

'''simple docstring'''



import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def __init__(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=1_024   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=1_024   ,				__SCREAMING_SNAKE_CASE   :			int=3.6     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = tokenizer
      __SCREAMING_SNAKE_CASE      = tokenizer.bos_token_id
      __SCREAMING_SNAKE_CASE      = dataset
      __SCREAMING_SNAKE_CASE      = seq_length
      __SCREAMING_SNAKE_CASE      = seq_length * chars_per_token * num_of_sequences


 def __iter__(						self   :			List[Any]     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = iter(self.dataset     )
      __SCREAMING_SNAKE_CASE      = True
      while more_examples:
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = [], 0
           while True:
                if buffer_len >= self.input_characters:
                     break
                try:
                     buffer.append(next(__SCREAMING_SNAKE_CASE     )["""content"""]     )
                     buffer_len += len(buffer[-1]     )
                except StopIteration:
                     __SCREAMING_SNAKE_CASE      = False
                     break
           __SCREAMING_SNAKE_CASE      = tokenizer(__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE     )["""input_ids"""]
           __SCREAMING_SNAKE_CASE      = []
           for tokenized_input in tokenized_inputs:
                all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
           for i in range(0   ,				len(__SCREAMING_SNAKE_CASE     )   ,				self.seq_length     ):
                __SCREAMING_SNAKE_CASE      = all_token_ids[i : i + self.seq_length]
                if len(__SCREAMING_SNAKE_CASE     ) == self.seq_length:
                     yield torch.tensor(__SCREAMING_SNAKE_CASE     )







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {"""streaming""": True}
     __SCREAMING_SNAKE_CASE      = load_dataset(args.dataset_name ,       split="""train""" ,       **a__		)
     __SCREAMING_SNAKE_CASE      = ConstantLengthDataset(a__ ,       a__ ,       seq_length=args.seq_length		)
     __SCREAMING_SNAKE_CASE      = DataLoader(a__ ,       batch_size=args.batch_size		)
     return eval_dataloader







def   a__			(    a__		):




     """simple docstring"""


     model.eval()
     __SCREAMING_SNAKE_CASE      = []
     for step, batch in enumerate(a__		):
          with torch.no_grad():
               __SCREAMING_SNAKE_CASE      = model(a__ ,       labels=a__		)
          __SCREAMING_SNAKE_CASE      = outputs.loss.repeat(args.batch_size		)
          losses.append(accelerator.gather(a__		)		)

          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
               break
     __SCREAMING_SNAKE_CASE      = torch.mean(torch.cat(a__		)		)
     try:
          __SCREAMING_SNAKE_CASE      = torch.exp(a__		)
     except OverflowError:
          __SCREAMING_SNAKE_CASE      = float("""inf"""		)
     return loss.item(), perplexity.item()


# Setup Accelerator
UpperCAmelCase					:			Union[str, Any]       =	Accelerator()

# Parse configuration
UpperCAmelCase					:			Union[str, Any]       =	HfArgumentParser(EvaluationArguments)
UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()
set_seed(args.seed)

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

# Load model and tokenizer
UpperCAmelCase					:			List[str]       =	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCAmelCase					:			Union[str, Any]       =	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCAmelCase					:			Dict       =	create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCAmelCase  ,	UpperCAmelCase					:			Dict       =	accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase  ,	UpperCAmelCase					:			str       =	evaluate(args)



logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")









'''simple docstring'''



import math
import sys

import cva
import numpy as np







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = math.sqrt(a__		)
     __SCREAMING_SNAKE_CASE      = 1 / (sigma * math.sqrt(2 * math.pi		))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros((kernel_size, kernel_size)		)
     for i in range(0 ,       a__		):
          for j in range(0 ,       a__		):
               __SCREAMING_SNAKE_CASE      = math.sqrt(
                   abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
     return vec_gaussian(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros(img.shape		)
     __SCREAMING_SNAKE_CASE      = get_gauss_kernel(a__ ,       a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = img.shape
     for i in range(kernel_size // 2 ,       size_x - kernel_size // 2		):
          for j in range(kernel_size // 2 ,       size_y - kernel_size // 2		):
               __SCREAMING_SNAKE_CASE      = get_slice(a__ ,       a__ ,       a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = img_s - img_s[kernel_size // 2, kernel_size // 2]
               __SCREAMING_SNAKE_CASE      = vec_gaussian(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.sum(a__		) / np.sum(a__		)
               __SCREAMING_SNAKE_CASE      = val
     return imga







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = args[1] if args[1:] else """../image_data/lena.jpg"""
     __SCREAMING_SNAKE_CASE      = float(args[2]		) if args[2:] else 1.0
     __SCREAMING_SNAKE_CASE      = float(args[3]		) if args[3:] else 1.0
     if args[4:]:
          __SCREAMING_SNAKE_CASE      = int(args[4]		)
          __SCREAMING_SNAKE_CASE      = kernel_size + abs(kernel_size % 2 - 1		)
     else:
          __SCREAMING_SNAKE_CASE      = 5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase					:			Any       =	parse_args(sys.argv)
  UpperCAmelCase					:			Union[str, Any]       =	cva.imread(filename, 0)
  cva.imshow('input image', img)

  UpperCAmelCase					:			Optional[int]       =	img / 2_5_5
  UpperCAmelCase					:			Optional[Any]       =	out.astype('float32')
  UpperCAmelCase					:			Tuple       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  UpperCAmelCase					:			List[Any]       =	out * 2_5_5
  UpperCAmelCase					:			Any       =	np.uinta(out)
  cva.imshow('output image', out)
  cva.waitKey(0)
  cva.destroyAllWindows()






