




'''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 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A,							A,							A=1_024,							A=1_024,							A=3.6  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.bos_token_id
             SCREAMING_SNAKE_CASE   :      Tuple    					=						dataset
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						seq_length
             SCREAMING_SNAKE_CASE   :      Tuple    					=						seq_length * chars_per_token * num_of_sequences







       def __iter__(      self  ):







             '''simple docstring'''







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




def   lowercase__(			__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{'streaming': True}
      SCREAMING_SNAKE_CASE   :      Dict    					=						load_dataset(args.dataset_name      ,split='train'      ,**__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ConstantLengthDataset(__UpperCamelCase      ,__UpperCamelCase      ,seq_length=args.seq_length    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						DataLoader(__UpperCamelCase      ,batch_size=args.batch_size    )
      return eval_dataloader




def   lowercase__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






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

            if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                  break
      SCREAMING_SNAKE_CASE   :      str    					=						torch.mean(torch.cat(__UpperCamelCase    )    )
      try:
            SCREAMING_SNAKE_CASE   :      Any    					=						torch.exp(__UpperCamelCase    )
      except OverflowError:
            SCREAMING_SNAKE_CASE   :      List[str]    					=						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}""")








'''simple docstring'''



from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						9, 14  # noqa: F841
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[
          [0, 1, 4],
          [0, 7, 8],
          [1, 2, 8],
          [7, 8, 7],
          [7, 6, 1],
          [2, 8, 2],
          [8, 6, 6],
          [2, 3, 7],
          [2, 5, 4],
          [6, 5, 2],
          [3, 5, 14],
          [3, 4, 9],
          [5, 4, 10],
          [1, 7, 11],
      ]

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						defaultdict(__UpperCamelCase    )
      for nodea, nodea, cost in edges:
            adjancency[nodea].append([nodea, cost]    )
            adjancency[nodea].append([nodea, cost]    )

      SCREAMING_SNAKE_CASE   :      Dict    					=						mst(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[
          [7, 6, 1],
          [2, 8, 2],
          [6, 5, 2],
          [0, 1, 4],
          [2, 5, 4],
          [2, 3, 7],
          [0, 7, 8],
          [3, 4, 9],
      ]

      for answer in expected:
            SCREAMING_SNAKE_CASE   :      Any    					=						tuple(answer[:2]    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						tuple(edge[::-1]    )
            assert edge in result or reverse in result


