




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )







'''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   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


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



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


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

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				Any       							=		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
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

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

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

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

