





'''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					(   __magic_name__			):






       """simple docstring"""





       def __init__(							self       :		Optional[int]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Any=1024	,			lowerCAmelCase__       :		Any=1024	,			lowerCAmelCase__       :		str=3.6					)   ->					Optional[Any]:

            '''simple docstring'''




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







       def __iter__(							self       :		Tuple					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	iter(self.dataset					)
            _UpperCamelCase       =	True
            while more_examples:
                 _UpperCamelCase       =	[], 0
                 while True:
                      if buffer_len >= self.input_characters:
                           break
                      try:
                           buffer.append(next(lowerCAmelCase__					)['''content''']					)
                           buffer_len += len(buffer[-1]					)
                      except StopIteration:
                           _UpperCamelCase       =	False
                           break
                 _UpperCamelCase       =	tokenizer(lowerCAmelCase__	,			truncation=lowerCAmelCase__					)['''input_ids''']
                 _UpperCamelCase       =	[]
                 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					):
                      _UpperCamelCase       =	all_token_ids[i : i + self.seq_length]
                      if len(lowerCAmelCase__					) == self.seq_length:
                           yield torch.tensor(lowerCAmelCase__					)







def  a__    (    lowercase :					Dict				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	{'''streaming''': True}
     _UpperCamelCase       =	load_dataset(args.dataset_name, split='''train''', **UpperCamelCase__				)
     _UpperCamelCase       =	ConstantLengthDataset(UpperCamelCase__, UpperCamelCase__, seq_length=args.seq_length				)
     _UpperCamelCase       =	DataLoader(UpperCamelCase__, batch_size=args.batch_size				)
     return eval_dataloader







def  a__    (    lowercase :					Optional[Any]				)		->		Optional[Any]:





     """simple docstring"""

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

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


# Setup Accelerator
lowercase__						:      str	=    Accelerator()

# Parse configuration
lowercase__						:      List[Any]	=    HfArgumentParser(EvaluationArguments)
lowercase__						:      Any	=    parser.parse_args()
set_seed(args.seed)

# Logging
lowercase__						:      Union[str, Any]	=    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
lowercase__						:      Any	=    AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase__						:      List[str]	=    AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase__						:      Optional[Any]	=    create_dataloader(args)

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

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
lowercase__						:      List[Any]	=    evaluate(args)




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










'''simple docstring'''

def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     def update_area_of_max_square(lowercase :					int, lowercase :					int				) -> int:
          # BASE CASE
          if row >= rows or col >= cols:
               return 0

          _UpperCamelCase       =	update_area_of_max_square(lowercase, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, lowercase				)

          if mat[row][col]:
               _UpperCamelCase       =	1 + min([right, diagonal, down]				)
               _UpperCamelCase       =	max(largest_square_area[0], lowercase				)
               return sub_problem_sol
          else:
               return 0

     _UpperCamelCase       =	[0]
     update_area_of_max_square(0, 0				)
     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     def update_area_of_max_square_using_dp_array(
         lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				) -> int:
          if row >= rows or col >= cols:
               return 0
          if dp_array[row][col] != -1:
               return dp_array[row][col]

          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(lowercase, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, lowercase, lowercase				)

          if mat[row][col]:
               _UpperCamelCase       =	1 + min([right, diagonal, down]				)
               _UpperCamelCase       =	max(largest_square_area[0], lowercase				)
               _UpperCamelCase       =	sub_problem_sol
               return sub_problem_sol
          else:
               return 0

     _UpperCamelCase       =	[0]
     _UpperCamelCase       =	[[-1] * cols for _ in range(lowercase				)]
     update_area_of_max_square_using_dp_array(0, 0, lowercase				)

     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	[[0] * (cols + 1) for _ in range(rows + 1				)]
     _UpperCamelCase       =	0
     for row in range(rows - 1, -1, -1				):
          for col in range(cols - 1, -1, -1				):
               _UpperCamelCase       =	dp_array[row][col + 1]
               _UpperCamelCase       =	dp_array[row + 1][col + 1]
               _UpperCamelCase       =	dp_array[row + 1][col]

               if mat[row][col] == 1:
                    _UpperCamelCase       =	1 + min(lowercase, lowercase, lowercase				)
                    _UpperCamelCase       =	max(dp_array[row][col], lowercase				)
               else:
                    _UpperCamelCase       =	0

     return largest_square_area







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	[0] * (cols + 1)
     _UpperCamelCase       =	[0] * (cols + 1)
     _UpperCamelCase       =	0
     for row in range(rows - 1, -1, -1				):
          for col in range(cols - 1, -1, -1				):
               _UpperCamelCase       =	current_row[col + 1]
               _UpperCamelCase       =	next_row[col + 1]
               _UpperCamelCase       =	next_row[col]

               if mat[row][col] == 1:
                    _UpperCamelCase       =	1 + min(lowercase, lowercase, lowercase				)
                    _UpperCamelCase       =	max(current_row[col], lowercase				)
               else:
                    _UpperCamelCase       =	0
          _UpperCamelCase       =	current_row

     return largest_square_area


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))



