


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_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	List[str]						,				a:	Dict						,				a:	Optional[Any]						,				a:	Any=1024						,				a:	str=1024						,				a:	Optional[Any]=3.6    ):
       __lowerCamelCase		:							List[str]		 =						tokenizer
       __lowerCamelCase		:							str		 =						tokenizer.bos_token_id
       __lowerCamelCase		:							List[Any]		 =						dataset
       __lowerCamelCase		:							str		 =						seq_length
       __lowerCamelCase		:							List[str]		 =						seq_length * chars_per_token * num_of_sequences




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



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Any		 =						{'streaming': True}
 __lowerCamelCase		:							Optional[int]		 =						load_dataset(args.dataset_name      ,	split='train'      ,	**SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Optional[int]		 =						ConstantLengthDataset(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	seq_length=args.seq_length						)
 __lowerCamelCase		:							Union[str, Any]		 =						DataLoader(SCREAMING_SNAKE_CASE__      ,	batch_size=args.batch_size						)
 return eval_dataloader
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 model.eval()
 __lowerCamelCase		:							Any		 =						[]
 for step, batch in enumerate(SCREAMING_SNAKE_CASE__						):
  with torch.no_grad():
   __lowerCamelCase		:							Optional[Any]		 =						model(SCREAMING_SNAKE_CASE__      ,	labels=SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Any		 =						outputs.loss.repeat(args.batch_size						)
  losses.append(accelerator.gather(SCREAMING_SNAKE_CASE__						)						)

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


# Setup Accelerator
lowercase_	   =     Accelerator()

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

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

# Load dataset and dataloader
lowercase_	   =     create_dataloader(args)

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

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



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





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """vision-encoder-decoder"""
      __snake_case 	=       True




      def __init__(    self:	Union[str, Any]						,				**a:	Optional[Any]    ):
       super().__init__(**a    )
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'A configuraton of type {self.model_type} cannot be instantiated because '
            F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'    )

       __lowerCamelCase		:							Dict		 =						kwargs.pop('encoder'    )
       __lowerCamelCase		:							int		 =						encoder_config.pop('model_type'    )
       __lowerCamelCase		:							Any		 =						kwargs.pop('decoder'    )
       __lowerCamelCase		:							Union[str, Any]		 =						decoder_config.pop('model_type'    )

       __lowerCamelCase		:							Optional[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							List[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						True




      @classmethod
      def 					_snake_case     (    cls:	Optional[Any]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				**a:	Dict    ):
       logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'    )
       __lowerCamelCase		:							Optional[Any]		 =						True
       __lowerCamelCase		:							Optional[Any]		 =						True

       return cls(encoder=encoder_config.to_dict()						,				decoder=decoder_config.to_dict()						,				**a    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							int		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Dict		 =						self.encoder.to_dict()
       __lowerCamelCase		:							Union[str, Any]		 =						self.decoder.to_dict()
       __lowerCamelCase		:							Optional[int]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       version.parse("""1.11"""   )




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return 1e-4




      @property
      def 					_snake_case     (    self:	List[str]    ):
       return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Union[str, Any]		 =						OrderedDict()
       __lowerCamelCase		:							Union[str, Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Optional[Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Dict		 =						{0: 'batch', 1: 'encoder_sequence'}

       return common_inputs




      def 					_snake_case     (    self:	List[Any]						,				a:	"PreTrainedTokenizerBase"						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional["TensorType"] = None						,				):
       import torch

       __lowerCamelCase		:							str		 =						OrderedDict()

       __lowerCamelCase		:							List[Any]		 =						super().generate_dummy_inputs(
           a						,				batch_size=a						,				seq_length=a						,				is_pair=a						,				framework=a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						dummy_input['input_ids'].shape
       __lowerCamelCase		:							str		 =						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowerCamelCase		:							str		 =						dummy_input.pop('input_ids'    )
       __lowerCamelCase		:							Union[str, Any]		 =						dummy_input.pop('attention_mask'    )
       __lowerCamelCase		:							str		 =						torch.zeros(a    )

       return common_inputs



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	List[Any]    ):
       pass




      def 					_snake_case     (    self:	List[str]						,				a:	PretrainedConfig    ):
       return VisionEncoderDecoderEncoderOnnxConfig(a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				a:	str = "default"    ):
       __lowerCamelCase		:							List[Any]		 =						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(a						,				a    )

