






'''simple docstring'''
import numpy as np
from PIL import Image




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  np.array(_UpperCamelCase				)
   if arr.shape[0] != arr.shape[1]:
      raise ValueError("The input array is not a square matrix"				)
   lowercase_						:     int							     =  0
   lowercase_						:     str							     =  0
   lowercase_						:     Optional[Any]							     =  0
   lowercase_						:     Optional[int]							     =  0

   # compute the shape of the output matrix
   lowercase_						:     Dict							     =  (arr.shape[0] - size) // stride + 1
   # initialize the output matrix with zeros of shape maxpool_shape
   lowercase_						:     Dict							     =  np.zeros((maxpool_shape, maxpool_shape)				)

   while i < arr.shape[0]:
      if i + size > arr.shape[0]:
         # if the end of the matrix is reached, break
         break
      while j < arr.shape[1]:
         # if the end of the matrix is reached, break
         if j + size > arr.shape[1]:
            break
         # compute the maximum of the pooling matrix
         lowercase_						:     Optional[Any]							     =  np.max(arr[i : i + size, j : j + size]				)
         # shift the pooling matrix by stride of column pixels
         j += stride
         mat_j += 1

      # shift the pooling matrix by stride of row pixels
      i += stride
      mat_i += 1

      # reset the column index to 0
      lowercase_						:     int							     =  0
      lowercase_						:     Tuple							     =  0

   return updated_arr




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     List[Any]							     =  np.array(_UpperCamelCase				)
   if arr.shape[0] != arr.shape[1]:
      raise ValueError("The input array is not a square matrix"				)
   lowercase_						:     Any							     =  0
   lowercase_						:     Optional[int]							     =  0
   lowercase_						:     Union[str, Any]							     =  0
   lowercase_						:     Optional[Any]							     =  0

   # compute the shape of the output matrix
   lowercase_						:     Optional[Any]							     =  (arr.shape[0] - size) // stride + 1
   # initialize the output matrix with zeros of shape avgpool_shape
   lowercase_						:     Optional[Any]							     =  np.zeros((avgpool_shape, avgpool_shape)				)

   while i < arr.shape[0]:
      # if the end of the matrix is reached, break
      if i + size > arr.shape[0]:
         break
      while j < arr.shape[1]:
         # if the end of the matrix is reached, break
         if j + size > arr.shape[1]:
            break
         # compute the average of the pooling matrix
         lowercase_						:     Optional[Any]							     =  int(np.average(arr[i : i + size, j : j + size]				)				)
         # shift the pooling matrix by stride of column pixels
         j += stride
         mat_j += 1

      # shift the pooling matrix by stride of row pixels
      i += stride
      mat_i += 1
      # reset the column index to 0
      lowercase_						:     Optional[int]							     =  0
      lowercase_						:     Any							     =  0

   return updated_arr


# Main Function
if __name__ == "__main__":
 from doctest import testmod

 testmod(name='avgpooling', verbose=True)

 # Loading the image
 UpperCamelCase__			=      Image.open('path_to_image')

 # Converting the image to numpy array and maxpooling, displaying the result
 # Ensure that the image is a square matrix

 Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

 # Converting the image to numpy array and averagepooling, displaying the result
 # Ensure that the image is a square matrix

 Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()












'''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 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							int	,  a	:							Any=1_0_2_4	,  a	:							Tuple=1_0_2_4	,  a	:							Optional[int]=3.6      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  tokenizer
       lowercase_						:     Union[str, Any]							     =  tokenizer.bos_token_id
       lowercase_						:     Union[str, Any]							     =  dataset
       lowercase_						:     Optional[Any]							     =  seq_length
       lowercase_						:     Any							     =  seq_length * chars_per_token * num_of_sequences





    def __iter__(      self	:							int      ):






       '''simple docstring'''






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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {"streaming": True}
   lowercase_						:     List[Any]							     =  load_dataset(args.dataset_name    ,		split="train"    ,		**_UpperCamelCase				)
   lowercase_						:     int							     =  ConstantLengthDataset(_UpperCamelCase    ,		_UpperCamelCase    ,		seq_length=args.seq_length				)
   lowercase_						:     Dict							     =  DataLoader(_UpperCamelCase    ,		batch_size=args.batch_size				)
   return eval_dataloader




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   model.eval()
   lowercase_						:     Optional[Any]							     =  []
   for step, batch in enumerate(_UpperCamelCase				):
      with torch.no_grad():
         lowercase_						:     str							     =  model(_UpperCamelCase    ,		labels=_UpperCamelCase				)
      lowercase_						:     List[Any]							     =  outputs.loss.repeat(args.batch_size				)
      losses.append(accelerator.gather(_UpperCamelCase				)				)

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


# Setup Accelerator
UpperCamelCase__			=      Accelerator()

# Parse configuration
UpperCamelCase__			=      HfArgumentParser(EvaluationArguments)
UpperCamelCase__			=      parser.parse_args()
set_seed(args.seed)

# Logging
UpperCamelCase__			=      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__			=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCamelCase__			=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCamelCase__			=      create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCamelCase__,  UpperCamelCase__			=      accelerator.prepare(model, eval_dataloader)

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





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




