





'''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


UpperCAmelCase					:		Dict		=							'true'




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=82			,      lowerCAmelCase_=16	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=False	):





      """simple docstring"""



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

      def tokenize_function(lowerCAmelCase_	):
            _snake_case		:	str					     =     tokenizer(examples['''sentence1''']			,      examples['''sentence2''']			,      truncation=lowerCAmelCase_			,      max_length=lowerCAmelCase_	)
            return outputs

      with accelerator.main_process_first():
            _snake_case		:	int					     =     dataset.map(
                lowerCAmelCase_			,      batched=lowerCAmelCase_			,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,      )

      _snake_case		:	Any					     =     tokenized_datasets.rename_column('''label'''			,      '''labels'''	)

      def collate_fn(lowerCAmelCase_	):
            if use_longest:
                  return tokenizer.pad(lowerCAmelCase_			,      padding='''longest'''			,      return_tensors='''pt'''	)
            return tokenizer.pad(lowerCAmelCase_			,      padding='''max_length'''			,      max_length=128			,      return_tensors='''pt'''	)

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=82			,      lowerCAmelCase_=False			,      lowerCAmelCase_=False			,      lowerCAmelCase_=16	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     get_basic_setup(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Optional[int]					     =     generate_predictions(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      assert (
          len(lowerCAmelCase_	) == num_samples
      ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase_	)}'''




def    _a    (							lowerCAmelCase_ = False			,      lowerCAmelCase_ = False	):





      """simple docstring"""



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

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



      _snake_case		:	List[str]					     =     Accelerator(split_batches=lowerCAmelCase_			,      dispatch_batches=lowerCAmelCase_	)
      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(lowerCAmelCase_			,      lowerCAmelCase_	)
                        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]:
                  _snake_case		:	List[Any]					     =     Accelerator(split_batches=lowerCAmelCase_			,      dispatch_batches=lowerCAmelCase_	)
                  if accelerator.is_local_main_process:
                        print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''	)
                  test_torch_metrics(lowerCAmelCase_			,      99	)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('''**Test last batch is not dropped when perfectly divisible**'''	)
      _snake_case		:	Optional[int]					     =     Accelerator()
      test_torch_metrics(lowerCAmelCase_			,      512	)
      accelerator.state._reset_state()




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      main()


if __name__ == "__main__":
   main()









'''simple docstring'''

import functools
import operator

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


UpperCAmelCase					:		List[str]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Dict		=							{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class lowerCamelCase						(a__					):
  _lowercase  :	List[str]       					=				"""sew-d"""







  def __init__(		self     ,     lowercase__=32     ,     lowercase__=768     ,     lowercase__=12     ,     lowercase__=12     ,     lowercase__=3_072     ,     lowercase__=2     ,     lowercase__=512     ,     lowercase__=256     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=("p2c", "c2p")     ,     lowercase__="layer_norm"     ,     lowercase__="gelu_python"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.0     ,     lowercase__=0.1     ,     lowercase__=0.02     ,     lowercase__=1E-7     ,     lowercase__=1E-5     ,     lowercase__="group"     ,     lowercase__="gelu"     ,     lowercase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,     lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,     lowercase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,     lowercase__=False     ,     lowercase__=128     ,     lowercase__=16     ,     lowercase__=True     ,     lowercase__=0.05     ,     lowercase__=10     ,     lowercase__=2     ,     lowercase__=0.0     ,     lowercase__=10     ,     lowercase__=0     ,     lowercase__="mean"     ,     lowercase__=False     ,     lowercase__=False     ,     lowercase__=256     ,     lowercase__=0     ,     lowercase__=1     ,     lowercase__=2     ,     **lowercase__     ,     )		->						Dict:






        """simple docstring"""





        super().__init__(**lowercase__     ,     pad_token_id=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__		)
        _snake_case		:	List[str]					     =     hidden_size
        _snake_case		:	Optional[Any]					     =     feat_extract_norm
        _snake_case		:	Tuple					     =     feat_extract_activation
        _snake_case		:	Tuple					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     conv_bias
        _snake_case		:	List[Any]					     =     num_conv_pos_embeddings
        _snake_case		:	Any					     =     num_conv_pos_embedding_groups
        _snake_case		:	Union[str, Any]					     =     len(self.conv_dim		)
        _snake_case		:	Optional[Any]					     =     num_hidden_layers
        _snake_case		:	Optional[int]					     =     intermediate_size
        _snake_case		:	Any					     =     squeeze_factor
        _snake_case		:	Optional[Any]					     =     max_position_embeddings
        _snake_case		:	Tuple					     =     position_buckets
        _snake_case		:	Tuple					     =     share_att_key
        _snake_case		:	Any					     =     relative_attention
        _snake_case		:	Optional[int]					     =     norm_rel_ebd
        _snake_case		:	Optional[Any]					     =     list(lowercase__		)
        _snake_case		:	List[Any]					     =     hidden_act
        _snake_case		:	List[Any]					     =     num_attention_heads
        _snake_case		:	Dict					     =     hidden_dropout
        _snake_case		:	Tuple					     =     attention_dropout
        _snake_case		:	Union[str, Any]					     =     activation_dropout
        _snake_case		:	List[Any]					     =     feat_proj_dropout
        _snake_case		:	Optional[int]					     =     final_dropout
        _snake_case		:	Optional[Any]					     =     layer_norm_eps
        _snake_case		:	Dict					     =     feature_layer_norm_eps
        _snake_case		:	List[Any]					     =     initializer_range
        _snake_case		:	Dict					     =     vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect.'''
                  '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                  F'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
                  F'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _snake_case		:	Union[str, Any]					     =     apply_spec_augment
        _snake_case		:	Any					     =     mask_time_prob
        _snake_case		:	List[str]					     =     mask_time_length
        _snake_case		:	Dict					     =     mask_time_min_masks
        _snake_case		:	Union[str, Any]					     =     mask_feature_prob
        _snake_case		:	Tuple					     =     mask_feature_length
        _snake_case		:	Union[str, Any]					     =     mask_feature_min_masks

        # ctc loss
        _snake_case		:	Optional[Any]					     =     ctc_loss_reduction
        _snake_case		:	Optional[Any]					     =     ctc_zero_infinity

        # sequence classification
        _snake_case		:	List[Any]					     =     use_weighted_layer_sum
        _snake_case		:	Any					     =     classifier_proj_size







  @property
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        return functools.reduce(operator.mul     ,     self.conv_stride     ,     1		)


