"""simple docstring"""
from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  ["""pixel_values"""]

				def __init__(			self						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = PILImageResampling.BICUBIC						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      lowerCAmelCase = 1 / 2_55						,      lowerCAmelCase = True						,      lowerCAmelCase = IMAGENET_DEFAULT_MEAN						,      lowerCAmelCase = IMAGENET_DEFAULT_STD						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )
										snake_case        =     size if size is not None else {'shortest_edge': 2_24}
										snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
										snake_case        =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
										snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'      )

										snake_case        =     do_resize
										snake_case        =     size
										snake_case        =     resample
										snake_case        =     do_center_crop
										snake_case        =     crop_size
										snake_case        =     do_rescale
										snake_case        =     rescale_factor
										snake_case        =     do_normalize
										snake_case        =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										snake_case        =     image_std if image_std is not None else IMAGENET_DEFAULT_STD

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = PILImageResampling.BICUBIC						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
										# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
										if "shortest_edge" in size:
																snake_case        =     int((2_56 / 2_24) * size['shortest_edge']      )
																snake_case        =     get_resize_output_image_size(lowerCAmelCase						,      size=lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
																snake_case        =     {'height': output_size[0], 'width': output_size[1]}
										if "height" not in size_dict or "width" not in size_dict:
																raise ValueError(
																    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""      )
										return resize(
										    lowerCAmelCase						,      size=(size_dict['height'], size_dict['width'])						,      resample=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     get_size_dict(lowerCAmelCase      )
										if "height" not in size or "width" not in size:
																raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""      )
										return center_crop(lowerCAmelCase						,      size=(size['height'], size['width'])						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										return rescale(lowerCAmelCase						,      scale=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = ChannelDimension.FIRST						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     do_resize if do_resize is not None else self.do_resize
										snake_case        =     resample if resample is not None else self.resample
										snake_case        =     do_center_crop if do_center_crop is not None else self.do_center_crop
										snake_case        =     do_rescale if do_rescale is not None else self.do_rescale
										snake_case        =     rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case        =     do_normalize if do_normalize is not None else self.do_normalize
										snake_case        =     image_mean if image_mean is not None else self.image_mean
										snake_case        =     image_std if image_std is not None else self.image_std

										snake_case        =     size if size is not None else self.size
										snake_case        =     get_size_dict(lowerCAmelCase						,      default_to_square=lowerCAmelCase      )
										snake_case        =     crop_size if crop_size is not None else self.crop_size
										snake_case        =     get_size_dict(lowerCAmelCase						,      param_name='crop_size'      )

										snake_case        =     make_list_of_images(lowerCAmelCase      )

										if not valid_images(lowerCAmelCase      ):
																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.'      )

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

										# All transformations expect numpy arrays.
										snake_case        =     [to_numpy_array(lowerCAmelCase      ) for image in images]

										if do_resize:
																snake_case        =     [self.resize(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										if do_center_crop:
																snake_case        =     [self.center_crop(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										if do_rescale:
																snake_case        =     [self.rescale(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										if do_normalize:
																snake_case        =     [self.normalize(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										snake_case        =     [to_channel_dimension_format(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										snake_case        =     {'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase						,      tensor_type=lowerCAmelCase      )



"""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


SCREAMING_SNAKE_CASE__   =					"true"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Dict=8_2	,     _UpperCamelCase    :			int=1_6   )   ->			str:





						"""simple docstring"""






						set_seed(4_2   )
						snake_case        =     RegressionModel()
						snake_case        =     deepcopy(_UpperCamelCase   )
						snake_case        =     RegressionDataset(length=_UpperCamelCase   )
						snake_case        =     DataLoader(_UpperCamelCase	,     batch_size=_UpperCamelCase   )
						model.to(accelerator.device   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return model, ddp_model, dataloader





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Optional[Any]=False   )   ->			List[str]:





						"""simple docstring"""






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

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

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

						snake_case        =     tokenized_datasets.rename_column('label'	,     'labels'   )

						def collate_fn(_UpperCamelCase    :			Optional[Any]   ):
												if use_longest:
																		return tokenizer.pad(_UpperCamelCase	,     padding='longest'	,     return_tensors='pt'   )
												return tokenizer.pad(_UpperCamelCase	,     padding='max_length'	,     max_length=1_2_8	,     return_tensors='pt'   )

						return DataLoader(_UpperCamelCase	,     shuffle=_UpperCamelCase	,     collate_fn=_UpperCamelCase	,     batch_size=1_6   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			Optional[Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     []
						for batch in dataloader:
												snake_case   ,snake_case        =     batch.values()
												with torch.no_grad():
																		snake_case        =     model(_UpperCamelCase   )
																		snake_case   ,snake_case        =     accelerator.gather_for_metrics((logit, target)   )
																		logits_and_targets.append((logit, target)   )
						snake_case   ,snake_case        =     [], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCamelCase   )
												targs.append(_UpperCamelCase   )
						snake_case   ,snake_case        =     torch.cat(_UpperCamelCase   ), torch.cat(_UpperCamelCase   )
						return logits, targs





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Tuple=8_2	,     _UpperCamelCase    :			Optional[Any]=False	,     _UpperCamelCase    :			int=False	,     _UpperCamelCase    :			List[str]=1_6   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case        =     get_basic_setup(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						snake_case   ,snake_case        =     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    :			bool = False	,     _UpperCamelCase    :			bool = False   )   ->			Tuple:





						"""simple docstring"""






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

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





						"""simple docstring"""






						snake_case        =     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]:
																		snake_case        =     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	,     9_9   )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test last batch is not dropped when perfectly divisible**'   )
						snake_case        =     Accelerator()
						test_torch_metrics(_UpperCamelCase	,     5_1_2   )
						accelerator.state._reset_state()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			str:





						"""simple docstring"""






						main()


if __name__ == "__main__":
					main()


