
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


lowercase__							:							int									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   np.ndarray ,      snake_case							:   Union[int, Iterable[int]] ,      snake_case							:   bool ,      snake_case							:   int )      ->    Tuple[int, int]:



						'''simple docstring'''

						def constraint_to_multiple_of(snake_case							:   Any ,      snake_case							:   Optional[int] ,      snake_case							:   List[str]=0 ,      snake_case							:   str=None ):
												__UpperCamelCase       =			round(val / multiple ) * multiple

												if max_val is not None and x > max_val:
																		__UpperCamelCase       =			math.floor(val / multiple ) * multiple

												if x < min_val:
																		__UpperCamelCase       =			math.ceil(val / multiple ) * multiple

												return x

						__UpperCamelCase       =			(output_size, output_size) if isinstance(snake_case ,      snake_case ) else output_size

						__UpperCamelCase  ,							__UpperCamelCase       =			get_image_size(snake_case )
						__UpperCamelCase  ,							__UpperCamelCase       =			output_size

						# determine new height and width
						__UpperCamelCase       =			output_height / input_height
						__UpperCamelCase       =			output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width ) < abs(1 - scale_height ):
																		# fit width
																		__UpperCamelCase       =			scale_width
												else:
																		# fit height
																		__UpperCamelCase       =			scale_height

						__UpperCamelCase       =			constraint_to_multiple_of(scale_height * input_height ,      multiple=snake_case )
						__UpperCamelCase       =			constraint_to_multiple_of(scale_width * input_width ,      multiple=snake_case )

						return (new_height, new_width)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  ['pixel_values']



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





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			size if size is not None else {'''height''': 384, '''width''': 384}
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			do_resize
													__UpperCamelCase       =			size
													__UpperCamelCase       =			keep_aspect_ratio
													__UpperCamelCase       =			ensure_multiple_of
													__UpperCamelCase       =			resample
													__UpperCamelCase       =			do_rescale
													__UpperCamelCase       =			rescale_factor
													__UpperCamelCase       =			do_normalize
													__UpperCamelCase       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__UpperCamelCase       =			image_std if image_std is not None else IMAGENET_STANDARD_STD



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													if "height" not in size or "width" not in size:
																			raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"			)
													__UpperCamelCase       =			get_resize_output_image_size(
													    SCREAMING_SNAKE_CASE_     ,	output_size=(size['''height'''], size['''width'''])     ,	keep_aspect_ratio=SCREAMING_SNAKE_CASE_     ,	multiple=SCREAMING_SNAKE_CASE_     ,	)
													return resize(SCREAMING_SNAKE_CASE_     ,	size=SCREAMING_SNAKE_CASE_     ,	resample=SCREAMING_SNAKE_CASE_     ,	data_format=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   str:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   np.ndarray:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST     ,	**SCREAMING_SNAKE_CASE_     ,	)->   PIL.Image.Image:





													'''simple docstring'''






													__UpperCamelCase       =			do_resize if do_resize is not None else self.do_resize
													__UpperCamelCase       =			size if size is not None else self.size
													__UpperCamelCase       =			get_size_dict(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
													__UpperCamelCase       =			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
													__UpperCamelCase       =			resample if resample is not None else self.resample
													__UpperCamelCase       =			do_rescale if do_rescale is not None else self.do_rescale
													__UpperCamelCase       =			rescale_factor if rescale_factor is not None else self.rescale_factor
													__UpperCamelCase       =			do_normalize if do_normalize is not None else self.do_normalize
													__UpperCamelCase       =			image_mean if image_mean is not None else self.image_mean
													__UpperCamelCase       =			image_std if image_std is not None else self.image_std

													__UpperCamelCase       =			make_list_of_images(SCREAMING_SNAKE_CASE_			)

													if not valid_images(SCREAMING_SNAKE_CASE_			):
																			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 or resample is None:
																			raise ValueError('''Size and resample must be specified if do_resize 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.
													__UpperCamelCase       =			[to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

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

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

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

													__UpperCamelCase       =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) for image in images]

													__UpperCamelCase       =			{'''pixel_values''': images}
													return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,	tensor_type=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			outputs.logits

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

																			if is_torch_tensor(SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			target_sizes.numpy()

