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


__lowercase							 =					'''true'''



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=82  ,			SCREAMING_SNAKE_CASE=16	):
    '''simple docstring'''







    set_seed(42	)
    __UpperCamelCase     :List[str]			  =		RegressionModel()
    __UpperCamelCase     :List[Any]			  =		deepcopy(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[int]			  =		RegressionDataset(length=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		DataLoader(SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    model.to(accelerator.device	)
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		accelerator.prepare(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    return model, ddp_model, dataloader



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''	)
    __UpperCamelCase     :Optional[int]			  =		load_dataset('''glue'''  ,			'''mrpc'''  ,			split='''validation'''	)

    def tokenize_function(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=SCREAMING_SNAKE_CASE  ,			max_length=SCREAMING_SNAKE_CASE	)
        return outputs

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

    __UpperCamelCase     :Union[str, Any]			  =		tokenized_datasets.rename_column('''label'''  ,			'''labels'''	)

    def collate_fn(SCREAMING_SNAKE_CASE	):
        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=128  ,			return_tensors='''pt'''	)

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		[]
    for batch in dataloader:
        __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		batch.values()
        with torch.no_grad():
            __UpperCamelCase     :Any			  =		model(SCREAMING_SNAKE_CASE	)
            __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		accelerator.gather_for_metrics((logit, target)	)
            logits_and_targets.append((logit, target)	)
    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		[], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE	)
        targs.append(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		torch.cat(SCREAMING_SNAKE_CASE	), torch.cat(SCREAMING_SNAKE_CASE	)
    return logits, targs



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=82  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE=16	):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		get_basic_setup(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :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 lowerCamelCase  (   SCREAMING_SNAKE_CASE = False  ,			SCREAMING_SNAKE_CASE = False	):
    '''simple docstring'''







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

    # Then do distributed
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		setup['''ddp''']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            __UpperCamelCase     :Optional[Any]			  =		model(**SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Union[str, Any]			  =		outputs.logits.argmax(dim=-1	)
        __UpperCamelCase     :Optional[Any]			  =		batch['''labels''']
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		accelerator.gather_for_metrics((preds, references)	)
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE  ,			references=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		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 lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :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]:
            __UpperCamelCase     :Union[str, Any]			  =		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**'''	)
    __UpperCamelCase     :Union[str, Any]			  =		Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE  ,			512	)
    accelerator.state._reset_state()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    main()


if __name__ == "__main__":
    main()













import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__UpperCAmelCase								=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
		from .safilesystem import SaFileSystem  # noqa: F401

__UpperCAmelCase								=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
		if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
				warnings.warn(F'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
		fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					if "://" in dataset_path:
										UpperCAmelCase_    :      int            =						dataset_path.split('://'  )[1]
					return dataset_path




def      lowercase__      (     __snake_case    :							fsspec.AbstractFileSystem  ):


					'''simple docstring'''


					if fs is not None and fs.protocol != "file":
										return True
					else:
										return False




def      lowercase__      (     __snake_case    :							fsspec.AbstractFileSystem     ,       __snake_case    :							str     ,       __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						not is_remote_filesystem(__snake_case  )
					if is_local:
										# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
										shutil.move(fs._strip_protocol(__snake_case  )     ,       fs._strip_protocol(__snake_case  )  )
					else:
										fs.mv(__snake_case     ,       __snake_case     ,       recursive=__snake_case  )






def      lowercase__      (     ):


					'''simple docstring'''


					if hasattr(fsspec.asyn     ,       'reset_lock'  ):
										# for future fsspec>2022.05.0
										fsspec.asyn.reset_lock()
					else:
										UpperCAmelCase_    :      Optional[Any]            =						None
										UpperCAmelCase_    :      Union[str, Any]            =						None
										UpperCAmelCase_    :      int            =						threading.Lock()






