



'''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__						(    __lowercase				):
 def __init__(   self						:							int      ,     __a						:							List[Any]      ,     __a						:							Optional[Any]      ,     __a						:							Any=1024      ,     __a						:							Tuple=1024      ,     __a						:							int=3.6  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    tokenizer
       __snake_case :   Optional[Any]													=    tokenizer.bos_token_id
       __snake_case :   Any													=    dataset
       __snake_case :   int													=    seq_length
       __snake_case :   Tuple													=    seq_length * chars_per_token * num_of_sequences





 def __iter__(   self						:							Tuple  )      ->   List[Any]:







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





def   a_   (		_UpperCAmelCase    :      Optional[Any]				)    ->  Any:
      __snake_case :   int													=    {'''streaming''': True}
      __snake_case :   Optional[Any]													=    load_dataset(args.dataset_name ,split='train' ,**UpperCamelCase__				)
      __snake_case :   Any													=    ConstantLengthDataset(UpperCamelCase__ ,UpperCamelCase__ ,seq_length=args.seq_length				)
      __snake_case :   List[Any]													=    DataLoader(UpperCamelCase__ ,batch_size=args.batch_size				)
      return eval_dataloader





def   a_   (		_UpperCAmelCase    :      List[str]				)    ->  Optional[int]:
      model.eval()
      __snake_case :   Optional[Any]													=    []
      for step, batch in enumerate(UpperCamelCase__				):
            with torch.no_grad():
                  __snake_case :   Any													=    model(UpperCamelCase__ ,labels=UpperCamelCase__				)
            __snake_case :   Dict													=    outputs.loss.repeat(args.batch_size				)
            losses.append(accelerator.gather(UpperCamelCase__				)				)

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


# Setup Accelerator
A__    :   Optional[Any]           =					Accelerator()

# Parse configuration
A__    :   Union[str, Any]           =					HfArgumentParser(EvaluationArguments)
A__    :   str           =					parser.parse_args()
set_seed(args.seed)

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

# Load dataset and dataloader
A__    :   Any           =					create_dataloader(args)

# Prepare everything with our `accelerator`.
A__    :   Optional[Any]           =					accelerator.prepare(model, eval_dataloader)

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





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










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
							from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase							:       Tuple					 =	logging.get_logger(__name__)

lowerCAmelCase							:       List[Any]					 =	{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     '''deberta-v2'''






				def __init__(  self			, _lowerCAmelCase=128100			, _lowerCAmelCase=1536			, _lowerCAmelCase=24			, _lowerCAmelCase=24			, _lowerCAmelCase=6144			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-7			, _lowerCAmelCase=False			, _lowerCAmelCase=-1			, _lowerCAmelCase=0			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, _lowerCAmelCase="gelu"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				str           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				List[Any]           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Dict           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				str           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Any           =   relative_attention
										__SCREAMING_SNAKE_CASE:				Tuple           =   max_relative_positions
										__SCREAMING_SNAKE_CASE:				List[str]           =   pad_token_id
										__SCREAMING_SNAKE_CASE:				List[str]           =   position_biased_input

										# Backwards compatibility
										if type(_lowerCAmelCase					) == str:
																__SCREAMING_SNAKE_CASE:				str           =   [x.strip() for x in pos_att_type.lower().split('''|'''					)]

										__SCREAMING_SNAKE_CASE:				int           =   pos_att_type
										__SCREAMING_SNAKE_CASE:				List[Any]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   kwargs.get('''pooler_hidden_size'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   pooler_dropout
										__SCREAMING_SNAKE_CASE:				int           =   pooler_hidden_act





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''sequence'''}
										if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]					)
										else:
																return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 12






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = 3			, _lowerCAmelCase = 40			, _lowerCAmelCase = 40			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   super().generate_dummy_inputs(preprocessor=_lowerCAmelCase			, framework=_lowerCAmelCase					)
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
										return dummy_inputs



