



"""simple docstring"""


import argparse
import os
import re


snake_case_    					=	"""src/transformers/models/auto"""


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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = False						):
						with open(lowercase_		,     'r'		,     encoding='utf-8'						) as f:
												UpperCAmelCase					=			f.read()

						UpperCAmelCase					=			content.split('\n'						)
						UpperCAmelCase					=			[]
						UpperCAmelCase					=			0
						while line_idx < len(lowercase_						):
												if _re_intro_mapping.search(lines[line_idx]						) is not None:
																		UpperCAmelCase					=			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					=			[]
																		while lines[line_idx].strip() != "]":
																								# Blocks either fit in one line or not
																								if lines[line_idx].strip() == "(":
																														UpperCAmelCase					=			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					=			sorted(lowercase_		,     key=lambda lowercase_						: _re_identifier.search(lowercase_						).groups()[0]						)
																		new_lines += blocks
												else:
																		new_lines.append(lines[line_idx]						)
																		line_idx += 1

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

def 			_lowerCAmelCase				(       lowercase_ = False						):
						UpperCAmelCase					=			[os.path.join(lowercase_		,     lowercase_						) for f in os.listdir(lowercase_						) if f.endswith('.py'						)]
						UpperCAmelCase					=			[sort_auto_mapping(lowercase_		,     overwrite=lowercase_						) for fname in fnames]

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


if __name__ == "__main__":
				snake_case_    					=	argparse.ArgumentParser()
				parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
				snake_case_    					=	parser.parse_args()

				sort_all_auto_mappings(not args.check_only)










"""simple docstring"""


from typing import TYPE_CHECKING

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


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





