




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,
)


__lowerCamelCase   :						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:
	__lowerCamelCase   :						str				=			['''BertTokenizerFast''']

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

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Union[str, Any]				=			[
	    '''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

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__lowerCamelCase   :						int				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict[Optional[str], Type[Formatter]]				=			{}
__lowerCamelCase   :						Dict[Optional[str], str]				=			{}
__lowerCamelCase   :						Dict[Optional[str], Exception]				=			{}







def 		lowercase__   (		__A:	type							,__A:	Optional[str]							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__magic_name__							:	Tuple    							=							formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__magic_name__							:	Optional[int]    							=							format_type







def 		lowercase__   (		__A:	Exception							,__A:	Optional[str]							,__A:	Optional[List[str]] = None				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__magic_name__							:	List[str]    							=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	__lowerCamelCase   :						str				=			ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	__lowerCamelCase   :						int				=			ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	__lowerCamelCase   :						str				=			ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])







def 		lowercase__   (		__A:	Optional[str]				):





					'''simple docstring'''






					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 		lowercase__   (		__A:	Optional[str]							,**__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							get_format_type_from_alias(__A				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**__A				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)



