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


_lowercase							:      Tuple      							=logging.get_logger(__name__)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Tuple            =				["pixel_values"]







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					Optional[Dict[str, int]] = None      ,	SCREAMING_SNAKE_CASE__    :					PILImageResampling = PILImageResampling.BILINEAR      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					Dict[str, int] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					Union[int, float] = 1 / 2_55      ,	SCREAMING_SNAKE_CASE__    :					bool = True      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[float, List[float]]] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[float, List[float]]] = None      ,	**SCREAMING_SNAKE_CASE__    :					List[str]      ,	)    ->				None:
         super().__init__(**SCREAMING_SNAKE_CASE__     )
         A						:     Any         =size if size is not None else {'shortest_edge': 2_56}
         A						:     Dict         =get_size_dict(SCREAMING_SNAKE_CASE__      ,	default_to_square=SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
         A						:     Optional[Any]         =get_size_dict(SCREAMING_SNAKE_CASE__      ,	param_name='crop_size'     )
         A						:     Dict         =do_resize
         A						:     Union[str, Any]         =size
         A						:     Optional[Any]         =resample
         A						:     int         =do_center_crop
         A						:     Dict         =crop_size
         A						:     Dict         =do_rescale
         A						:     Dict         =rescale_factor
         A						:     str         =do_normalize
         A						:     Dict         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A						:     Tuple         =image_std if image_std is not None else IMAGENET_STANDARD_STD







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					np.ndarray      ,	SCREAMING_SNAKE_CASE__    :					Dict[str, int]      ,	SCREAMING_SNAKE_CASE__    :					PILImageResampling = PILImageResampling.BICUBIC      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, ChannelDimension]] = None      ,	**SCREAMING_SNAKE_CASE__    :					str      ,	)    ->				np.ndarray:
         A						:     Tuple         =get_size_dict(SCREAMING_SNAKE_CASE__      ,	default_to_square=SCREAMING_SNAKE_CASE__     )
         if "shortest_edge" not in size:
                raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'     )
         A						:     Tuple         =get_resize_output_image_size(SCREAMING_SNAKE_CASE__      ,	size=size['shortest_edge']      ,	default_to_square=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 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					np.ndarray      ,	SCREAMING_SNAKE_CASE__    :					Dict[str, int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, ChannelDimension]] = None      ,	**SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	)    ->				np.ndarray:
         A						:     int         =get_size_dict(SCREAMING_SNAKE_CASE__     )
         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(SCREAMING_SNAKE_CASE__      ,	size=(size['height'], size['width'])      ,	data_format=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					np.ndarray      ,	SCREAMING_SNAKE_CASE__    :					float      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, ChannelDimension]] = None      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				np.ndarray:
         return rescale(SCREAMING_SNAKE_CASE__      ,	scale=SCREAMING_SNAKE_CASE__      ,	data_format=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					np.ndarray      ,	SCREAMING_SNAKE_CASE__    :					Union[float, List[float]]      ,	SCREAMING_SNAKE_CASE__    :					Union[float, List[float]]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, ChannelDimension]] = None      ,	**SCREAMING_SNAKE_CASE__    :					Any      ,	)    ->				np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__      ,	mean=SCREAMING_SNAKE_CASE__      ,	std=SCREAMING_SNAKE_CASE__      ,	data_format=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					ImageInput      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	SCREAMING_SNAKE_CASE__    :					Dict[str, int] = None      ,	SCREAMING_SNAKE_CASE__    :					PILImageResampling = None      ,	SCREAMING_SNAKE_CASE__    :					bool = None      ,	SCREAMING_SNAKE_CASE__    :					Dict[str, int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[float] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[float, List[float]]] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[float, List[float]]] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, TensorType]] = None      ,	SCREAMING_SNAKE_CASE__    :					Union[str, ChannelDimension] = ChannelDimension.FIRST      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]      ,	)    ->				Any:
         A						:     Optional[int]         =do_resize if do_resize is not None else self.do_resize
         A						:     Tuple         =size if size is not None else self.size
         A						:     Any         =get_size_dict(SCREAMING_SNAKE_CASE__      ,	default_to_square=SCREAMING_SNAKE_CASE__     )
         A						:     str         =resample if resample is not None else self.resample
         A						:     Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
         A						:     List[str]         =crop_size if crop_size is not None else self.crop_size
         A						:     List[str]         =get_size_dict(SCREAMING_SNAKE_CASE__      ,	param_name='crop_size'     )
         A						:     Any         =do_rescale if do_rescale is not None else self.do_rescale
         A						:     Tuple         =rescale_factor if rescale_factor is not None else self.rescale_factor
         A						:     List[Any]         =do_normalize if do_normalize is not None else self.do_normalize
         A						:     str         =image_mean if image_mean is not None else self.image_mean
         A						:     Optional[Any]         =image_std if image_std is not None else self.image_std

