
import argparse
import os
import re


lowerCamelCase_						: int         =  """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCamelCase_						: str         =  re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
lowerCamelCase_						: Optional[Any]         =  re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase = False						)  ->	List[str]:
				with open(lowerCamelCase     ,    """r"""     ,    encoding="""utf-8"""						) as f:
								UpperCamelCase_:							int	=						f.read()

				UpperCamelCase_:							Union[str, Any]	=						content.split("""\n"""						)
				UpperCamelCase_:							Tuple	=						[]
				UpperCamelCase_:							List[str]	=						0
				while line_idx < len(lowerCamelCase						):
								if _re_intro_mapping.search(lines[line_idx]						) is not None:
												UpperCamelCase_:							Union[str, Any]	=						len(re.search(r"""^(\s*)\S"""     ,    lines[line_idx]						).groups()[0]						) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(""" """ * indent + """("""						):
																new_lines.append(lines[line_idx]						)
																line_idx += 1

												UpperCamelCase_:							Tuple	=						[]
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				UpperCamelCase_:							Optional[Any]	=						line_idx
																				while not lines[line_idx].startswith(""" """ * indent + """)"""						):
																								line_idx += 1
																				blocks.append("""\n""".join(lines[start_idx : line_idx + 1]						)						)
																else:
																				blocks.append(lines[line_idx]						)
																line_idx += 1

												# Sort blocks by their identifiers
												UpperCamelCase_:							Tuple	=						sorted(lowerCamelCase     ,    key=lambda lowerCamelCase						: _re_identifier.search(lowerCamelCase						).groups()[0]						)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]						)
												line_idx += 1

				if overwrite:
								with open(lowerCamelCase     ,    """w"""     ,    encoding="""utf-8"""						) as f:
												f.write("""\n""".join(lowerCamelCase						)						)
				elif "\n".join(lowerCamelCase						) != content:
								return True







def        A__					(						lowerCamelCase = False						)  ->	Union[str, Any]:
				UpperCamelCase_:							Optional[int]	=						[os.path.join(lowerCamelCase     ,    lowerCamelCase						) for f in os.listdir(lowerCamelCase						) if f.endswith(""".py"""						)]
				UpperCamelCase_:							Dict	=						[sort_auto_mapping(lowerCamelCase     ,    overwrite=lowerCamelCase						) for fname in fnames]

				if not overwrite and any(lowerCamelCase						):
								UpperCamelCase_:							Any	=						[f for f, d in zip(lowerCamelCase     ,    lowerCamelCase						) if d]
								raise ValueError(
								    F'''The following files have auto mappings that need sorting: {", ".join(lowerCamelCase						)}. Run `make style` to fix'''
								    """ this."""						)


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  argparse.ArgumentParser()
							parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
							lowerCamelCase_						: str         =  parser.parse_args()

							sort_all_auto_mappings(not args.check_only)








from typing import TYPE_CHECKING

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


lowerCamelCase_						: Optional[Any]         =  {
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""DistilBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DistilBertForMaskedLM""",
							    """DistilBertForMultipleChoice""",
							    """DistilBertForQuestionAnswering""",
							    """DistilBertForSequenceClassification""",
							    """DistilBertForTokenClassification""",
							    """DistilBertModel""",
							    """DistilBertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDistilBertForMaskedLM""",
							    """TFDistilBertForMultipleChoice""",
							    """TFDistilBertForQuestionAnswering""",
							    """TFDistilBertForSequenceClassification""",
							    """TFDistilBertForTokenClassification""",
							    """TFDistilBertMainLayer""",
							    """TFDistilBertModel""",
							    """TFDistilBertPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """FlaxDistilBertForMaskedLM""",
							    """FlaxDistilBertForMultipleChoice""",
							    """FlaxDistilBertForQuestionAnswering""",
							    """FlaxDistilBertForSequenceClassification""",
							    """FlaxDistilBertForTokenClassification""",
							    """FlaxDistilBertModel""",
							    """FlaxDistilBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

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






