




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 (       _lowerCamelCase ):







			def __init__(       self    :			str    ,    snake_case__    :			Any    ,    snake_case__    :			int    ,    snake_case__    :			List[str]=1_024    ,    snake_case__    :			Any=1_024    ,    snake_case__    :			Dict=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    :			Dict      ):




									"""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(_UpperCamelCase      )["content"]      )
																											buffer_len += len(buffer[-1]      )
																					except StopIteration:
																											_UpperCAmelCase													=     False
																											break
															_UpperCAmelCase													=     tokenizer(_UpperCamelCase    ,    truncation=_UpperCamelCase      )["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(_UpperCamelCase      )    ,    self.seq_length      ):
																					_UpperCAmelCase													=     all_token_ids[i : i + self.seq_length]
																					if len(_UpperCamelCase      ) == self.seq_length:
																											yield torch.tensor(_UpperCamelCase      )




def 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=     {"streaming": True}
						_UpperCAmelCase													=     load_dataset(args.dataset_name	,    split="train"	,    **snake_case_						)
						_UpperCAmelCase													=     ConstantLengthDataset(snake_case_	,    snake_case_	,    seq_length=args.seq_length						)
						_UpperCAmelCase													=     DataLoader(snake_case_	,    batch_size=args.batch_size						)
						return eval_dataloader


def 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''simple docstring'''


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

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


# Setup Accelerator
lowercase_	:				Tuple  				= Accelerator()

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

# Logging
lowercase_	:				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
lowercase_	:				List[str]  				= AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase_	:				Optional[Any]  				= AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase_	:				Any  				= create_dataloader(args)

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

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





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











import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()





