

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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=16      )							->      Optional[Any]:





    '''simple docstring'''






    set_seed(42      )
    A__          =   RegressionModel()
    A__          =   deepcopy(SCREAMING_SNAKE_CASE__      )
    A__          =   RegressionDataset(length=SCREAMING_SNAKE_CASE__      )
    A__          =   DataLoader(SCREAMING_SNAKE_CASE__    ,			batch_size=SCREAMING_SNAKE_CASE__      )
    model.to(accelerator.device      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return model, ddp_model, dataloader
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      int:





    '''simple docstring'''






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

    def tokenize_function(SCREAMING_SNAKE_CASE__			:	List[Any]      ):
        A__          =   tokenizer(examples['sentence1']    ,			examples['sentence2']    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__      )
        return outputs

    with accelerator.main_process_first():
        A__          =   dataset.map(
            SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

    A__          =   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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      str:





    '''simple docstring'''






    A__          =   Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__    ,			split_batches=SCREAMING_SNAKE_CASE__      )
    A__          =   get_dataloader(SCREAMING_SNAKE_CASE__    ,			not dispatch_batches      )
    A__          =   AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'    ,			return_dict=SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   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(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      List[str]:





    '''simple docstring'''






    A__          =   []
    for batch in dataloader:
        A__      ,						A__          =   batch.values()
        with torch.no_grad():
            A__          =   model(SCREAMING_SNAKE_CASE__      )
            A__      ,						A__          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    A__      ,						A__          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__      )
        targs.append(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   torch.cat(SCREAMING_SNAKE_CASE__      ), torch.cat(SCREAMING_SNAKE_CASE__      )
    return logits, targs
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	int=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False    ,			SCREAMING_SNAKE_CASE__			:	Any=False    ,			SCREAMING_SNAKE_CASE__			:	Tuple=16      )							->      List[Any]:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   get_basic_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   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(    SCREAMING_SNAKE_CASE__			:	bool = False    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      str:





    '''simple docstring'''






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

    # Then do distributed
    A__      ,						A__      ,						A__          =   setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        A__          =   batch['labels']
        A__      ,						A__          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=SCREAMING_SNAKE_CASE__      )
    A__          =   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(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   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]:
            A__          =   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**'      )
    A__          =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			512      )
    accelerator.state._reset_state()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

