





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				"true"


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       str=82		,							SCREAMING_SNAKE_CASE_	:       Dict=16     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       Accelerator		,							SCREAMING_SNAKE_CASE_	:       int=False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"     )
    _lowerCAmelCase						      =   load_dataset("glue"		,							"mrpc"		,							split="validation"     )

    def tokenize_function(SCREAMING_SNAKE_CASE_	:       List[str]     ):
        _lowerCAmelCase						      =   tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=SCREAMING_SNAKE_CASE_		,							max_length=SCREAMING_SNAKE_CASE_     )
        return outputs

    with accelerator.main_process_first():
        _lowerCAmelCase						      =   dataset.map(
            SCREAMING_SNAKE_CASE_		,							batched=SCREAMING_SNAKE_CASE_		,							remove_columns=["idx", "sentence1", "sentence2"]		,							)

    _lowerCAmelCase						      =   tokenized_datasets.rename_column("label"		,							"labels"     )

    def collate_fn(SCREAMING_SNAKE_CASE_	:       str     ):
        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 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       Accelerator		,							SCREAMING_SNAKE_CASE_	:       List[Any]=82		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=False		,							SCREAMING_SNAKE_CASE_	:       List[Any]=False		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=16     ):



    '''simple docstring'''





    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   get_basic_setup(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   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 						__a(SCREAMING_SNAKE_CASE_	:       bool = False		,							SCREAMING_SNAKE_CASE_	:       bool = False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   evaluate.load("glue"		,							"mrpc"     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   get_mrpc_setup(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    # First do baseline
    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   setup["no"]
    model.to(SCREAMING_SNAKE_CASE_     )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE_     )
        with torch.inference_mode():
            _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   outputs.logits.argmax(dim=-1     )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE_		,							references=batch["labels"]     )
    _lowerCAmelCase						      =   metric.compute()

    # Then do distributed
    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   setup["ddp"]
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            _lowerCAmelCase						      =   model(**SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   outputs.logits.argmax(dim=-1     )
        _lowerCAmelCase						      =   batch["labels"]
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.gather_for_metrics((preds, references)     )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE_		,							references=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   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 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   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]:
            _lowerCAmelCase						      =   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**"     )
    _lowerCAmelCase						      =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE_		,							512     )
    accelerator.state._reset_state()


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]     ):



    '''simple docstring'''





    main()


if __name__ == "__main__":
       main()





"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							__UpperCamelCase    : Any				 =						None

__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)
__UpperCamelCase    : List[Any]				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : str				 =						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__UpperCamelCase    : Any				 =						{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase    : Union[str, Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			AlbertTokenizer







					def __init__(			self     :							Union[str, Any]  ,lowercase_     :							List[str]=None  ,lowercase_     :							Union[str, Any]=None  ,lowercase_     :							str=True  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=False  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							Optional[int]="[SEP]"  ,lowercase_     :							Optional[Any]="<unk>"  ,lowercase_     :							List[Any]="[SEP]"  ,lowercase_     :							Optional[int]="<pad>"  ,lowercase_     :							List[Any]="[CLS]"  ,lowercase_     :							Optional[int]="[MASK]"  ,**lowercase_     :							Optional[int]  ,):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,remove_space=lowercase_  ,keep_accents=lowercase_  ,bos_token=lowercase_  ,eos_token=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		List[Any]    							=      do_lower_case
						lowerCAmelCase__      :		str    							=      remove_space
						lowerCAmelCase__      :		Optional[int]    							=      keep_accents
						lowerCAmelCase__      :		Dict    							=      vocab_file
						lowerCAmelCase__      :		Optional[int]    							=      False if not self.vocab_file else True







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[Any]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [self.cls_token_id]
						if token_ids_a is None:
							return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[str]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		List[Any]    							=      [self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''						)

						if not os.path.isdir(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						):
							copyfile(self.vocab_file  ,lowercase_						)

						return (out_vocab_file,)




