




import numpy as np




def       __magic_name__    (			A		: List[str],  A		: str="utf-8",  A		: Any="surrogatepass"       ):






			'''simple docstring'''







			a   =      bin(int.from_bytes(text.encode(A,  A       ),  "big"       )       )[2:]
			return bits.zfill(8 * ((len(A       ) + 7) // 8)       )




def       __magic_name__    (			A		: int,  A		: Dict="utf-8",  A		: Optional[Any]="surrogatepass"       ):






			'''simple docstring'''







			a   =      int(A,  2       )
			return n.to_bytes((n.bit_length() + 7) // 8,  "big"       ).decode(A,  A       ) or "\0"




def       __magic_name__    (			A		: int,  A		: List[Any]       ):






			'''simple docstring'''







			if size_par + len(A       ) <= 2**size_par - (len(A       ) - 1):
						raise ValueError("size of parity don't match with size of data"       )

			a   =      []
			a   =      []
			a   =      [bin(A       )[2:] for x in range(1,  size_par + len(A       ) + 1       )]

			# sorted information data for the size of the output data
			a   =      []
			# data position template + parity
			a   =      []
			# parity bit counter
			a   =      0
			# counter position of data bits
			a   =      0

			for x in range(1,  size_par + len(A       ) + 1       ):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(A       ) / np.log(2       )).is_integer():
												data_out_gab.append("P"       )
												a   =      qtd_bp + 1
									else:
												data_out_gab.append("D"       )
						else:
									data_out_gab.append("D"       )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]       )
									cont_data += 1
						else:
									data_ord.append(A       )

    # Calculates parity
			a   =      0  # parity bit counter
			for bp in range(1,  size_par + 1       ):
						# Bit counter one for a given parity
						a   =      0
						# counter to control the loop reading
						a   =      0
						for x in data_ord:
									if x is not None:
												try:
															a   =      (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															a   =      "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2       )

						qtd_bp += 1

			# Mount the message
			a   =      0  # parity bit counter
			for x in range(0,  size_par + len(A       )       ):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]       )       )
									cont_bp += 1
						else:
									data_out.append(data_ord[x]       )

			return data_out




def       __magic_name__    (			A		: Any,  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      []
			# Parity bit counter
			a   =      0
			# Counter p data bit reading
			a   =      0
			# list of parity received
			a   =      []
			a   =      []

			for x in range(1,  len(A       ) + 1       ):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(A       ) / np.log(2       )).is_integer():
									data_out_gab.append("P"       )
									a   =      qtd_bp + 1
						else:
									data_out_gab.append("D"       )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]       )
						else:
									parity_received.append(data[cont_data]       )
						cont_data += 1

			# -----------calculates the parity with the data
			a   =      []
			a   =      []
			a   =      [bin(A       )[2:] for x in range(1,  size_par + len(A       ) + 1       )]

			#  sorted information data for the size of the output data
			a   =      []
			# Data position feedback + parity
			a   =      []
			# Parity bit counter
			a   =      0
			# Counter p data bit reading
			a   =      0

			for x in range(1,  size_par + len(A       ) + 1       ):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(A       ) / np.log(2       )).is_integer():
									data_out_gab.append("P"       )
									a   =      qtd_bp + 1
						else:
									data_out_gab.append("D"       )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]       )
									cont_data += 1
						else:
									data_ord.append(A       )

    # Calculates parity
			a   =      0  # parity bit counter
			for bp in range(1,  size_par + 1       ):
						# Bit counter one for a certain parity
						a   =      0
						# Counter to control loop reading
						a   =      0
						for x in data_ord:
									if x is not None:
												try:
															a   =      (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															a   =      "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2       )       )

						qtd_bp += 1

			# Mount the message
			a   =      0  # Parity bit counter
			for x in range(0,  size_par + len(A       )       ):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]       )       )
									cont_bp += 1
						else:
									data_out.append(data_ord[x]       )

			a   =      parity_received == parity
			return data_output, ack












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."							)
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."							)

							super().__init__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






