


'''simple docstring'''

from typing import TYPE_CHECKING

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


_UpperCAmelCase :       Tuple										= {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase :       int										= ["""RoFormerTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase :       Any										= [
	    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RoFormerForCausalLM""",
	    """RoFormerForMaskedLM""",
	    """RoFormerForMultipleChoice""",
	    """RoFormerForQuestionAnswering""",
	    """RoFormerForSequenceClassification""",
	    """RoFormerForTokenClassification""",
	    """RoFormerLayer""",
	    """RoFormerModel""",
	    """RoFormerPreTrainedModel""",
	    """load_tf_weights_in_roformer""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase :       int										= [
	    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRoFormerForCausalLM""",
	    """TFRoFormerForMaskedLM""",
	    """TFRoFormerForMultipleChoice""",
	    """TFRoFormerForQuestionAnswering""",
	    """TFRoFormerForSequenceClassification""",
	    """TFRoFormerForTokenClassification""",
	    """TFRoFormerLayer""",
	    """TFRoFormerModel""",
	    """TFRoFormerPreTrainedModel""",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase :       List[str]										= [
	    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FlaxRoFormerForMaskedLM""",
	    """FlaxRoFormerForMultipleChoice""",
	    """FlaxRoFormerForQuestionAnswering""",
	    """FlaxRoFormerForSequenceClassification""",
	    """FlaxRoFormerForTokenClassification""",
	    """FlaxRoFormerModel""",
	    """FlaxRoFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase :       str										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""




lowercase_			     =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

def        lowercase							(		lowerCAmelCase__ :     bytes			) ->      bytes:
		# Make sure the supplied data is a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
				raise TypeError(lowerCAmelCase__			)

		__a           =			''''''.join(bin(lowerCAmelCase__			)[2:].zfill(8			) for byte in data			)

		__a           =			len(lowerCAmelCase__			) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__a           =			b'''=''' * ((6 - len(lowerCAmelCase__			) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(lowerCAmelCase__			) % 6)
		else:
				__a           =			b''''''

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6]    ,					2			)]
		        for index in range(0    ,					len(lowerCAmelCase__			)    ,					6			)			).encode()
		    + padding
		)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bytes:
		# Make sure encoded_data is either a string or a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) and not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			(
				    '''argument should be a bytes-like object or ASCII string, '''
				    f'''not \'{encoded_data.__class__.__name__}\''''
				)
				raise TypeError(lowerCAmelCase__			)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				try:
						__a           =			encoded_data.decode('''utf-8'''			)
				except UnicodeDecodeError:
						raise ValueError('''base64 encoded data should only contain ASCII characters'''			)

		__a           =			encoded_data.count('''='''			)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

		# Check the padding
		assert len(lowerCAmelCase__			) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__a           =			encoded_data[:-padding]

				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
		else:
				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)

		__a           =			[
		    int(binary_stream[index : index + 8]    ,					2			)
		    for index in range(0    ,					len(lowerCAmelCase__			)    ,					8			)
		]

		return bytes(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






