




'''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		:			str     ,       lowercase		:			str     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]=1_024     ,       lowercase		:			Optional[Any]=1_024     ,       lowercase		:			Union[str, Any]=3.6       ):



           '''simple docstring'''

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







       def __iter__(			self		:			Tuple       ):



           '''simple docstring'''

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






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





def 		snake_case_							(_a			:							int  ):
    model.eval()
    UpperCAmelCase     =	[]
    for step, batch in enumerate(_a  ):
        with torch.no_grad():
            UpperCAmelCase     =	model(_a					,     labels=_a  )
        UpperCAmelCase     =	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     =	torch.mean(torch.cat(_a  )  )
    try:
        UpperCAmelCase     =	torch.exp(_a  )
    except OverflowError:
        UpperCAmelCase     =	float('''inf'''  )
    return loss.item(), perplexity.item()


# Setup Accelerator
A									=Accelerator()

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

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

# Load dataset and dataloader
A									=create_dataloader(args)

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

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







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










'''simple docstring'''







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


A									=logging.get_logger(__name__)

A									={
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}


class     _a						(			__a				):
       __a       :  List[Any]            =      """mgp-str"""
       def __init__(			self		:			str     ,       lowercase		:			str=[32, 128]     ,       lowercase		:			Optional[Any]=4     ,       lowercase		:			Optional[Any]=3     ,       lowercase		:			Dict=27     ,       lowercase		:			Any=38     ,       lowercase		:			int=50_257     ,       lowercase		:			List[str]=30_522     ,       lowercase		:			Optional[int]=768     ,       lowercase		:			List[Any]=12     ,       lowercase		:			Tuple=12     ,       lowercase		:			Optional[int]=4.0     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			str=False     ,       lowercase		:			str=1E-5     ,       lowercase		:			Dict=0.0     ,       lowercase		:			Dict=0.0     ,       lowercase		:			Tuple=0.0     ,       lowercase		:			str=False     ,       lowercase		:			Optional[Any]=0.02     ,       **lowercase		:			List[str]     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )

           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	patch_size
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	max_token_length
           UpperCAmelCase     =	num_character_labels
           UpperCAmelCase     =	num_bpe_labels
           UpperCAmelCase     =	num_wordpiece_labels
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	mlp_ratio
           UpperCAmelCase     =	distilled
           UpperCAmelCase     =	layer_norm_eps
           UpperCAmelCase     =	drop_rate
           UpperCAmelCase     =	qkv_bias
           UpperCAmelCase     =	attn_drop_rate
           UpperCAmelCase     =	drop_path_rate
           UpperCAmelCase     =	output_aa_attentions
           UpperCAmelCase     =	initializer_range




