
'''simple docstring'''



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
)
UpperCamelCase__ :						Tuple    							=					logging.getLogger(__name__)
def 				lowerCAmelCase_		(			)       ->       Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""  )
   parser.add_argument("""--model_name"""      , type=SCREAMING_SNAKE_CASE_      , default="""transfo-xl-wt103"""      , help="""pretrained model name"""  )
   parser.add_argument(
       """--split"""      , type=SCREAMING_SNAKE_CASE_      , default="""test"""      , choices=["""all""", """valid""", """test"""]      , help="""which split to evaluate"""  )
   parser.add_argument("""--batch_size"""      , type=SCREAMING_SNAKE_CASE_      , default=10      , help="""batch size"""  )
   parser.add_argument("""--tgt_len"""      , type=SCREAMING_SNAKE_CASE_      , default=1_28      , help="""number of tokens to predict"""  )
   parser.add_argument("""--ext_len"""      , type=SCREAMING_SNAKE_CASE_      , default=0      , help="""length of the extended context"""  )
   parser.add_argument("""--mem_len"""      , type=SCREAMING_SNAKE_CASE_      , default=16_00      , help="""length of the retained previous heads"""  )
   parser.add_argument("""--clamp_len"""      , type=SCREAMING_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=SCREAMING_SNAKE_CASE_      , required=SCREAMING_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=SCREAMING_SNAKE_CASE_      , default=""""""      , help="""Can be used for distant debugging."""  )
   parser.add_argument("""--server_port"""      , type=SCREAMING_SNAKE_CASE_      , default=""""""      , help="""Can be used for distant debugging."""  )
   _SCREAMING_SNAKE_CASE       	=			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=SCREAMING_SNAKE_CASE_  )
      ptvsd.wait_for_attach()

   _SCREAMING_SNAKE_CASE       	=			torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""  )
   logger.info("""device: {}""".format(SCREAMING_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 )
   _SCREAMING_SNAKE_CASE       	=			TransfoXLCorpus.from_pretrained(args.model_name  )

   _SCREAMING_SNAKE_CASE       	=			corpus.get_iterator("""valid"""      , args.batch_size      , args.tgt_len      , device=SCREAMING_SNAKE_CASE_      , ext_len=args.ext_len  )
   _SCREAMING_SNAKE_CASE       	=			corpus.get_iterator("""test"""      , args.batch_size      , args.tgt_len      , device=SCREAMING_SNAKE_CASE_      , ext_len=args.ext_len  )

   # Load a pre-trained model
   _SCREAMING_SNAKE_CASE       	=			TransfoXLLMHeadModel.from_pretrained(args.model_name  )
   model.to(SCREAMING_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:
      _SCREAMING_SNAKE_CASE       	=			args.clamp_len
   if args.same_length:
      _SCREAMING_SNAKE_CASE       	=			True

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

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

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

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

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


if __name__ == "__main__":
     main()








'''simple docstring'''



from cva import destroyAllWindows, imread, imshow, waitKey
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			img.shape[0], img.shape[1]

   def to_grayscale(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      return 0.2126 * red + 0.587 * green + 0.114 * blue

   def normalize(SCREAMING_SNAKE_CASE_  ):
      return min(SCREAMING_SNAKE_CASE_      , 2_55  )

   for i in range(SCREAMING_SNAKE_CASE_  ):
      for j in range(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			int(to_grayscale(*img[i][j]  )  )
         _SCREAMING_SNAKE_CASE       	=			[
             normalize(SCREAMING_SNAKE_CASE_  ),
             normalize(greyscale + factor  ),
             normalize(greyscale + 2 * factor  ),
         ]

   return img


if __name__ == "__main__":
     # read original image
     UpperCamelCase__ :						Optional[int]    							=					{
         percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
     }

     for percentage, img in images.items():
          make_sepia(img, percentage)

     for percentage, img in images.items():
          imshow(f"""Original image with sepia (factor: {percentage})""", img)

     waitKey(0)
     destroyAllWindows()






