



"""simple docstring"""







from typing import TYPE_CHECKING

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


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

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

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A     =      [
							    "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:
							A     =      [
							    "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

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








'''simple docstring'''



import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''   ,				[
        (Version('''1.0.0'''			), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''			), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]   ,				)
def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:int			)		->						List[str]:
				snake_case__      :   Optional[int]      =    Version('''1.0.0'''			)
				assert (version == other) is expected_equality
				assert (version != other) is not expected_equality
				assert (hash(UpperCamelCase__			) == hash(UpperCamelCase__			)) is expected_equality




