




'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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   __UpperCamelCase       ( _A			,					unittest.TestCase				):
       lowercase	:    List[Any]      						=MvpTokenizer
       lowercase	:    int      						=MvpTokenizerFast
       lowercase	:    int      						=True
       lowercase	:    Tuple      						=filter_roberta_detectors






       def      lowercase__     (							self  ):



              """simple docstring"""







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

              lowerCamelCase_							=os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES['''vocab_file''']  )
              lowerCamelCase_							=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      lowercase__     (							self,		**lowerCAmelCase  ):



              """simple docstring"""







              kwargs.update(self.special_tokens_map  )
              return self.tokenizer_class.from_pretrained(self.tmpdirname,		**UpperCamelCase__  )






       def      lowercase__     (							self,		**lowerCAmelCase  ):



              """simple docstring"""







              kwargs.update(self.special_tokens_map  )
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,		**UpperCamelCase__  )






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              return "lower newer", "lower newer"






       @cached_property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






       @cached_property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              lowerCamelCase_							=[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowerCamelCase_							=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  )
                     lowerCamelCase_							=batch.input_ids.tolist()[0]
                     self.assertListEqual(UpperCamelCase__,		UpperCamelCase__  )
                     # Test that special tokens are reset






       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=[
                  '''Summary of the text.''',
                  '''Another summary.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowerCamelCase_							=tokenizer(text_target=UpperCamelCase__,		max_length=32,		padding='''max_length''',		return_tensors='''pt'''  )
                     self.assertEqual(32,		targets['''input_ids'''].shape[1]  )






       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowerCamelCase_							=tokenizer(
                         ['''I am a small frog''' * 1_024, '''I am a small frog'''],		padding=UpperCamelCase__,		truncation=UpperCamelCase__,		return_tensors='''pt'''  )
                     self.assertIsInstance(UpperCamelCase__,		UpperCamelCase__  )
                     self.assertEqual(batch.input_ids.shape,		(2, 1_024)  )






       @require_torch
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=['''A long paragraph for summarization.''']
              lowerCamelCase_							=[
                  '''Summary of the text.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     lowerCamelCase_							=tokenizer(UpperCamelCase__,		text_target=UpperCamelCase__,		return_tensors='''pt'''  )
                     lowerCamelCase_							=inputs['''input_ids''']
                     lowerCamelCase_							=inputs['''labels''']
                     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      lowercase__     (							self  ):



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                            lowerCamelCase_							=self.rust_tokenizer_class.from_pretrained(UpperCamelCase__,		**UpperCamelCase__  )
                            lowerCamelCase_							=self.tokenizer_class.from_pretrained(UpperCamelCase__,		**UpperCamelCase__  )
                            lowerCamelCase_							='''A, <mask> AllenNLP sentence.'''
                            lowerCamelCase_							=tokenizer_r.encode_plus(UpperCamelCase__,		add_special_tokens=UpperCamelCase__,		return_token_type_ids=UpperCamelCase__  )
                            lowerCamelCase_							=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''']  ),		)

                            lowerCamelCase_							=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                            lowerCamelCase_							=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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
                            self.assertSequenceEqual(tokens_r['''input_ids'''],		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

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






from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     Any           =   {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     int           =   [
        'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MCTCTForCTC',
        'MCTCTModel',
        'MCTCTPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
    from .feature_extraction_mctct import MCTCTFeatureExtractor
    from .processing_mctct import MCTCTProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
    import sys

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

