


import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
snake_case_  :	str							  =							logging.getLogger(__name__)







def   lowerCamelCase(      a__):
				np.random.seed(args.seed)
				torch.manual_seed(args.seed)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed)







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_name_or_path'''		,default=a__		,type=a__		,required=a__		,)
				parser.add_argument('''--prompt'''		,type=a__		,default='''''')
				parser.add_argument('''--length'''		,type=a__		,default=20)
				parser.add_argument('''--stop_token'''		,type=a__		,default=a__		,help='''Token at which text generation is stopped''')
				parser.add_argument(
				    '''--temperature'''		,type=a__		,default=1.0		,help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''		,)
				parser.add_argument(
				    '''--repetition_penalty'''		,type=a__		,default=1.0		,help='''primarily useful for CTRL model; in that case, use 1.2''')
				parser.add_argument('''--k'''		,type=a__		,default=0)
				parser.add_argument('''--penalty_alpha'''		,type=a__		,default=0.0)
				parser.add_argument('''--p'''		,type=a__		,default=0.9)

				parser.add_argument('''--prefix'''		,type=a__		,default=''''''		,help='''Text added prior to input.''')
				parser.add_argument('''--padding_text'''		,type=a__		,default=''''''		,help='''Deprecated, the use of `--prefix` is preferred.''')
				parser.add_argument('''--xlm_language'''		,type=a__		,default=''''''		,help='''Optional language when used with the XLM model.''')

				parser.add_argument('''--seed'''		,type=a__		,default=42		,help='''random seed for initialization''')
				parser.add_argument('''--no_cuda'''		,action='''store_true'''		,help='''Avoid using CUDA when available''')
				parser.add_argument(
				    '''--fp16'''		,action='''store_true'''		,help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''		,)
				_SCREAMING_SNAKE_CASE	     =parser.parse_args()

				_SCREAMING_SNAKE_CASE	     =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
				_SCREAMING_SNAKE_CASE	     =0 if args.no_cuda else torch.cuda.device_count()

				logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}")

				set_seed(a__)

				# Initialize the model and tokenizer
				_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(args.model_name_or_path)
				_SCREAMING_SNAKE_CASE	     =AutoModelForCausalLM.from_pretrained(args.model_name_or_path)

				# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
				# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
				model.to(args.device)

				if args.fpaa:
								model.half()

				logger.info(a__)
				_SCREAMING_SNAKE_CASE	     =args.prompt if args.prompt else input('''Model prompt >>> ''')

				_SCREAMING_SNAKE_CASE	     =tokenizer(a__		,return_tensors='''pt'''		,add_special_tokens=a__)
				_SCREAMING_SNAKE_CASE	     ={key: value.to(args.device) for key, value in inputs.items()}

				_SCREAMING_SNAKE_CASE	     =model.generate(
				    **a__		,max_length=args.length + len(inputs['''input_ids'''][0])		,penalty_alpha=args.penalty_alpha		,top_k=args.k		,)

				_SCREAMING_SNAKE_CASE	     =[]
				for generated_sequence_idx, generated_sequence in enumerate(a__):
								print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
								_SCREAMING_SNAKE_CASE	     =generated_sequence.tolist()

								# Decode text
								_SCREAMING_SNAKE_CASE	     =tokenizer.decode(a__		,clean_up_tokenization_spaces=a__		,add_special_tokens=a__)

								# Remove all text after the stop token
								_SCREAMING_SNAKE_CASE	     =text[: text.find(args.stop_token) if args.stop_token else None]

								# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
								_SCREAMING_SNAKE_CASE	     =(
								    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]		,clean_up_tokenization_spaces=a__)) :]
								)

								generated_sequences.append(a__)
								print(a__)

				return generated_sequences


if __name__ == "__main__":
							main()

"""simple docstring"""

import numpy as np
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    bin(int.from_bytes(text.encode(a_,						a_			),						'big'			)			)[2:]
		return bits.zfill(8 * ((len(a_			) + 7) // 8)			)
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			List[Any]		=    int(a_,						2			)
		return n.to_bytes((n.bit_length() + 7) // 8,						'big'			).decode(a_,						a_			) or "\0"
def 		UpperCAmelCase					(		a_,						a_			):





		'''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'			)

		lowerCamelCase     :			List[str]		=    []
		lowerCamelCase     :			str		=    []
		lowerCamelCase     :			Any		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

		# sorted information data for the size of the output data
		lowerCamelCase     :			int		=    []
		# data position template + parity
		lowerCamelCase     :			Union[str, Any]		=    []
		# parity bit counter
		lowerCamelCase     :			Optional[int]		=    0
		# counter position of data bits
		lowerCamelCase     :			Any		=    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'			)
								lowerCamelCase     :			List[Any]		=    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
		lowerCamelCase     :			Optional[Any]		=    0  # parity bit counter
		for bp in range(1,						size_par + 1			):
				# Bit counter one for a given parity
				lowerCamelCase     :			Union[str, Any]		=    0
				# counter to control the loop reading
				lowerCamelCase     :			Optional[int]		=    0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase     :			Union[str, Any]		=    (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase     :			str		=    '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2			)

				qtd_bp += 1

		# Mount the message
		lowerCamelCase     :			Tuple		=    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 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    []
		# Parity bit counter
		lowerCamelCase     :			Dict		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    0
		# list of parity received
		lowerCamelCase     :			Optional[int]		=    []
		lowerCamelCase     :			Any		=    []

		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'			)
						lowerCamelCase     :			List[Any]		=    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
		lowerCamelCase     :			Dict		=    []
		lowerCamelCase     :			Optional[Any]		=    []
		lowerCamelCase     :			Dict		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

		#  sorted information data for the size of the output data
		lowerCamelCase     :			Any		=    []
		# Data position feedback + parity
		lowerCamelCase     :			List[str]		=    []
		# Parity bit counter
		lowerCamelCase     :			List[str]		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    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'			)
						lowerCamelCase     :			Tuple		=    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
		lowerCamelCase     :			List[str]		=    0  # parity bit counter
		for bp in range(1,						size_par + 1			):
				# Bit counter one for a certain parity
				lowerCamelCase     :			List[Any]		=    0
				# Counter to control loop reading
				lowerCamelCase     :			List[Any]		=    0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase     :			Dict		=    (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase     :			List[Any]		=    '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
		lowerCamelCase     :			Tuple		=    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]			)

		lowerCamelCase     :			Union[str, Any]		=    parity_received == parity
		return data_output, ack






