






from typing import TYPE_CHECKING

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


a_		:						str             =    {
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    [
				    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BertForMaskedLM',
				    'BertForMultipleChoice',
				    'BertForNextSentencePrediction',
				    'BertForPreTraining',
				    'BertForQuestionAnswering',
				    'BertForSequenceClassification',
				    'BertForTokenClassification',
				    'BertLayer',
				    'BertLMHeadModel',
				    'BertModel',
				    'BertPreTrainedModel',
				    'load_tf_weights_in_bert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    [
				    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFBertEmbeddings',
				    'TFBertForMaskedLM',
				    'TFBertForMultipleChoice',
				    'TFBertForNextSentencePrediction',
				    'TFBertForPreTraining',
				    'TFBertForQuestionAnswering',
				    'TFBertForSequenceClassification',
				    'TFBertForTokenClassification',
				    'TFBertLMHeadModel',
				    'TFBertMainLayer',
				    'TFBertModel',
				    'TFBertPreTrainedModel',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['TFBertTokenizer']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						str             =    [
				    'FlaxBertForCausalLM',
				    'FlaxBertForMaskedLM',
				    'FlaxBertForMultipleChoice',
				    'FlaxBertForNextSentencePrediction',
				    'FlaxBertForPreTraining',
				    'FlaxBertForQuestionAnswering',
				    'FlaxBertForSequenceClassification',
				    'FlaxBertForTokenClassification',
				    'FlaxBertModel',
				    'FlaxBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				a_		:						Optional[int]             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












import os
from collections.abc import Iterator






def 				lowerCamelCase__				(_UpperCAmelCase = "."):
			for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= [d for d in dir_names if d != 'scripts' and d[0] not in '._']
						for filename in filenames:
									if filename == "__init__.py":
												continue
									if os.path.splitext(_UpperCAmelCase)[1] in (".py", ".ipynb"):
												yield os.path.join(_UpperCAmelCase							,       _UpperCAmelCase).lstrip('./')






def 				lowerCamelCase__				(_UpperCAmelCase):
			return F'''{i * '  '}*''' if i else "\n##"






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= old_path.split(os.sep)
			for i, new_part in enumerate(new_path.split(os.sep)):
						if (i + 1 > len(_UpperCAmelCase) or old_parts[i] != new_part) and new_part:
									print(F'''{md_prefix(_UpperCAmelCase)} {new_part.replace('_'							,       ' ').title()}''')
			return new_path






def 				lowerCamelCase__				(_UpperCAmelCase = "."):
			SCREAMING_SNAKE_CASE						= ''
			for filepath in sorted(good_file_paths(_UpperCAmelCase)):
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= os.path.split(_UpperCAmelCase)
						if filepath != old_path:
									SCREAMING_SNAKE_CASE						= print_path(_UpperCAmelCase							,       _UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= (filepath.count(os.sep) + 1) if filepath else 0
						SCREAMING_SNAKE_CASE						= F'''{filepath}/{filename}'''.replace(' '							,       '%20')
						SCREAMING_SNAKE_CASE						= os.path.splitext(filename.replace('_'							,       ' ').title())[0]
						print(F'''{md_prefix(_UpperCAmelCase)} [{filename}]({url})''')


if __name__ == "__main__":
				print_directory_md('.')




