

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput



@dataclass
class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		float
  __lowerCAmelCase   :		Optional[float]         =     None
  __lowerCAmelCase   :		Optional[float]         =     None






class 					__snake_case     (			unittest.TestCase				):

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30)
      self.assertEqual(x.a  ,						30)
      self.assertIsNone(x.b)
      self.assertIsNone(x.c)
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  x.d

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30  ,						b=10)
      self.assertEqual(x[0]  ,						30)
      self.assertEqual(x[1]  ,						10)
      self.assertEqual(x[:2]  ,						(30, 10))
      self.assertEqual(x[:]  ,						(30, 10))

      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30  ,						c=10)
      self.assertEqual(x[0]  ,						30)
      self.assertEqual(x[1]  ,						10)
      self.assertEqual(x[:2]  ,						(30, 10))
      self.assertEqual(x[:]  ,						(30, 10))

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30  ,						b=10)
      self.assertEqual(x['a']  ,						30)
      self.assertEqual(x['b']  ,						10)
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  x['c']

      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30  ,						c=10)
      self.assertEqual(x['a']  ,						30)
      self.assertEqual(x['c']  ,						10)
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  x['b']

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30)
      self.assertEqual(list(x.keys())  ,						['a'])
      self.assertEqual(list(x.values())  ,						[30])
      self.assertEqual(list(x.items())  ,						[('a', 30)])
      self.assertEqual(list(_A)  ,						['a'])

      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30  ,						b=10)
      self.assertEqual(list(x.keys())  ,						['a', 'b'])
      self.assertEqual(list(x.values())  ,						[30, 10])
      self.assertEqual(list(x.items())  ,						[('a', 30), ('b', 10)])
      self.assertEqual(list(_A)  ,						['a', 'b'])

      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30  ,						c=10)
      self.assertEqual(list(x.keys())  ,						['a', 'c'])
      self.assertEqual(list(x.values())  ,						[30, 10])
      self.assertEqual(list(x.items())  ,						[('a', 30), ('c', 10)])
      self.assertEqual(list(_A)  ,						['a', 'c'])

      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  x.update({'d': 20})
      with self.assertRaises(_A):
          del x["a"]
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  x.pop('a')
      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  x.setdefault('d'  ,						32)

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30)
      SCREAMING_SNAKE_CASE_													=  10
      self.assertEqual(x.a  ,						10)
      self.assertEqual(x['a']  ,						10)

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=30)
      SCREAMING_SNAKE_CASE_													=  10
      self.assertEqual(x.a  ,						10)
      self.assertEqual(x['a']  ,						10)

  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest({'a': 30, 'b': 10})
      self.assertEqual(list(x.keys())  ,						['a', 'b'])
      self.assertEqual(x.a  ,						30)
      self.assertEqual(x.b  ,						10)


  def   lowerCAmelCase__   (    self):
      SCREAMING_SNAKE_CASE_													=  ModelOutputTest([('a', 30), ('b', 10)])
      self.assertEqual(list(x.keys())  ,						['a', 'b'])
      self.assertEqual(x.a  ,						30)
      self.assertEqual(x.b  ,						10)

      with self.assertRaises(_A):
          SCREAMING_SNAKE_CASE_													=  ModelOutputTest([('a', 30), (10, 10)])

      SCREAMING_SNAKE_CASE_													=  ModelOutputTest(a=(30, 30))
      self.assertEqual(list(x.keys())  ,						['a'])
      self.assertEqual(x.a  ,						(30, 30))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__     :					List[str]								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


