





"""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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=1024					,						__lowerCamelCase=1024					,						__lowerCamelCase=3.6  ):



     '''simple docstring'''




     __A    :						int						 =     tokenizer
     __A    :						int						 =     tokenizer.bos_token_id
     __A    :						Any						 =     dataset
     __A    :						List[Any]						 =     seq_length
     __A    :						Optional[Any]						 =     seq_length * chars_per_token * num_of_sequences







 def __iter__(    self  ):



     '''simple docstring'''




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







def 		__lowercase     (   snake_case_  :  List[str]						)   ->Any:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  List[str]						)   ->Optional[int]:



    '''simple docstring'''







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

        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    __A    :						Tuple						 =     torch.mean(torch.cat(snake_case_						)						)
    try:
        __A    :						Tuple						 =     torch.exp(snake_case_						)
    except OverflowError:
        __A    :						List[str]						 =     float('''inf'''						)
    return loss.item(), perplexity.item()


# Setup Accelerator
a_ =			Accelerator()

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

# Logging
a_ =			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_ =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_ =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_ =			create_dataloader(args)

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

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




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











"""simple docstring"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




