



import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


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


def 	_UpperCAmelCase       (   ):





					"""simple docstring"""

					_lowerCAmelCase      			=					argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""    )
					parser.add_argument("""--model_name"""			,		type=snake_case			,		default="""transfo-xl-wt103"""			,		help="""pretrained model name"""    )
					parser.add_argument(
					    """--split"""			,		type=snake_case			,		default="""test"""			,		choices=["""all""", """valid""", """test"""]			,		help="""which split to evaluate"""    )
					parser.add_argument("""--batch_size"""			,		type=snake_case			,		default=10			,		help="""batch size"""    )
					parser.add_argument("""--tgt_len"""			,		type=snake_case			,		default=1_28			,		help="""number of tokens to predict"""    )
					parser.add_argument("""--ext_len"""			,		type=snake_case			,		default=0			,		help="""length of the extended context"""    )
					parser.add_argument("""--mem_len"""			,		type=snake_case			,		default=16_00			,		help="""length of the retained previous heads"""    )
					parser.add_argument("""--clamp_len"""			,		type=snake_case			,		default=10_00			,		help="""max positional embedding index"""    )
					parser.add_argument("""--no_cuda"""			,		action="""store_true"""			,		help="""Do not use CUDA even though CUA is available"""    )
					parser.add_argument("""--work_dir"""			,		type=snake_case			,		required=snake_case			,		help="""path to the work_dir"""    )
					parser.add_argument("""--no_log"""			,		action="""store_true"""			,		help="""do not log the eval result"""    )
					parser.add_argument("""--same_length"""			,		action="""store_true"""			,		help="""set same length attention with masking"""    )
					parser.add_argument("""--server_ip"""			,		type=snake_case			,		default=""""""			,		help="""Can be used for distant debugging."""    )
					parser.add_argument("""--server_port"""			,		type=snake_case			,		default=""""""			,		help="""Can be used for distant debugging."""    )
					_lowerCAmelCase      			=					parser.parse_args()
					assert args.ext_len >= 0, "extended context length must be non-negative"

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print("""Waiting for debugger attach"""    )
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,		redirect_output=snake_case    )
										ptvsd.wait_for_attach()

					_lowerCAmelCase      			=					torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""    )
					logger.info("""device: {}""".format(snake_case    )    )

					# Load a pre-processed dataset
					# You can also build the corpus yourself using TransfoXLCorpus methods
					# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
					# and tokenizing the dataset
					# The pre-processed corpus is a convertion (using the conversion script )
					_lowerCAmelCase      			=					TransfoXLCorpus.from_pretrained(args.model_name    )

					_lowerCAmelCase      			=					corpus.get_iterator("""valid"""			,		args.batch_size			,		args.tgt_len			,		device=snake_case			,		ext_len=args.ext_len    )
					_lowerCAmelCase      			=					corpus.get_iterator("""test"""			,		args.batch_size			,		args.tgt_len			,		device=snake_case			,		ext_len=args.ext_len    )

					# Load a pre-trained model
					_lowerCAmelCase      			=					TransfoXLLMHeadModel.from_pretrained(args.model_name    )
					model.to(snake_case    )

					logger.info(
					    """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
					        args.batch_size			,		args.tgt_len			,		args.ext_len			,		args.mem_len			,		args.clamp_len    )    )

					model.reset_memory_length(args.mem_len    )
					if args.clamp_len > 0:
										_lowerCAmelCase      			=					args.clamp_len
					if args.same_length:
										_lowerCAmelCase      			=					True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(snake_case    ):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										_lowerCAmelCase			,			_lowerCAmelCase      			=					0, 0.0
										_lowerCAmelCase      			=					time.time()
										with torch.no_grad():
															_lowerCAmelCase      			=					None
															for idx, (data, target, seq_len) in enumerate(snake_case    ):
																				_lowerCAmelCase      			=					model(snake_case			,		lm_labels=snake_case			,		mems=snake_case    )
																				_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					ret
																				_lowerCAmelCase      			=					loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															_lowerCAmelCase      			=					time.time() - start_time
										logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(snake_case			,		10_00 * total_time / (idx + 1)    )    )
										return total_loss / total_len

					# Run on test data.
					if args.split == "all":
										_lowerCAmelCase      			=					evaluate(snake_case    )
										_lowerCAmelCase      			=					evaluate(snake_case    )
					elif args.split == "valid":
										_lowerCAmelCase      			=					evaluate(snake_case    )
										_lowerCAmelCase      			=					None
					elif args.split == "test":
										_lowerCAmelCase      			=					evaluate(snake_case    )
										_lowerCAmelCase      			=					None

					def format_log(snake_case			,		snake_case    ):
										_lowerCAmelCase      			=					"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(snake_case			,		snake_case			,		math.exp(snake_case    )    )
										return log_str

					_lowerCAmelCase      			=					""""""
					if valid_loss is not None:
										log_str += format_log(snake_case			,		"""valid"""    )
					if test_loss is not None:
										log_str += format_log(snake_case			,		"""test"""    )

					logger.info("""=""" * 1_00    )
					logger.info(snake_case    )
					logger.info("""=""" * 1_00    )


if __name__ == "__main__":
	main()








import cva
import numpy as np


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					try:
										return int(image[x_coordinate][y_coordinate] >= center    )
					except (IndexError, TypeError):
										return 0


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					image[x_coordinate][y_coordinate]
					_lowerCAmelCase      			=					[1, 2, 4, 8, 16, 32, 64, 1_28]

					# skip get_neighbors_pixel if center is null
					if center is None:
										return 0

					# Starting from the top right, assigning value to pixels clockwise
					_lowerCAmelCase      			=					[
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate - 1			,		snake_case    ),
					]

					# Converting the binary value to decimal.
					return sum(
					    binary_value * power for binary_value, power in zip(snake_case			,		snake_case    )    )


if __name__ == "__main__":
	# Reading the image and converting it to grayscale.
	A__			   = cva.imread(
	    """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
	)

	# Create a numpy array as the same height and width of read image
	A__			   = np.zeros((image.shape[0], image.shape[1]))

	# Iterating through the image and calculating the
	# local binary pattern value for each pixel.
	for i in range(0, image.shape[0]):
		for j in range(0, image.shape[1]):
			A__			   = local_binary_value(image, i, j)

	cva.imshow("""local binary pattern""", lbp_image)
	cva.waitKey(0)
	cva.destroyAllWindows()



