






'''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 _lowercase     (    A__		):




       def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Any=1_024      ,							UpperCamelCase__:    Tuple=3.6					):
              lowerCamelCase__ :     Optional[int]	  =	tokenizer
              lowerCamelCase__ :     Union[str, Any]	  =	tokenizer.bos_token_id
              lowerCamelCase__ :     Optional[Any]	  =	dataset
              lowerCamelCase__ :     Union[str, Any]	  =	seq_length
              lowerCamelCase__ :     Tuple	  =	seq_length * chars_per_token * num_of_sequences




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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[str]:
       lowerCamelCase__ :     Tuple	  =	{"""streaming""": True}
       lowerCamelCase__ :     Any	  =	load_dataset(args.dataset_name    ,      split="""train"""    ,      **lowercase_				)
       lowerCamelCase__ :     Dict	  =	ConstantLengthDataset(lowercase_    ,      lowercase_    ,      seq_length=args.seq_length				)
       lowerCamelCase__ :     Union[str, Any]	  =	DataLoader(lowercase_    ,      batch_size=args.batch_size				)
       return eval_dataloader





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
       model.eval()
       lowerCamelCase__ :     List[str]	  =	[]
       for step, batch in enumerate(lowercase_				):
              with torch.no_grad():
                     lowerCamelCase__ :     int	  =	model(lowercase_    ,      labels=lowercase_				)
              lowerCamelCase__ :     List[str]	  =	outputs.loss.repeat(args.batch_size				)
              losses.append(accelerator.gather(lowercase_				)				)

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       lowerCamelCase__ :     Optional[int]	  =	torch.mean(torch.cat(lowercase_				)				)
       try:
              lowerCamelCase__ :     Any	  =	torch.exp(lowercase_				)
       except OverflowError:
              lowerCamelCase__ :     Optional[int]	  =	float("""inf"""				)
       return loss.item(), perplexity.item()


# Setup Accelerator
_A     :      Optional[Any]   					=Accelerator()

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

# Logging
_A     :      Dict   					=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
_A     :      Optional[Any]   					=AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_A     :      Union[str, Any]   					=AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_A     :      int   					=create_dataloader(args)

# Prepare everything with our `accelerator`.
_A     :      Union[str, Any]   					=accelerator.prepare(model, eval_dataloader)

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



logger.info(F'loss/eval: {eval_loss}, perplexity: {perplexity}')









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(UpperCamelCase    ,      110				)				).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""				)





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	lbp.get_neighbors_pixel(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							lowerCamelCase__ :     List[str]	  =	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]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

