type_ids=_UpperCamelCase      )

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r['token_type_ids']      ) ,    sum(tokens_p['token_type_ids']      )      )

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      ) ,    sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      ) ,    )

                      _A				=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
                      _A				=       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p['input_ids'] ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
                      self.assertSequenceEqual(tokens_r['input_ids'] ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

                      self.assertSequenceEqual(
                          _UpperCamelCase ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
                      self.assertSequenceEqual(
                          _UpperCamelCase ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )









'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase  =				{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvNextV2ForImageClassification""",
        """ConvNextV2Model""",
        """ConvNextV2PreTrainedModel""",
        """ConvNextV2Backbone""",
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase  =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)





