
"""simple docstring"""


import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowerCAmelCase_				(      _lowerCAmelCase		,					unittest.TestCase    ):



      '''simple docstring'''


      _lowerCamelCase:    Dict      =    ProphetNetTokenizer
      _lowerCamelCase:    List[str]      =    False


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
       super().setUp()

       A						   =   [
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       A						   =   os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['vocab_file']      )
       with open(self.vocab_file  ,'w'  ,encoding='utf-8'      ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[Any]      )     ->   List[str]:
       A						   =   'UNwant\u00E9d,running'
       A						   =   'unwanted, running'
       return input_text, output_text


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[Any]:
       A						   =   self.tokenizer_class(self.vocab_file      )

       A						   =   tokenizer.tokenize('UNwant\u00E9d,running'      )
       self.assertListEqual(_lowerCAmelCase  ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )  ,[9, 6, 7, 12, 10, 11]      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
       A						   =   BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      )  ,['ah', '\u535A', '\u63A8', 'zz']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[Any]:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )  ,['hello', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['hello']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase  ,strip_accents=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['hällo', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['h\u00E9llo']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[Any]:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase  ,strip_accents=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['hallo', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['hello']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Union[str, Any]:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['hallo', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['hello']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )  ,['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase  ,strip_accents=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[Any]:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase  ,strip_accents=_lowerCAmelCase      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   str:
       A						   =   BasicTokenizer(do_lower_case=_lowerCAmelCase  ,never_split=['[UNK]']      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      )  ,['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[Any]:
       A						   =   ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

       A						   =   {}
       for i, token in enumerate(_lowerCAmelCase      ):
        A						   =   i
       A						   =   WordpieceTokenizer(vocab=_lowerCAmelCase  ,unk_token='[UNK]'      )

       self.assertListEqual(tokenizer.tokenize(''      )  ,[]      )

       self.assertListEqual(tokenizer.tokenize('unwanted running'      )  ,['un', '##want', '##ed', 'runn', '##ing']      )

       self.assertListEqual(tokenizer.tokenize('unwantedX running'      )  ,['[UNK]', 'runn', '##ing']      )


      @require_torch
      def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
       A						   =   self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'      )

       A						   =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
       A						   =   [1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
       A						   =   tokenizer(_lowerCAmelCase  ,padding=_lowerCAmelCase  ,return_tensors='pt'      )
       self.assertIsInstance(_lowerCAmelCase  ,_lowerCAmelCase      )
       A						   =   list(batch.input_ids.numpy()[0]      )
       self.assertListEqual(_lowerCAmelCase  ,_lowerCAmelCase      )

       self.assertEqual((2, 9)  ,batch.input_ids.shape      )
       self.assertEqual((2, 9)  ,batch.attention_mask.shape      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[Any]:
       self.assertTrue(_is_whitespace(' '      )      )
       self.assertTrue(_is_whitespace('\t'      )      )
       self.assertTrue(_is_whitespace('\r'      )      )
       self.assertTrue(_is_whitespace('\n'      )      )
       self.assertTrue(_is_whitespace('\u00A0'      )      )

       self.assertFalse(_is_whitespace('A'      )      )
       self.assertFalse(_is_whitespace('-'      )      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
       self.assertTrue(_is_control('\u0005'      )      )

       self.assertFalse(_is_control('A'      )      )
       self.assertFalse(_is_control(' '      )      )
       self.assertFalse(_is_control('\t'      )      )
       self.assertFalse(_is_control('\r'      )      )


      def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[Any]:
       self.assertTrue(_is_punctuation('-'      )      )
       self.assertTrue(_is_punctuation('$'      )      )
       self.assertTrue(_is_punctuation('`'      )      )
       self.assertTrue(_is_punctuation('.'      )      )

       self.assertFalse(_is_punctuation('A'      )      )
       self.assertFalse(_is_punctuation(' '      )      )




      @slow
      def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
       A						   =   self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'      )

       A						   =   tokenizer.encode('sequence builders'  ,add_special_tokens=_lowerCAmelCase      )
       A						   =   tokenizer.encode('multi-sequence build'  ,add_special_tokens=_lowerCAmelCase      )

       A						   =   tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase      )
       A						   =   tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase  ,_lowerCAmelCase      )

       assert encoded_sentence == text + [102]
       assert encoded_pair == text + [102] + text_a + [102]

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

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