


'''simple docstring'''







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


A				:					Any    						=			subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
A				:					Tuple    						=			subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

A				:					List[str]    						=			'''|'''.join(sys.argv[1:])
A				:					Any    						=			re.compile(Rf"""^({joined_dirs}).*?\.py$""")

A				:					List[str]    						=			[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')










'''simple docstring'''







from typing import TYPE_CHECKING

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


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

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






