




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE    :   Optional[int]       ={
    """configuration_mpnet""": ["""MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MPNetConfig"""],
    """tokenization_mpnet""": ["""MPNetTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE    :   Optional[int]       =["""MPNetTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE    :   Tuple       =[
          """MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MPNetForMaskedLM""",
          """MPNetForMultipleChoice""",
          """MPNetForQuestionAnswering""",
          """MPNetForSequenceClassification""",
          """MPNetForTokenClassification""",
          """MPNetLayer""",
          """MPNetModel""",
          """MPNetPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE    :   List[str]       =[
          """TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFMPNetEmbeddings""",
          """TFMPNetForMaskedLM""",
          """TFMPNetForMultipleChoice""",
          """TFMPNetForQuestionAnswering""",
          """TFMPNetForSequenceClassification""",
          """TFMPNetForTokenClassification""",
          """TFMPNetMainLayer""",
          """TFMPNetModel""",
          """TFMPNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE    :   int       =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__SCREAMING_SNAKE_CASE    :   int       =importlib.metadata.version("""numpy""")
__SCREAMING_SNAKE_CASE    :   Optional[int]       =""".""".join([str(x) for x in sys.version_info[:3]])






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           # lt + different version strings
           require_version_core('numpy<1000.4.5'			)
           require_version_core('numpy<1000.4'			)
           require_version_core('numpy<1000'			)

           # le
           require_version_core('numpy<=1000.4.5'			)
           require_version_core(F"""numpy<={numpy_ver}"""			)

           # eq
           require_version_core(F"""numpy=={numpy_ver}"""			)

           # ne
           require_version_core('numpy!=1000.4.5'			)

           # ge
           require_version_core('numpy>=1.0'			)
           require_version_core('numpy>=1.0.0'			)
           require_version_core(F"""numpy>={numpy_ver}"""			)

           # gt
           require_version_core('numpy>1.0.0'			)

           # mix
           require_version_core('numpy>1.0.0,<1000'			)

           # requirement w/o version
           require_version_core('numpy'			)

           # unmet requirements due to version conflict
           for req in ["numpy==1.0.0", "numpy>=1000.0.0", F"""numpy<{numpy_ver}"""]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn('but found'	,  str(A__			)			)

        # unmet requirements due to missing module
           for req in ["numpipypie>1", "numpipypie2"]:
                 try:
                       require_version_core(A__			)
                 except importlib.metadata.PackageNotFoundError as e:
                       self.assertIn(F"""The '{req}' distribution was not found and is required by this application"""	,  str(A__			)			)
                       self.assertIn('Try: pip install transformers -U'	,  str(A__			)			)

        # bogus requirements formats:
        # 1. whole thing
           for req in ["numpy??1.0.0", "numpy1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('requirement needs to be in the pip package format'	,  str(A__			)			)
        # 2. only operators
           for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('need one of '	,  str(A__			)			)




     def  __snake_case   (    self  :   str			)				->       Optional[int]:
           # matching requirement
           require_version('python>=3.6.0'			)

           # not matching requirements
           for req in ["python>9.9.9", "python<3.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn(F"""but found python=={python_ver}"""	,  str(A__			)			)