
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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=82 ,				_UpperCamelCase=16						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=False						):






  """simple docstring"""



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

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

  with accelerator.main_process_first():
    _lowercase:			str       =     dataset.map(
        _UpperCamelCase ,				batched=_UpperCamelCase ,				remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,				)

  _lowercase:			int       =     tokenized_datasets.rename_column('''label''' ,				'''labels'''						)

  def collate_fn(_UpperCamelCase						):
    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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=82 ,				_UpperCamelCase=False ,				_UpperCamelCase=False ,				_UpperCamelCase=16						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase = False ,				_UpperCamelCase = False						):






  """simple docstring"""



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

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






  """simple docstring"""



  _lowercase:			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]:
      _lowercase:			Dict       =     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**'''						)
  _lowercase:			str       =     Accelerator()
  test_torch_metrics(_UpperCamelCase ,				512						)
  accelerator.state._reset_state()




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  main()


if __name__ == "__main__":
  main()


"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A__   :					Dict							=   logging.get_logger(__name__)


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		['''pixel_values''']
       def __init__(     self ,				A_ = True ,				A_ = None ,				A_ = PILImageResampling.BICUBIC ,				A_ = True ,				A_ = True ,				A_ = 1 / 255 ,				A_ = None ,				A_ = True ,				A_ = None ,				A_ = None ,				**A_ ,				)     ->	None:





         """simple docstring"""




         super().__init__(**A_       )
         _lowercase:			List[str]       =     size if size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			Dict       =     get_size_dict(A_       )
         _lowercase:			Any       =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			List[str]       =     get_size_dict(A_ ,				default_to_square=A_ ,				param_name='''crop_size'''       )

         _lowercase:			List[Any]       =     do_resize
         _lowercase:			Tuple       =     do_rescale
         _lowercase:			Union[str, Any]       =     do_normalize
         _lowercase:			Optional[int]       =     do_center_crop
         _lowercase:			List[str]       =     crop_size
         _lowercase:			int       =     size
         _lowercase:			List[Any]       =     resample
         _lowercase:			Optional[Any]       =     rescale_factor
         _lowercase:			str       =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         _lowercase:			Dict       =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = PILImageResampling.BILINEAR ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     get_size_dict(A_       )

         if "shortest_edge" in size:
           _lowercase:			Optional[int]       =     get_resize_output_image_size(A_ ,				size=size['''shortest_edge'''] ,				default_to_square=A_       )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           _lowercase:			Dict       =     (size['''height'''], size['''width'''])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
         return resize(A_ ,				size=A_ ,				resample=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Dict       =     get_size_dict(A_       )
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
         return center_crop(A_ ,				size=(size['''height'''], size['''width''']) ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_       )     ->	np.ndarray:





         """simple docstring"""




         return rescale(A_ ,				scale=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         return normalize(A_ ,				mean=A_ ,				std=A_ ,				data_format=A_ ,				**A_       )



       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = ChannelDimension.FIRST ,				**A_ ,				)     ->	BatchFeature:





         """simple docstring"""




         _lowercase:			List[Any]       =     do_resize if do_resize is not None else self.do_resize
         _lowercase:			Tuple       =     do_rescale if do_rescale is not None else self.do_rescale
         _lowercase:			Optional[Any]       =     do_normalize if do_normalize is not None else self.do_normalize
         _lowercase:			Optional[int]       =     do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase:			Optional[int]       =     crop_size if crop_size is not None else self.crop_size
         _lowercase:			List[Any]       =     get_size_dict(A_ ,				param_name='''crop_size''' ,				default_to_square=A_       )
         _lowercase:			List[str]       =     resample if resample is not None else self.resample
         _lowercase:			List[str]       =     rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase:			List[Any]       =     image_mean if image_mean is not None else self.image_mean
         _lowercase:			List[str]       =     image_std if image_std is not None else self.image_std

         _lowercase:			List[Any]       =     size if size is not None else self.size
         _lowercase:			List[str]       =     get_size_dict(A_       )

         if not is_batched(A_       ):
           _lowercase:			Optional[Any]       =     [images]

         if not valid_images(A_       ):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

         if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''       )

         if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''       )

         if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

         # All transformations expect numpy arrays.
         _lowercase:			Dict       =     [to_numpy_array(A_       ) for image in images]

         if do_resize:
           _lowercase:			int       =     [self.resize(image=A_ ,				size=A_ ,				resample=A_       ) for image in images]

         if do_center_crop:
           _lowercase:			str       =     [self.center_crop(image=A_ ,				size=A_       ) for image in images]

         if do_rescale:
           _lowercase:			Optional[Any]       =     [self.rescale(image=A_ ,				scale=A_       ) for image in images]

         if do_normalize:
           _lowercase:			List[Any]       =     [self.normalize(image=A_ ,				mean=A_ ,				std=A_       ) for image in images]

         _lowercase:			Optional[Any]       =     [to_channel_dimension_format(A_ ,				A_       ) for image in images]

         _lowercase:			Any       =     {'''pixel_values''': images}
         return BatchFeature(data=A_ ,				tensor_type=A_       )
