



'''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 __A			(  lowerCAmelCase__   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       MvpTokenizer
 __lowerCamelCase    :       Optional[Any]          =       MvpTokenizerFast
 __lowerCamelCase    :       str          =       True
 __lowerCamelCase    :       List[Any]          =       filter_roberta_detectors



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      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(__lowerCAmelCase ,   range(len(__lowerCAmelCase       )       )       )       )
      _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(__lowerCAmelCase       ) + '''\n'''       )
      with open(self.merges_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
           fp.write('''\n'''.join(__lowerCAmelCase       )       )



 def 					a__  (self ,   **A       )    ->     int:


      """simple docstring"""



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



 def 					a__  (self ,   **A       )    ->     Dict:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      return "lower newer", "lower newer"



 @cached_property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



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



 @cached_property
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



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



 @require_torch
 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



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

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

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



 @require_torch
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _a      =  tokenizer(__lowerCAmelCase ,   padding=__lowerCAmelCase ,   return_tensors='''pt'''       )
           # check if input_ids are returned and no labels
           self.assertIn('''input_ids''' ,   __lowerCAmelCase       )
           self.assertIn('''attention_mask''' ,   __lowerCAmelCase       )
           self.assertNotIn('''labels''' ,   __lowerCAmelCase       )
           self.assertNotIn('''decoder_attention_mask''' ,   __lowerCAmelCase       )



 @require_torch
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



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



 @require_torch
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



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



 @require_torch
 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  ['''A long paragraph for summarization.''']
      _a      =  [
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _a      =  tokenizer(__lowerCAmelCase ,   text_target=__lowerCAmelCase ,   return_tensors='''pt'''       )
           _a      =  inputs['''input_ids''']
           _a      =  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 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      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(__lowerCAmelCase ,   **__lowerCAmelCase       )
                _a      =  self.tokenizer_class.from_pretrained(__lowerCAmelCase ,   **__lowerCAmelCase       )
                _a      =  '''A, <mask> AllenNLP sentence.'''
                _a      =  tokenizer_r.encode_plus(__lowerCAmelCase ,   add_special_tokens=__lowerCAmelCase ,   return_token_type_ids=__lowerCAmelCase       )
                _a      =  tokenizer_p.encode_plus(__lowerCAmelCase ,   add_special_tokens=__lowerCAmelCase ,   return_token_type_ids=__lowerCAmelCase       )

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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase          =  {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ResNetForImageClassification''',
						    '''ResNetModel''',
						    '''ResNetPreTrainedModel''',
						    '''ResNetBackbone''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFResNetForImageClassification''',
						    '''TFResNetModel''',
						    '''TFResNetPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxResNetForImageClassification''',
						    '''FlaxResNetModel''',
						    '''FlaxResNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure)




