


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


__A :  Tuple    							=				"true"







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Union[str, Any]=82							,   A__ :       Dict=16 ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Accelerator							,   A__ :       Optional[Any]=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
  lowerCAmelCase_      :				List[Any]      =       load_dataset("""glue"""							,   """mrpc"""							,   split="""validation""" )

  def tokenize_function(A__ :       Any ):
    lowerCAmelCase_      :				Dict      =       tokenizer(examples["""sentence1"""]							,   examples["""sentence2"""]							,   truncation=A__							,   max_length=A__ )
    return outputs

  with accelerator.main_process_first():
    lowerCAmelCase_      :				Tuple      =       dataset.map(
        A__							,   batched=A__							,   remove_columns=["""idx""", """sentence1""", """sentence2"""]							,   )

  lowerCAmelCase_      :				Any      =       tokenized_datasets.rename_column("""label"""							,   """labels""" )

  def collate_fn(A__ :       Union[str, Any] ):
    if use_longest:
      return tokenizer.pad(A__							,   padding="""longest"""							,   return_tensors="""pt""" )
    return tokenizer.pad(A__							,   padding="""max_length"""							,   max_length=1_28							,   return_tensors="""pt""" )

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







def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       Optional[Any] ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Any							,   A__ :       Optional[Any] ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Accelerator							,   A__ :       Dict=82							,   A__ :       Union[str, Any]=False							,   A__ :       Dict=False							,   A__ :       List[str]=16 ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       bool = False							,   A__ :       bool = False ):
  '''simple docstring'''





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

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





  lowerCAmelCase_      :				Dict      =       Accelerator(split_batches=A__							,   dispatch_batches=A__ )
  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(A__							,   A__ )
        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_      :				Tuple      =       Accelerator(split_batches=A__							,   dispatch_batches=A__ )
      if accelerator.is_local_main_process:
        print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
      test_torch_metrics(A__							,   99 )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("""**Test last batch is not dropped when perfectly divisible**""" )
  lowerCAmelCase_      :				Any      =       Accelerator()
  test_torch_metrics(A__							,   5_12 )
  accelerator.state._reset_state()







def     UpperCamelCase_      (   A__ :       Optional[int] ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()







'''simple docstring'''






from typing import List

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


__A :  Dict    							=				logging.get_logger(__name__)

__A :  int    							=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'efficientformer'

    def __init__(       self   :  Any   ,						lowerCamelCase   :  List[int] = [3, 2, 6, 4]   ,						lowerCamelCase   :  List[int] = [48, 96, 2_24, 4_48]   ,						lowerCamelCase   :  List[bool] = [True, True, True, True]   ,						lowerCamelCase   :  int = 4_48   ,						lowerCamelCase   :  int = 32   ,						lowerCamelCase   :  int = 4   ,						lowerCamelCase   :  int = 7   ,						lowerCamelCase   :  int = 5   ,						lowerCamelCase   :  int = 8   ,						lowerCamelCase   :  int = 4   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  int = 16   ,						lowerCamelCase   :  int = 3   ,						lowerCamelCase   :  int = 3   ,						lowerCamelCase   :  int = 3   ,						lowerCamelCase   :  int = 2   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  float = 1E-5   ,						lowerCamelCase   :  str = "gelu"   ,						lowerCamelCase   :  float = 0.02   ,						lowerCamelCase   :  float = 1E-12   ,						lowerCamelCase   :  int = 2_24   ,						lowerCamelCase   :  float = 1E-05   ,						**lowerCamelCase   :  int   ,						)       ->  None:
      super().__init__(**lowerCamelCase		)

      lowerCAmelCase_      :				List[str]      =       hidden_act
      lowerCAmelCase_      :				List[Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       hidden_sizes
      lowerCAmelCase_      :				List[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				Union[str, Any]      =       num_attention_heads
      lowerCAmelCase_      :				Tuple      =       initializer_range
      lowerCAmelCase_      :				Union[str, Any]      =       layer_norm_eps
      lowerCAmelCase_      :				int      =       patch_size
      lowerCAmelCase_      :				List[str]      =       num_channels
      lowerCAmelCase_      :				Dict      =       depths
      lowerCAmelCase_      :				int      =       mlp_expansion_ratio
      lowerCAmelCase_      :				Optional[Any]      =       downsamples
      lowerCAmelCase_      :				Union[str, Any]      =       dim
      lowerCAmelCase_      :				Union[str, Any]      =       key_dim
      lowerCAmelCase_      :				str      =       attention_ratio
      lowerCAmelCase_      :				Tuple      =       resolution
      lowerCAmelCase_      :				Optional[Any]      =       pool_size
      lowerCAmelCase_      :				str      =       downsample_patch_size
      lowerCAmelCase_      :				Dict      =       downsample_stride
      lowerCAmelCase_      :				str      =       downsample_pad
      lowerCAmelCase_      :				str      =       drop_path_rate
      lowerCAmelCase_      :				List[Any]      =       num_metaad_blocks
      lowerCAmelCase_      :				Tuple      =       distillation
      lowerCAmelCase_      :				Optional[Any]      =       use_layer_scale
      lowerCAmelCase_      :				Dict      =       layer_scale_init_value
      lowerCAmelCase_      :				Optional[Any]      =       image_size
      lowerCAmelCase_      :				Optional[Any]      =       batch_norm_eps



