





"""simple docstring"""





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase       =	get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class 			snake_case		(   __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= ErnieMTokenizer
      lowerCamelCase__						= False
      lowerCamelCase__						= True
      lowerCamelCase__						= False
      lowerCamelCase__						= False

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       super().setUp()

       # We have a SentencePiece fixture for testing
       __SCREAMING_SNAKE_CASE :			Dict												=       ErnieMTokenizer(_lowerCamelCase						,							unk_token='''<unk>'''						,							pad_token='''<pad>'''						)
       tokenizer.save_pretrained(self.tmpdirname						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       '''this is a test'''
       __SCREAMING_SNAKE_CASE :			Dict												=       '''this is a test'''
       return input_text, output_text

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''<pad>'''
       __SCREAMING_SNAKE_CASE :			Dict												=       0

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       list(self.get_tokenizer().get_vocab().keys()						)

       self.assertEqual(vocab_keys[0]						,							'''<pad>'''						)
       self.assertEqual(vocab_keys[1]						,							'''<unk>'''						)
       self.assertEqual(vocab_keys[-1]						,							'''▁eloquent'''						)
       self.assertEqual(len(_lowerCamelCase						)						,							3_0_0_0_0						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       self.assertEqual(self.get_tokenizer().vocab_size						,							3_0_0_0_0						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       if not self.test_rust_tokenizer:
        return

       __SCREAMING_SNAKE_CASE :			List[str]												=       self.get_tokenizer()
       __SCREAMING_SNAKE_CASE :			Dict												=       self.get_rust_tokenizer()

       __SCREAMING_SNAKE_CASE :			Dict												=       '''I was born in 92000, and this is falsé.'''

       __SCREAMING_SNAKE_CASE :			str												=       tokenizer.tokenize(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       rust_tokenizer.tokenize(_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.encode(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       rust_tokenizer.encode(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       self.get_rust_tokenizer()
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.encode(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       rust_tokenizer.encode(_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       ErnieMTokenizer(_lowerCamelCase						,							do_lower_case=_lowerCamelCase						,							unk_token='''<unk>'''						,							pad_token='''<pad>'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       tokenizer.tokenize('''This is a test'''						)
       self.assertListEqual(_lowerCamelCase						,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']						)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase						)						,							[4_8, 2_5, 2_1, 1_2_8_9]						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           _lowerCamelCase						,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.convert_tokens_to_ids(_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]						)

       __SCREAMING_SNAKE_CASE :			Dict												=       tokenizer.convert_ids_to_tokens(_lowerCamelCase						)
       self.assertListEqual(
           _lowerCamelCase						,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']						,							)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       ErnieMTokenizer(_lowerCamelCase						,							unk_token='''<unk>'''						,							pad_token='''<pad>'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       tokenizer.encode('''sequence builders'''						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       tokenizer.encode('''multi-sequence build'''						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase						,							_lowerCamelCase						)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       # fmt: off
       __SCREAMING_SNAKE_CASE :			List[Any]												=       {'''input_ids''': [[0, 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, 9, 3_0_4, 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, 5, 5, 5, 1_6, 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, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 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], [0, 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, 1, 1, 1, 1], [0, 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, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_lowerCamelCase						,							model_name='''susnato/ernie-m-base_pytorch'''						,							sequences=[
               '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
               '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
               '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
               '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
               '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
               '''conditioning on both left and right context in all layers.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]						,							)








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

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

