




from typing import TYPE_CHECKING

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


lowerCamelCase							:						int					       =  {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						Optional[Any]					       =  ["XLNetTokenizer"]

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						Optional[Any]					       =  [
     "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLNetForMultipleChoice",
     "XLNetForQuestionAnswering",
     "XLNetForQuestionAnsweringSimple",
     "XLNetForSequenceClassification",
     "XLNetForTokenClassification",
     "XLNetLMHeadModel",
     "XLNetModel",
     "XLNetPreTrainedModel",
     "load_tf_weights_in_xlnet",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						Optional[Any]					       =  [
     "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLNetForMultipleChoice",
     "TFXLNetForQuestionAnsweringSimple",
     "TFXLNetForSequenceClassification",
     "TFXLNetForTokenClassification",
     "TFXLNetLMHeadModel",
     "TFXLNetMainLayer",
     "TFXLNetModel",
     "TFXLNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlnet import XLNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlnet_fast import XLNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlnet import (
      XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLNetForMultipleChoice,
      XLNetForQuestionAnswering,
      XLNetForQuestionAnsweringSimple,
      XLNetForSequenceClassification,
      XLNetForTokenClassification,
      XLNetLMHeadModel,
      XLNetModel,
      XLNetPreTrainedModel,
      load_tf_weights_in_xlnet,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlnet import (
      TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLNetForMultipleChoice,
      TFXLNetForQuestionAnsweringSimple,
      TFXLNetForSequenceClassification,
      TFXLNetForTokenClassification,
      TFXLNetLMHeadModel,
      TFXLNetMainLayer,
      TFXLNetModel,
      TFXLNetPreTrainedModel,
  )

else:
 import sys

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










'''simple docstring'''






import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					    =	logging.getLogger()




@unittest.skip('''Temporarily disable the doc tests.'''		)
@require_torch
@require_tf
@slow
class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Path  ,      __magic_name__					:   Union[str, None] = None  ,      __magic_name__					:   Union[List[str], None] = None  ,      __magic_name__					:   Union[str, List[str], None] = None  ,      __magic_name__					:   bool = True  ,      )					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [file for file in os.listdir(__magic_name__			) if os.path.isfile(os.path.join(__magic_name__  ,      __magic_name__			)			)]

					if identifier is not None:
								__snake_case					:      List[Any]							      =     [file for file in files if identifier in file]

					if n_identifier is not None:
								if isinstance(__magic_name__  ,      __magic_name__			):
											for n_ in n_identifier:
														__snake_case					:      Optional[int]							      =     [file for file in files if n_ not in file]
								else:
											__snake_case					:      Tuple							      =     [file for file in files if n_identifier not in file]

					__snake_case					:      Dict							      =     ignore_files or []
					ignore_files.append("""__init__.py"""			)
					__snake_case					:      List[str]							      =     [file for file in files if file not in ignore_files]

					for file in files:
								# Open all files
								print("""Testing"""  ,      __magic_name__			)

								if only_modules:
											__snake_case					:      List[Any]							      =     file.split("""."""			)[0]
											try:
														__snake_case					:      List[Any]							      =     getattr(__magic_name__  ,      __magic_name__			)
														__snake_case					:      Union[str, Any]							      =     doctest.DocTestSuite(__magic_name__			)
														__snake_case					:      Dict							      =     unittest.TextTestRunner().run(__magic_name__			)
														self.assertIs(len(result.failures			)  ,      0			)
											except AttributeError:
														logger.info(f'''{module_identifier} is not a module.'''			)
								else:
											__snake_case					:      Tuple							      =     doctest.testfile(str("""..""" / directory / file			)  ,      optionflags=doctest.ELLIPSIS			)
											self.assertIs(result.failed  ,      0			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[Any]							      =     """modeling"""
					__snake_case					:      Union[str, Any]							      =     [
					    """modeling_ctrl.py""",
					    """modeling_tf_ctrl.py""",
					]
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__  ,      ignore_files=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      Any							      =     """tokenization"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[str]							      =     """configuration"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Path("""src/transformers"""			)
					__snake_case					:      int							      =     ["""configuration""", """modeling""", """tokenization"""]
					self.analyze_directory(__magic_name__  ,      n_identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     Path("""docs/source"""			)
					__snake_case					:      Optional[int]							      =     ["""favicon.ico"""]
					self.analyze_directory(__magic_name__  ,      ignore_files=__magic_name__  ,      only_modules=__magic_name__			)


