
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,
)
UpperCAmelCase     				=			logging.getLogger(__name__)






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






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

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

	parser.add_argument('--seed'			,							type=_lowerCamelCase			,							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'			,							)
	lowercase					  =   parser.parse_args()

	lowercase					  =   torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
	lowercase					  =   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(_lowerCamelCase							)

	# Initialize the model and tokenizer
	lowercase					  =   AutoTokenizer.from_pretrained(args.model_name_or_path							)
	lowercase					  =   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(_lowerCamelCase							)
	lowercase					  =   args.prompt if args.prompt else input('Model prompt >>> '							)

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

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

	lowercase					  =   []
	for generated_sequence_idx, generated_sequence in enumerate(_lowerCamelCase							):
		print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''							)
		lowercase					  =   generated_sequence.tolist()

		# Decode text
		lowercase					  =   tokenizer.decode(_lowerCamelCase			,							clean_up_tokenization_spaces=_lowerCamelCase			,							add_special_tokens=_lowerCamelCase							)

		# Remove all text after the stop token
		lowercase					  =   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
		lowercase					  =   (
		    prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]			,							clean_up_tokenization_spaces=_lowerCamelCase							)							) :]
		)

		generated_sequences.append(_lowerCamelCase							)
		print(_lowerCamelCase							)

	return generated_sequences


if __name__ == "__main__":
							main()



def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   [701, 301, 132, 57, 23, 10, 4, 1]
	for gap in gaps:
		for i in range(__SCREAMING_SNAKE_CASE			,							len(__SCREAMING_SNAKE_CASE							)							):
			lowercase					  =   collection[i]
			lowercase					  =   i
			while j >= gap and collection[j - gap] > insert_value:
				lowercase					  =   collection[j - gap]
				j -= gap
			if j != i:
				lowercase					  =   insert_value
	return collection


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	UpperCAmelCase     				=			input('''Enter numbers separated by a comma:\n''').strip()
	UpperCAmelCase     				=			[int(item) for item in user_input.split(''',''')]
	print(shell_sort(unsorted))

