





import numpy as np




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_="utf-8"       ,		UpperCamelCase_="surrogatepass"	)  ->		Any:
       UpperCamelCase_ 							=	bin(int.from_bytes(text.encode(UpperCamelCase_       ,		UpperCamelCase_	)       ,		"big"	)	)[2:]
       return bits.zfill(8 * ((len(UpperCamelCase_	) + 7) // 8)	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_="utf-8"       ,		UpperCamelCase_="surrogatepass"	)  ->		str:
       UpperCamelCase_ 							=	int(UpperCamelCase_       ,		2	)
       return n.to_bytes((n.bit_length() + 7) // 8       ,		"big"	).decode(UpperCamelCase_       ,		UpperCamelCase_	) or "\0"




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       if size_par + len(UpperCamelCase_	) <= 2**size_par - (len(UpperCamelCase_	) - 1):
              raise ValueError("size of parity don't match with size of data"	)

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

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

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

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

              qtd_bp += 1

       # Mount the message
       UpperCamelCase_ 							=	0  # parity bit counter
       for x in range(0       ,		size_par + len(UpperCamelCase_	)	):
              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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	[]
       # Parity bit counter
       UpperCamelCase_ 							=	0
       # Counter p data bit reading
       UpperCamelCase_ 							=	0
       # list of parity received
       UpperCamelCase_ 							=	[]
       UpperCamelCase_ 							=	[]

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

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

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

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

       UpperCamelCase_ 							=	parity_received == parity
       return data_output, ack






"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					str 		=		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					List[Any] 		=		["OpenAIGPTTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Union[str, Any] 		=		[
		    "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OpenAIGPTDoubleHeadsModel",
		    "OpenAIGPTForSequenceClassification",
		    "OpenAIGPTLMHeadModel",
		    "OpenAIGPTModel",
		    "OpenAIGPTPreTrainedModel",
		    "load_tf_weights_in_openai_gpt",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Optional[Any] 		=		[
		    "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFOpenAIGPTDoubleHeadsModel",
		    "TFOpenAIGPTForSequenceClassification",
		    "TFOpenAIGPTLMHeadModel",
		    "TFOpenAIGPTMainLayer",
		    "TFOpenAIGPTModel",
		    "TFOpenAIGPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					Dict 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




