'''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       (_UpperCamelCase   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=10_24				,     UpperCamelCase_=10_24				,     UpperCamelCase_=3.6      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer
              __UpperCAmelCase  :				str       				=		tokenizer.bos_token_id
              __UpperCAmelCase  :				List[Any]       				=		dataset
              __UpperCAmelCase  :				Union[str, Any]       				=		seq_length
              __UpperCAmelCase  :				List[str]       				=		seq_length * chars_per_token * num_of_sequences







       def __iter__(      self      ):
              __UpperCAmelCase  :				int       				=		iter(self.dataset      )
              __UpperCAmelCase  :				List[Any]       				=		True
              while more_examples:
                     __UpperCAmelCase  :				Tuple       				=		[], 0
                     while True:
                            if buffer_len >= self.input_characters:
                                   break
                            try:
                                   buffer.append(next(_UpperCAmelCase      )["content"]      )
                                   buffer_len += len(buffer[-1]      )
                            except StopIteration:
                                   __UpperCAmelCase  :				Union[str, Any]       				=		False
                                   break
                     __UpperCAmelCase  :				Any       				=		tokenizer(_UpperCAmelCase				,     truncation=_UpperCAmelCase      )['''input_ids''']
                     __UpperCAmelCase  :				Dict       				=		[]
                     for tokenized_input in tokenized_inputs:
                            all_token_ids.extend(tokenized_input + [self.concat_token_id]      )
                     for i in range(0				,     len(_UpperCAmelCase      )				,     self.seq_length      ):
                            __UpperCAmelCase  :				Union[str, Any]       				=		all_token_ids[i : i + self.seq_length]
                            if len(_UpperCAmelCase      ) == self.seq_length:
                                   yield torch.tensor(_UpperCAmelCase      )





def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		{'''streaming''': True}
       __UpperCAmelCase  :				int       				=		load_dataset(args.dataset_name							,    split="train"							,    **a_						)
       __UpperCAmelCase  :				int       				=		ConstantLengthDataset(a_							,    a_							,    seq_length=args.seq_length						)
       __UpperCAmelCase  :				Optional[int]       				=		DataLoader(a_							,    batch_size=args.batch_size						)
       return eval_dataloader

def _lowercase	(      lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


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

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       __UpperCAmelCase  :				Union[str, Any]       				=		torch.mean(torch.cat(a_						)						)
       try:
              __UpperCAmelCase  :				Tuple       				=		torch.exp(a_						)
       except OverflowError:
              __UpperCAmelCase  :				Dict       				=		float("inf"						)
       return loss.item(), perplexity.item()


# Setup Accelerator
_a   :	Any 							=   Accelerator()

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

# Logging
_a   :	List[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   :	Tuple 							=   AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_a   :	Dict 							=   AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_a   :	Tuple 							=   create_dataloader(args)

# Prepare everything with our `accelerator`.
_a							,					_a   :	Tuple 							=   accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
_a							,					_a   :	List[str] 							=   evaluate(args)


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







'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	List[Any] 							=   {
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class 		__A       (__magic_name__   ):
       snake_case       :Any				    =					"cvt"






       def __init__(      self				,     UpperCamelCase_=3				,     UpperCamelCase_=[7, 3, 3]				,     UpperCamelCase_=[4, 2, 2]				,     UpperCamelCase_=[2, 1, 1]				,     UpperCamelCase_=[64, 1_92, 3_84]				,     UpperCamelCase_=[1, 3, 6]				,     UpperCamelCase_=[1, 2, 10]				,     UpperCamelCase_=[4.0, 4.0, 4.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.1]				,     UpperCamelCase_=[True, True, True]				,     UpperCamelCase_=[False, False, True]				,     UpperCamelCase_=["dw_bn", "dw_bn", "dw_bn"]				,     UpperCamelCase_=[3, 3, 3]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[2, 2, 2]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		num_channels
              __UpperCAmelCase  :				Optional[Any]       				=		patch_sizes
              __UpperCAmelCase  :				List[str]       				=		patch_stride
              __UpperCAmelCase  :				Tuple       				=		patch_padding
              __UpperCAmelCase  :				int       				=		embed_dim
              __UpperCAmelCase  :				str       				=		num_heads
              __UpperCAmelCase  :				Any       				=		depth
              __UpperCAmelCase  :				List[str]       				=		mlp_ratio
              __UpperCAmelCase  :				List[str]       				=		attention_drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_path_rate
              __UpperCAmelCase  :				str       				=		qkv_bias
              __UpperCAmelCase  :				Optional[int]       				=		cls_token
              __UpperCAmelCase  :				Optional[Any]       				=		qkv_projection_method
              __UpperCAmelCase  :				Tuple       				=		kernel_qkv
              __UpperCAmelCase  :				Optional[Any]       				=		padding_kv
              __UpperCAmelCase  :				Optional[int]       				=		stride_kv
              __UpperCAmelCase  :				Any       				=		padding_q
              __UpperCAmelCase  :				List[Any]       				=		stride_q
              __UpperCAmelCase  :				Union[str, Any]       				=		initializer_range
              __UpperCAmelCase  :				Any       				=		layer_norm_eps






