

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      		=     {
    '''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      		=     ['''BertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A      		=     [
	    '''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      		=     [
	    '''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      		=     ['''TFBertTokenizer''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A      		=     [
	    '''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      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A      		=     {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FalconForCausalLM''',
     '''FalconModel''',
     '''FalconPreTrainedModel''',
     '''FalconForSequenceClassification''',
     '''FalconForTokenClassification''',
     '''FalconForQuestionAnswering''',
 ]


if TYPE_CHECKING:
 from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_falcon import (
      FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
      FalconPreTrainedModel,
  )


else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

