






'''simple docstring'''





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A							:							Dict  							=		{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 if got_ver is None or want_ver is None:
  raise ValueError(
      F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
      F''' reinstalling {pkg}.''')
 if not ops[op](version.parse(SCREAMING_SNAKE_CASE_)				,     version.parse(SCREAMING_SNAKE_CASE_)):
  raise ImportError(
      F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')






def    lowerCamelCase_     (    lowercase__				,     lowercase__ = None):
 lowerCamelCase__  =     F'''\n{hint}''' if hint is not None else ""

 # non-versioned check
 if re.match(r"^[\w_\-\d]+$"				,     SCREAMING_SNAKE_CASE_):
  lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     requirement, None, None
 else:
  lowerCamelCase__  =     re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"				,     SCREAMING_SNAKE_CASE_)
  if not match:
   raise ValueError(
       "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
       F''' got {requirement}''')
  lowerCamelCase__  ,	lowerCamelCase__  =     match[0]
  lowerCamelCase__  =     want_full.split(",")  # there could be multiple requirements
  lowerCamelCase__  =     {}
  for w in want_range:
   lowerCamelCase__  =     re.findall(r"^([\s!=<>]{1,2})(.+)"				,     SCREAMING_SNAKE_CASE_)
   if not match:
    raise ValueError(
        "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
        F''' but got {requirement}''')
   lowerCamelCase__  ,	lowerCamelCase__  =     match[0]
   lowerCamelCase__  =     want_ver
   if op not in ops:
    raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')

    # special case
 if pkg == "python":
  lowerCamelCase__  =     ".".join([str(SCREAMING_SNAKE_CASE_) for x in sys.version_info[:3]])
  for op, want_ver in wanted.items():
   _compare_versions(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_)
  return

 # check if any version is installed
 try:
  lowerCamelCase__  =     importlib.metadata.version(SCREAMING_SNAKE_CASE_)
 except importlib.metadata.PackageNotFoundError:
  raise importlib.metadata.PackageNotFoundError(
      F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')

 # check that the right version is installed if version number or a range was provided
 if want_ver is not None:
  for op, want_ver in wanted.items():
   _compare_versions(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_)






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
 return require_version(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_)







'''simple docstring'''





from typing import TYPE_CHECKING

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


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

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

