


'''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_			:  str										=   'true'


def       _lowercase							(					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Tuple=82,					UpperCamelCase__							:       Any=16      ):
 set_seed(42      )
 __A	:		Dict	  =      RegressionModel()
 __A	:		int	  =      deepcopy(UpperCamelCase__      )
 __A	:		int	  =      RegressionDataset(length=UpperCamelCase__      )
 __A	:		List[str]	  =      DataLoader(UpperCamelCase__,					batch_size=UpperCamelCase__      )
 model.to(accelerator.device      )
 __A  ,__A	:		int	  =      accelerator.prepare(UpperCamelCase__,					UpperCamelCase__      )
 return model, ddp_model, dataloader


def       _lowercase							(					UpperCamelCase__							:       Accelerator,					UpperCamelCase__							:       str=False      ):
 __A	:		Any	  =      AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
 __A	:		List[str]	  =      load_dataset('glue',					'mrpc',					split='validation'      )

 def tokenize_function(UpperCamelCase__							:       str      ):
  __A	:		Any	  =      tokenizer(examples['sentence1'],					examples['sentence2'],					truncation=UpperCamelCase__,					max_length=UpperCamelCase__      )
  return outputs

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

 __A	:		Tuple	  =      tokenized_datasets.rename_column('label',					'labels'      )

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

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


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


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


def       _lowercase							(					UpperCamelCase__							:       Accelerator,					UpperCamelCase__							:       Union[str, Any]=82,					UpperCamelCase__							:       List[str]=False,					UpperCamelCase__							:       Any=False,					UpperCamelCase__							:       Dict=16      ):
 __A  ,__A  ,__A	:		Tuple	  =      get_basic_setup(UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )
 __A  ,__A	:		List[Any]	  =      generate_predictions(UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )
 assert (
     len(UpperCamelCase__      ) == num_samples
 ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__      )}"""


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

 # Then do distributed
 __A  ,__A  ,__A	:		int	  =      setup['ddp']
 model.eval()
 for batch in dataloader:
  with torch.inference_mode():
   __A	:		Union[str, Any]	  =      model(**UpperCamelCase__      )
  __A	:		Dict	  =      outputs.logits.argmax(dim=-1      )
  __A	:		List[Any]	  =      batch['labels']
  __A  ,__A	:		Optional[Any]	  =      accelerator.gather_for_metrics((preds, references)      )
  metric.add_batch(predictions=UpperCamelCase__,					references=UpperCamelCase__      )
 __A	:		List[Any]	  =      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       _lowercase							(					):
 __A	:		List[str]	  =      Accelerator(split_batches=UpperCamelCase__,					dispatch_batches=UpperCamelCase__      )
 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(UpperCamelCase__,					UpperCamelCase__      )
    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	:		List[Any]	  =      Accelerator(split_batches=UpperCamelCase__,					dispatch_batches=UpperCamelCase__      )
   if accelerator.is_local_main_process:
    print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""      )
   test_torch_metrics(UpperCamelCase__,					99      )
   accelerator.state._reset_state()
 if accelerator.is_local_main_process:
  print('**Test last batch is not dropped when perfectly divisible**'      )
 __A	:		Optional[int]	  =      Accelerator()
 test_torch_metrics(UpperCamelCase__,					512      )
 accelerator.state._reset_state()


def       _lowercase							(					UpperCamelCase__							:       List[Any]      ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