																			__UpperCamelCase       =			[]

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

													return semantic_segmentation






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowercase__							:							Union[str, Any]									=			1_6
lowercase__							:							Tuple									=			3_2




def 	A_ (							snake_case							:   Accelerator ,      snake_case							:   int = 16 )      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			AutoTokenizer.from_pretrained('''bert-base-cased''' )
						__UpperCamelCase       =			load_dataset('''glue''' ,      '''mrpc''' )

						def tokenize_function(snake_case							:   Tuple ):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCamelCase       =			tokenizer(examples['''sentence1'''] ,      examples['''sentence2'''] ,      truncation=snake_case ,      max_length=snake_case )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__UpperCamelCase       =			datasets.map(
												    snake_case ,      batched=snake_case ,      remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,      )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__UpperCamelCase       =			tokenized_datasets.rename_column('''label''' ,      '''labels''' )

						def collate_fn(snake_case							:   Union[str, Any] ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__UpperCamelCase       =			128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__UpperCamelCase       =			16
												elif accelerator.mixed_precision != "no":
																		__UpperCamelCase       =			8
												else:
																		__UpperCamelCase       =			None

												return tokenizer.pad(
												    snake_case ,      padding='''longest''' ,      max_length=snake_case ,      pad_to_multiple_of=snake_case ,      return_tensors='''pt''' ,      )

						# Instantiate dataloaders.
						__UpperCamelCase       =			DataLoader(
						    tokenized_datasets['''train'''] ,      shuffle=snake_case ,      collate_fn=snake_case ,      batch_size=snake_case )
						__UpperCamelCase       =			DataLoader(
						    tokenized_datasets['''validation'''] ,      shuffle=snake_case ,      collate_fn=snake_case ,      batch_size=snake_case )

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				lowercase__							:							List[str]									=			mocked_dataloaders  # noqa: F811




def 	A_ (							snake_case							:   str ,      snake_case							:   Optional[Any] )      ->    Optional[int]:



						'''simple docstring'''

						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,      snake_case ) == "1":
												__UpperCamelCase       =			2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												__UpperCamelCase       =			Accelerator(
												    cpu=args.cpu ,      mixed_precision=args.mixed_precision ,      log_with='''all''' ,      project_dir=args.project_dir )
						else:
												__UpperCamelCase       =			Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCamelCase       =			config['''lr''']
						__UpperCamelCase       =			int(config['''num_epochs'''] )
						__UpperCamelCase       =			int(config['''seed'''] )
						__UpperCamelCase       =			int(config['''batch_size'''] )
						set_seed(snake_case )

						__UpperCamelCase  ,							__UpperCamelCase       =			get_dataloaders(snake_case ,      snake_case )
						__UpperCamelCase       =			evaluate.load('''glue''' ,      '''mrpc''' )

						# If the batch size is too big we use gradient accumulation
						__UpperCamelCase       =			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__UpperCamelCase       =			batch_size // MAX_GPU_BATCH_SIZE
												__UpperCamelCase       =			MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCamelCase       =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,      return_dict=snake_case )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__UpperCamelCase       =			model.to(accelerator.device )

						# Instantiate optimizer
						__UpperCamelCase       =			AdamW(params=model.parameters() ,      lr=snake_case )

						# Instantiate scheduler
						__UpperCamelCase       =			get_linear_schedule_with_warmup(
						    optimizer=snake_case ,      num_warmup_steps=100 ,      num_training_steps=(len(snake_case ) * num_epochs) // gradient_accumulation_steps ,      )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			accelerator.prepare(
						    snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												__UpperCamelCase       =			os.path.split(snake_case )[-1].split('''.''' )[0]
												accelerator.init_trackers(snake_case ,      snake_case )

						# Now we train the model
						for epoch in range(snake_case ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		__UpperCamelCase       =			0
												for step, batch in enumerate(snake_case ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		__UpperCamelCase       =			loss / gradient_accumulation_steps
																		accelerator.backward(snake_case )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(snake_case ):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device )
																		with torch.no_grad():
																								__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.logits.argmax(dim=-1 )
																		__UpperCamelCase  ,							__UpperCamelCase       =			accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																		metric.add_batch(
																		    predictions=snake_case ,      references=snake_case ,      )

												__UpperCamelCase       =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:" ,      snake_case )

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': eval_metric['''accuracy'''],
																		        '''f1''': eval_metric['''f1'''],
																		        '''train_loss''': total_loss.item() / len(snake_case ),
																		        '''epoch''': epoch,
																		    } ,      step=snake_case ,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()




def 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser(description='''Simple example of training script.''' )
						parser.add_argument(
						    '''--mixed_precision''' ,      type=snake_case ,      default=snake_case ,      choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,      help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.''' ,      )
						parser.add_argument('''--cpu''' ,      action='''store_true''' ,      help='''If passed, will train on the CPU.''' )
						parser.add_argument(
						    '''--with_tracking''' ,      action='''store_true''' ,      help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' ,      )
						parser.add_argument(
						    '''--project_dir''' ,      type=snake_case ,      default='''logs''' ,      help='''Location on where to store experiment tracking logs` and relevent project information''' ,      )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(snake_case ,      snake_case )


if __name__ == "__main__":
				main()




