




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase_						=			'true'






def  snake_case(    __magic_name__			,		__magic_name__=82			,		__magic_name__=16   )			->							Union[str, Any]:





     '''simple docstring'''


     set_seed(42   )
     lowercase :       Dict       =			RegressionModel()
     lowercase :       Dict       =			deepcopy(SCREAMING_SNAKE_CASE_   )
     lowercase :       Any       =			RegressionDataset(length=SCREAMING_SNAKE_CASE_   )
     lowercase :       Any       =			DataLoader(SCREAMING_SNAKE_CASE_			,		batch_size=SCREAMING_SNAKE_CASE_   )
     model.to(accelerator.device   )
     lowercase      ,	lowercase :       Dict       =			accelerator.prepare(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
     return model, ddp_model, dataloader






def  snake_case(    __magic_name__			,		__magic_name__=False   )			->							Union[str, Any]:





     '''simple docstring'''


     lowercase :       List[Any]       =			AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''   )
     lowercase :       Dict       =			load_dataset('''glue'''			,		'''mrpc'''			,		split='''validation'''   )

     def tokenize_function(__magic_name__   ):
          lowercase :       Dict       =			tokenizer(examples['''sentence1''']			,		examples['''sentence2''']			,		truncation=SCREAMING_SNAKE_CASE_			,		max_length=SCREAMING_SNAKE_CASE_   )
          return outputs

     with accelerator.main_process_first():
          lowercase :       Any       =			dataset.map(
              SCREAMING_SNAKE_CASE_			,		batched=SCREAMING_SNAKE_CASE_			,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,		)

     lowercase :       Optional[Any]       =			tokenized_datasets.rename_column('''label'''			,		'''labels'''   )

     def collate_fn(__magic_name__   ):
          if use_longest:
               return tokenizer.pad(SCREAMING_SNAKE_CASE_			,		padding='''longest'''			,		return_tensors='''pt'''   )
          return tokenizer.pad(SCREAMING_SNAKE_CASE_			,		padding='''max_length'''			,		max_length=1_28			,		return_tensors='''pt'''   )

     return DataLoader(SCREAMING_SNAKE_CASE_			,		shuffle=SCREAMING_SNAKE_CASE_			,		collate_fn=SCREAMING_SNAKE_CASE_			,		batch_size=16   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Dict:





     '''simple docstring'''


     lowercase :       Dict       =			Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_			,		split_batches=SCREAMING_SNAKE_CASE_   )
     lowercase :       Union[str, Any]       =			get_dataloader(SCREAMING_SNAKE_CASE_			,		not dispatch_batches   )
     lowercase :       List[str]       =			AutoModelForSequenceClassification.from_pretrained(
         '''hf-internal-testing/mrpc-bert-base-cased'''			,		return_dict=SCREAMING_SNAKE_CASE_   )
     lowercase      ,	lowercase :       int       =			accelerator.prepare(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
     return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Dict:





     '''simple docstring'''


     lowercase :       Optional[int]       =			[]
     for batch in dataloader:
          lowercase      ,	lowercase :       int       =			batch.values()
          with torch.no_grad():
               lowercase :       Dict       =			model(SCREAMING_SNAKE_CASE_   )
               lowercase      ,	lowercase :       Optional[int]       =			accelerator.gather_for_metrics((logit, target)   )
               logits_and_targets.append((logit, target)   )
     lowercase      ,	lowercase :       str       =			[], []
     for logit, targ in logits_and_targets:
          logits.append(SCREAMING_SNAKE_CASE_   )
          targs.append(SCREAMING_SNAKE_CASE_   )
     lowercase      ,	lowercase :       Any       =			torch.cat(SCREAMING_SNAKE_CASE_   ), torch.cat(SCREAMING_SNAKE_CASE_   )
     return logits, targs






def  snake_case(    __magic_name__			,		__magic_name__=82			,		__magic_name__=False			,		__magic_name__=False			,		__magic_name__=16   )			->							List[str]:





