



from typing import TYPE_CHECKING

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


__snake_case     :Dict			       ={
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case     :Dict			       =['RemBertTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case     :List[str]			       =['RemBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case     :Optional[Any]			       =[
				    '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     :Union[str, Any]			       =[
				    '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     :Any			       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




import argparse
from collections import defaultdict






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	List[str]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    F'''{file}_{class_name}_{test_name}'''
							done_test[_id] += 1

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    F'''class {class_name}('''
							A      						=    F'''{4 * " "}def {test_name}('''
							A      						=    F'''{8 * " "}{correct_line.split()[0]}'''
							A      						=    F'''{16 * " "}{correct_line.split()[0]}'''
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    0
							A      						=    0

							A      						=    []
							for line in lines:
														if line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and in_func and (line.startswith(lowerCAmelCase__				) or line.startswith(lowerCAmelCase__				)):
																					A      						=    len(line.split(correct_line.split()[0]				)[0]				)
																					count += 1

																					if count == done_test[_id]:
																												A      						=    True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												A      						=    True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F'''{spaces * " "}{correct_line}'''				)
																					A      						=    A      						=    A      						=    A      						=    False
														else:
																					new_lines.append(lowerCAmelCase__				)

							with open(lowerCAmelCase__			,	'w'				) as f:
														for line in new_lines:
																					f.write(lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple=None				)       ->							Union[str, Any]:

							'''simple docstring'''






							if fail is not None:
														with open(lowerCAmelCase__			,	'r'				) as f:
																					A      						=    {l.strip() for l in f.readlines()}
							else:
														A      						=    None

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    defaultdict(lowerCAmelCase__				)
							for line in correct_lines:
														A						,   A						,   A						,   A      						=    line.split(';'				)
														if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
																					overwrite_file(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :List[Any]			       =argparse.ArgumentParser()
				parser.add_argument('--correct_filename', help='filename of tests with expected result')
				parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
				__snake_case     :Optional[int]			       =parser.parse_args()

				main(args.correct_filename, args.fail_filename)