
"""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       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_    ,				snake_case_=1_024    ,				snake_case_=1_024    ,				snake_case_=3.6				)       ->  int:
													__lowerCAmelCase     =  tokenizer
													__lowerCAmelCase     =  tokenizer.bos_token_id
													__lowerCAmelCase     =  dataset
													__lowerCAmelCase     =  seq_length
													__lowerCAmelCase     =  seq_length * chars_per_token * num_of_sequences




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

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  {"""streaming""": True}
						__lowerCAmelCase     =  load_dataset(args.dataset_name ,    split="""train""" ,    **_lowerCAmelCase						)
						__lowerCAmelCase     =  ConstantLengthDataset(_lowerCAmelCase ,    _lowerCAmelCase ,    seq_length=args.seq_length						)
						__lowerCAmelCase     =  DataLoader(_lowerCAmelCase ,    batch_size=args.batch_size						)
						return eval_dataloader

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

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


# Setup Accelerator
SCREAMING_SNAKE_CASE_					     =	Accelerator()

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

# Logging
SCREAMING_SNAKE_CASE_					     =	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
SCREAMING_SNAKE_CASE_					     =	AutoModelForCausalLM.from_pretrained(args.model_ckpt)
SCREAMING_SNAKE_CASE_					     =	AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE_					     =	create_dataloader(args)

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

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



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


"""simple docstring"""
from typing import List

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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps
