






"""simple docstring"""
_lowerCAmelCase       =				"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def       __UpperCamelCase						(		snake_case__       ):
	# Make sure the supplied data is a bytes-like object
	if not isinstance(snake_case__ , snake_case__       ):
		A_		:    Optional[int]						     = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
		raise TypeError(snake_case__       )

	A_		:    str						     = """""".join(bin(snake_case__       )[2:].zfill(8       ) for byte in data       )

	A_		:    Optional[int]						     = len(snake_case__       ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		A_		:    List[Any]						     = b"""=""" * ((6 - len(snake_case__       ) % 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(snake_case__       ) % 6)
	else:
		A_		:    Optional[Any]						     = 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(snake_case__       ) , 6       )       ).encode()
	    + padding
	)






def       __UpperCamelCase						(		snake_case__       ):
	# Make sure encoded_data is either a string or a bytes-like object
	if not isinstance(snake_case__ , snake_case__       ) and not isinstance(snake_case__ , snake_case__       ):
		A_		:    Optional[int]						     = (
		    """argument should be a bytes-like object or ASCII string, """
		    F"""not '{encoded_data.__class__.__name__}'"""
		)
		raise TypeError(snake_case__       )

	# 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(snake_case__ , snake_case__       ):
		try:
			A_		:    List[Any]						     = encoded_data.decode("""utf-8"""       )
		except UnicodeDecodeError:
			raise ValueError("""base64 encoded data should only contain ASCII characters"""       )

	A_		:    str						     = 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(snake_case__       ) % 4 == 0 and padding < 3, "Incorrect padding"

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

		A_		:    Tuple						     = """""".join(
		    bin(B64_CHARSET.index(snake_case__       )       )[2:].zfill(6       ) for char in encoded_data       )[: -padding * 2]
	else:
		A_		:    Dict						     = """""".join(
		    bin(B64_CHARSET.index(snake_case__       )       )[2:].zfill(6       ) for char in encoded_data       )

	A_		:    Optional[Any]						     = [
	    int(binary_stream[index : index + 8] , 2       )
	    for index in range(0 , len(snake_case__       ) , 8       )
	]

	return bytes(snake_case__       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()









"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