     '''simple docstring'''


     lowercase      ,	lowercase      ,	lowercase :       Any       =			get_basic_setup(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
     lowercase      ,	lowercase :       List[Any]       =			generate_predictions(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
     assert (
         len(SCREAMING_SNAKE_CASE_   ) == num_samples
     ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE_   )}"""






def  snake_case(    __magic_name__ = False			,		__magic_name__ = False   )			->							Optional[int]:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			evaluate.load('''glue'''			,		'''mrpc'''   )
     lowercase      ,	lowercase :       Dict       =			get_mrpc_setup(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
     # First do baseline
     lowercase      ,	lowercase      ,	lowercase :       int       =			setup['''no''']
     model.to(SCREAMING_SNAKE_CASE_   )
     model.eval()
     for batch in dataloader:
          batch.to(SCREAMING_SNAKE_CASE_   )
          with torch.inference_mode():
               lowercase :       Union[str, Any]       =			model(**SCREAMING_SNAKE_CASE_   )
          lowercase :       Tuple       =			outputs.logits.argmax(dim=-1   )
          metric.add_batch(predictions=SCREAMING_SNAKE_CASE_			,		references=batch['''labels''']   )
     lowercase :       Tuple       =			metric.compute()

     # Then do distributed
     lowercase      ,	lowercase      ,	lowercase :       str       =			setup['''ddp''']
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               lowercase :       Dict       =			model(**SCREAMING_SNAKE_CASE_   )
          lowercase :       Tuple       =			outputs.logits.argmax(dim=-1   )
          lowercase :       List[Any]       =			batch['''labels''']
          lowercase      ,	lowercase :       Any       =			accelerator.gather_for_metrics((preds, references)   )
          metric.add_batch(predictions=SCREAMING_SNAKE_CASE_			,		references=SCREAMING_SNAKE_CASE_   )
     lowercase :       List[str]       =			metric.compute()

     for key in "accuracy f1".split():
          assert math.isclose(
              baseline[key]			,		distributed[key]   ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""






def  snake_case(    )			->							Dict:





     '''simple docstring'''


     lowercase :       List[str]       =			Accelerator(split_batches=SCREAMING_SNAKE_CASE_			,		dispatch_batches=SCREAMING_SNAKE_CASE_   )
     if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_warning()
     else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
     # These are a bit slower so they should only be ran on the GPU or TPU
     if torch.cuda.is_available() or is_tpu_available():
          if accelerator.is_local_main_process:
               print('''**Testing gather_for_metrics**'''   )
          for split_batches in [True, False]:
               for dispatch_batches in [True, False]:
                    if accelerator.is_local_main_process:
                         print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""   )
                    test_mrpc(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )
                    accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print('''**Test torch metrics**'''   )
     for split_batches in [True, False]:
          for dispatch_batches in [True, False]:
               lowercase :       Dict       =			Accelerator(split_batches=SCREAMING_SNAKE_CASE_			,		dispatch_batches=SCREAMING_SNAKE_CASE_   )
               if accelerator.is_local_main_process:
                    print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""   )
               test_torch_metrics(SCREAMING_SNAKE_CASE_			,		99   )
               accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print('''**Test last batch is not dropped when perfectly divisible**'''   )
     lowercase :       Union[str, Any]       =			Accelerator()
     test_torch_metrics(SCREAMING_SNAKE_CASE_			,		5_12   )
     accelerator.state._reset_state()






def  snake_case(    __magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     main()


if __name__ == "__main__":
 main()





import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCAmelCase_						=			'\\n    Text data.\n    Second line of data.'

lowerCAmelCase_						=			'file'






@pytest.fixture(scope='''session'''   )
def  snake_case(    __magic_name__   )			->							List[str]:





     '''simple docstring'''


     lowercase :       Any       =			tmp_path_factory.mktemp('''data'''   ) / (FILE_PATH + '''.zstd''')
     lowercase :       List[Any]       =			bytes(__magic_name__			,		'''utf-8'''   )
     with zstd.open(__magic_name__			,		'''wb'''   ) as f:
          f.write(__magic_name__   )
     return path






@pytest.fixture
def  snake_case(    __magic_name__   )			->							List[Any]:





     '''simple docstring'''


     with open(os.path.join(tmpfs.local_root_dir			,		__magic_name__   )			,		'''w'''   ) as f:
          f.write(__magic_name__   )
     return FILE_PATH






@pytest.mark.parametrize('''compression_format'''			,		['''gzip''', '''xz''', '''zstd''']   )
def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[str]:





