


"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_lowerCAmelCase							:    Optional[int]					   =	logging.getLogger()







def        lowerCamelCase_(		)    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				List[Any]							=     argparse.ArgumentParser()
   parser.add_argument("-f"       )
   _lowerCamelCase   :				Dict							=     parser.parse_args()
   return args.f




class 							A_      (       _a ):


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     logging.StreamHandler(sys.stdout    )
     logger.addHandler(__lowerCAmelCase    )


  def 							_lowercase					(			self:						Optional[int]							,__lowerCAmelCase:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     get_gpu_count()

     if n_gpu > 1:
        pass
        # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
        # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
        # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
        # cmd = [sys.executable] + distributed_args + args
        # execute_subprocess_async(cmd, env=self.get_env())
        # XXX: test the results - need to save them first into .json file
     else:
        args.insert(0							,"run_glue_deebert.py"    )
        with patch.object(__lowerCAmelCase							,"argv"							,__lowerCAmelCase    ):
           _lowerCamelCase   :				Dict							=     run_glue_deebert.main()
           for value in result.values():
              self.assertGreaterEqual(__lowerCAmelCase							,0.6_66    )






  @slow
  @require_torch_non_multi_gpu
  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
     self.run_and_check(__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
     self.run_and_check(__lowerCAmelCase    )

     _lowerCamelCase   :				Optional[Any]							=     "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
     self.run_and_check(__lowerCAmelCase    )







'''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}""")




