





'''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_  :					int		,       SCREAMING_SNAKE_CASE_  :					Any=82		,       SCREAMING_SNAKE_CASE_  :					int=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_  :					Union[str, Any]=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_  :					Union[str, Any]    ):
        _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_  :					Dict    ):
        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_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''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_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''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_  :					Optional[int]=82		,       SCREAMING_SNAKE_CASE_  :					Tuple=False		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]=False		,       SCREAMING_SNAKE_CASE_  :					Dict=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_  :					Any    ):



    '''simple docstring'''





    main()


if __name__ == "__main__":
       main()









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					"speech_to_text_2"
 __lowerCamelCase						:							Union[str, Any]         =					["past_key_values"]
 __lowerCamelCase						:							Tuple         =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}




 def __init__(	self			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=6			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=256			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=2			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1024			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      decoder_ffn_dim
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      decoder_attention_heads
     _lowerCAmelCase				=      dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      activation_function
     _lowerCAmelCase				=      init_std
     _lowerCAmelCase				=      decoder_layerdrop
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      scale_embedding  # scale factor will be sqrt(d_model) if True
     _lowerCAmelCase				=      max_target_positions

     super().__init__(
         pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     decoder_start_token_id=_lowerCAmelCase			,     **_lowerCAmelCase			,     )