     '''simple docstring'''


     lowercase :       Optional[int]       =			{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
     lowercase :       int       =			input_paths[compression_format]
     lowercase :       Any       =			tmp_path / '''cache'''
     lowercase :       int       =			DownloadConfig(cache_dir=__magic_name__			,		extract_compressed_file=__magic_name__   )
     lowercase :       Optional[Any]       =			cached_path(__magic_name__			,		download_config=__magic_name__   )
     with open(__magic_name__   ) as f:
          lowercase :       Optional[int]       =			f.read()
     with open(__magic_name__   ) as f:
          lowercase :       str       =			f.read()
     assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''			,		[True, False]   )
@pytest.mark.parametrize('''default_cache_dir'''			,		[True, False]   )
def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[str]:





     '''simple docstring'''


     lowercase :       Dict       =			'''custom_cache'''
     lowercase :       Union[str, Any]       =			'''custom_extracted_dir'''
     lowercase :       str       =			tmp_path / '''custom_extracted_path'''
     if default_extracted:
          lowercase :       Tuple       =			('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
     else:
          monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''			,		__magic_name__   )
          monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''			,		str(__magic_name__   )   )
          lowercase :       int       =			custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     lowercase :       List[str]       =			xz_file
     lowercase :       Any       =			(
         DownloadConfig(extract_compressed_file=__magic_name__   )
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir			,		extract_compressed_file=__magic_name__   )
     )
     lowercase :       Optional[int]       =			cached_path(__magic_name__			,		download_config=__magic_name__   )
     assert Path(__magic_name__   ).parent.parts[-2:] == expected






def  snake_case(    __magic_name__   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       Any       =			str(Path(__magic_name__   ).resolve()   )
     assert cached_path(__magic_name__   ) == text_file
     # relative path
     lowercase :       Union[str, Any]       =			str(Path(__magic_name__   ).resolve().relative_to(Path(os.getcwd()   )   )   )
     assert cached_path(__magic_name__   ) == text_file






def  snake_case(    __magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     lowercase :       List[Any]       =			str(tmp_path.resolve() / '''__missing_file__.txt'''   )
     with pytest.raises(__magic_name__   ):
          cached_path(__magic_name__   )
     # relative path
     lowercase :       Optional[int]       =			'''./__missing_file__.txt'''
     with pytest.raises(__magic_name__   ):
          cached_path(__magic_name__   )






def  snake_case(    __magic_name__   )			->							List[str]:





     '''simple docstring'''


     lowercase :       List[str]       =			get_from_cache(F"""tmp://{tmpfs_file}"""   )
     with open(__magic_name__   ) as f:
          lowercase :       List[Any]       =			f.read()
     assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''			,		__magic_name__   )
def  snake_case(    )			->							List[Any]:





     '''simple docstring'''


     with pytest.raises(__magic_name__   ):
          cached_path('''https://huggingface.co'''   )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''			,		__magic_name__   )
def  snake_case(    __magic_name__   )			->							Tuple:





     '''simple docstring'''


     lowercase :       Any       =			tmp_path_factory.mktemp('''data'''   ) / '''file.html'''
     with pytest.raises(__magic_name__   ):
          http_get('''https://huggingface.co'''			,		temp_file=__magic_name__   )
     with pytest.raises(__magic_name__   ):
          http_head('''https://huggingface.co'''   )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''			,		__magic_name__   )
def  snake_case(    __magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			tmp_path_factory.mktemp('''data'''   ) / '''file.html'''
     with pytest.raises(__magic_name__   ):
          ftp_get('''ftp://huggingface.co'''			,		temp_file=__magic_name__   )
     with pytest.raises(__magic_name__   ):
          ftp_head('''ftp://huggingface.co'''   )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''			,		__magic_name__   )
def  snake_case(    __magic_name__   )			->							List[str]:





     '''simple docstring'''


     lowercase :       str       =			tmp_path_factory.mktemp('''data'''   ) / '''file.html'''
     with pytest.raises(__magic_name__   ):
          fsspec_get('''s3://huggingface.co'''			,		temp_file=__magic_name__   )
     with pytest.raises(__magic_name__   ):
          fsspec_head('''s3://huggingface.co'''   )