





'''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  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					int=8_2						,    lowerCamelCase_     :					Dict=1_6			):
			set_seed(4_2			)
			__lowercase			  =							RegressionModel()
			__lowercase			  =							deepcopy(lowerCamelCase_			)
			__lowercase			  =							RegressionDataset(length=lowerCamelCase_			)
			__lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=lowerCamelCase_			)
			model.to(accelerator.device			)
			__lowercase  ,						__lowercase			  =							accelerator.prepare(lowerCamelCase_						,    lowerCamelCase_			)
			return model, ddp_model, dataloader




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					Tuple=False			):
			__lowercase			  =							AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''			)
			__lowercase			  =							load_dataset('''glue'''						,    '''mrpc'''						,    split='''validation'''			)

			def tokenize_function(lowerCamelCase_     :					int			):
						__lowercase			  =							tokenizer(examples['''sentence1''']						,    examples['''sentence2''']						,    truncation=lowerCamelCase_						,    max_length=lowerCamelCase_			)
						return outputs

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

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

			def collate_fn(lowerCamelCase_     :					List[str]			):
						if use_longest:
									return tokenizer.pad(lowerCamelCase_						,    padding='''longest'''						,    return_tensors='''pt'''			)
						return tokenizer.pad(lowerCamelCase_						,    padding='''max_length'''						,    max_length=1_2_8						,    return_tensors='''pt'''			)

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




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




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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					List[str]=8_2						,    lowerCamelCase_     :					int=False						,    lowerCamelCase_     :					str=False						,    lowerCamelCase_     :					Any=1_6			):
			__lowercase  ,						__lowercase  ,						__lowercase			  =							get_basic_setup(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
			__lowercase  ,						__lowercase			  =							generate_predictions(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
			assert (
			    len(lowerCamelCase_			) == num_samples
			), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase_			)}"




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

			# Then do distributed
			__lowercase  ,						__lowercase  ,						__lowercase			  =							setup['''ddp''']
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									__lowercase			  =							model(**lowerCamelCase_			)
						__lowercase			  =							outputs.logits.argmax(dim=-1			)
						__lowercase			  =							batch['''labels''']
						__lowercase  ,						__lowercase			  =							accelerator.gather_for_metrics((preds, references)			)
						metric.add_batch(predictions=lowerCamelCase_						,    references=lowerCamelCase_			)
			__lowercase			  =							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  (			):
			__lowercase			  =							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]:
									__lowercase			  =							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_						,    9_9			)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print('''**Test last batch is not dropped when perfectly divisible**'''			)
			__lowercase			  =							Accelerator()
			test_torch_metrics(lowerCamelCase_						,    5_1_2			)
			accelerator.state._reset_state()




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()












'''simple docstring'''



from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			int     =    ["pixel_values"]


	def __init__(self       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = PILImageResampling.BILINEAR       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = True       ,_lowerCamelCase = 1 / 255       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							None:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)
				__lowercase			  =							size if size is not None else {'''shortest_edge''': 256}
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__lowercase			  =							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)
				__lowercase			  =							do_resize
				__lowercase			  =							size
				__lowercase			  =							resample
				__lowercase			  =							do_center_crop
				__lowercase			  =							crop_size
				__lowercase			  =							do_rescale
				__lowercase			  =							rescale_factor
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__lowercase			  =							image_std if image_std is not None else IMAGENET_STANDARD_STD


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = PILImageResampling.BICUBIC       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				if "shortest_edge" not in size:
							raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
				__lowercase			  =							get_resize_output_image_size(_lowerCamelCase       ,size=size['''shortest_edge''']       ,default_to_square=_lowerCamelCase	)
				return resize(_lowerCamelCase       ,size=_lowerCamelCase       ,resample=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase	)
				if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"	)
				return center_crop(_lowerCamelCase       ,size=(size['''height'''], size['''width'''])       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase	)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (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       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							do_resize if do_resize is not None else self.do_resize
				__lowercase			  =							size if size is not None else self.size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__lowercase			  =							resample if resample is not None else self.resample
				__lowercase			  =							do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowercase			  =							crop_size if crop_size is not None else self.crop_size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)
				__lowercase			  =							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase			  =							rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowercase			  =							do_normalize if do_normalize is not None else self.do_normalize
				__lowercase			  =							image_mean if image_mean is not None else self.image_mean
				__lowercase			  =							image_std if image_std is not None else self.image_std

				__lowercase			  =							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.
				__lowercase			  =							[to_numpy_array(_lowerCamelCase	) for image in images]

				if do_resize:
							__lowercase			  =							[self.resize(image=_lowerCamelCase       ,size=_lowerCamelCase       ,resample=_lowerCamelCase	) for image in images]

				if do_center_crop:
							__lowercase			  =							[self.center_crop(image=_lowerCamelCase       ,size=_lowerCamelCase	) for image in images]

				if do_rescale:
							__lowercase			  =							[self.rescale(image=_lowerCamelCase       ,scale=_lowerCamelCase	) for image in images]

				if do_normalize:
							__lowercase			  =							[self.normalize(image=_lowerCamelCase       ,mean=_lowerCamelCase       ,std=_lowerCamelCase	) for image in images]

				__lowercase			  =							[to_channel_dimension_format(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

				__lowercase			  =							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase       ,tensor_type=_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							str:







				'''simple docstring'''





				__lowercase			  =							outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(_lowerCamelCase	) != len(_lowerCamelCase	):
										raise ValueError(
										    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''	)

							if is_torch_tensor(_lowerCamelCase	):
										__lowercase			  =							target_sizes.numpy()

							__lowercase			  =							[]

							for idx in range(len(_lowerCamelCase	)	):
										__lowercase			  =							torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0	)       ,size=target_sizes[idx]       ,mode='''bilinear'''       ,align_corners=_lowerCamelCase	)
										__lowercase			  =							resized_logits[0].argmax(dim=0	)
										semantic_segmentation.append(_lowerCamelCase	)
				else:
							__lowercase			  =							logits.argmax(dim=1	)
							__lowercase			  =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

				return semantic_segmentation