         A						:     int         =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:
                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.
         A						:     Tuple         =[to_numpy_array(SCREAMING_SNAKE_CASE__     ) for image in images]

         if do_resize:
                A						:     List[str]         =[self.resize(image=SCREAMING_SNAKE_CASE__      ,	size=SCREAMING_SNAKE_CASE__      ,	resample=SCREAMING_SNAKE_CASE__     ) for image in images]

         if do_center_crop:
                A						:     Any         =[self.center_crop(image=SCREAMING_SNAKE_CASE__      ,	size=SCREAMING_SNAKE_CASE__     ) for image in images]

         if do_rescale:
                A						:     Union[str, Any]         =[self.rescale(image=SCREAMING_SNAKE_CASE__      ,	scale=SCREAMING_SNAKE_CASE__     ) for image in images]

         if do_normalize:
                A						:     Union[str, Any]         =[self.normalize(image=SCREAMING_SNAKE_CASE__      ,	mean=SCREAMING_SNAKE_CASE__      ,	std=SCREAMING_SNAKE_CASE__     ) for image in images]

         A						:     Union[str, Any]         =[to_channel_dimension_format(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) for image in images]

         A						:     List[Any]         ={'pixel_values': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__      ,	tensor_type=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Tuple] = None     )    ->				Tuple:
         A						:     Any         =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__     ):
                       A						:     Union[str, Any]         =target_sizes.numpy()

                A						:     Optional[Any]         =[]

                for idx in range(len(SCREAMING_SNAKE_CASE__     )     ):
                       A						:     Union[str, Any]         =torch.nn.functional.interpolate(
                           logits[idx].unsqueeze(dim=0     )      ,	size=target_sizes[idx]      ,	mode='bilinear'      ,	align_corners=SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =resized_logits[0].argmax(dim=0     )
                       semantic_segmentation.append(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Tuple         =logits.argmax(dim=1     )
                A						:     Optional[int]         =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

         return semantic_segmentation







import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

       def tokenize_function(lowercase:     Any					):
              # max_length=None => use the model max length (it's actually the default)
              A						:     List[str]         =tokenizer(examples['sentence1'],		examples['sentence2'],		truncation=lowercase,		max_length=lowercase					)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A						:     Any         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

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

       def collate_fn(lowercase:     Optional[int]					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =0
       for step, batch in enumerate(lowercase					):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device					)
              with torch.no_grad():
                     A						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, Any]         =accelerator.gather(
                  (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A						:     int         =config['lr']
       A						:     Optional[Any]         =int(config['num_epochs']					)
       A						:     Union[str, Any]         =int(config['seed']					)
       A						:     List[str]         =int(config['batch_size']					)
       A						:     Optional[Any]         =args.model_name_or_path

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Any         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     List[str]         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =DummyScheduler(lowercase,		total_num_steps=lowercase,		warmup_num_steps=0					)

       # 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.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     Tuple         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     List[str]         =0
       A						:     Tuple         =evaluate.load('glue',		'mrpc'					)
       A						:     Optional[int]         =num_epochs

       if args.partial_train_epoch is not None:
              A						:     Dict         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     List[Any]         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     List[Any]         =''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     Union[str, Any]         =int(lowercase					) + 1
              A						:     List[str]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              accelerator.print('resumed checkpoint performance:',		lowercase					)
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:',		lr_scheduler.get_lr()[0]					)
              accelerator.print('resumed optimizers\'s lr:',		optimizer.param_groups[0]['lr']					)
              with open(os.path.join(args.output_dir,		F'state_{starting_epoch-1}.json'					),		'r'					) as f:
                     A						:     Union[str, Any]         =json.load(lowercase					)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       A						:     str         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Tuple         =model(**lowercase					)
                     A						:     List[Any]         =outputs.loss
                     A						:     Any         =loss / gradient_accumulation_steps
                     accelerator.backward(lowercase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              A						:     Union[str, Any]         =F'epoch_{epoch}'
              A						:     Optional[Any]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[Any]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Dict         =accuracy
              A						:     Optional[Any]         =lr_scheduler.get_lr()[0]
              A						:     Any         =optimizer.param_groups[0]['lr']
              A						:     str         =epoch
              A						:     Dict         =overall_step
              accelerator.print(F'epoch {epoch}:',		lowercase					)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir,		F'state_{epoch}.json'					),		'w'					) as f:
                            json.dump(lowercase,		lowercase					)



def A__  (			)				->		Optional[int]:
       A						:     Optional[int]         =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
       parser.add_argument(
           '--model_name_or_path',		type=lowercase,		default='bert-base-cased',		help='Path to pretrained model or model identifier from huggingface.co/models.',		required=lowercase,		)
       parser.add_argument(
           '--output_dir',		type=lowercase,		default='.',		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',		)
       parser.add_argument(
           '--resume_from_checkpoint',		type=lowercase,		default=lowercase,		help='If the training should continue from a checkpoint folder.',		)
       parser.add_argument(
           '--partial_train_epoch',		type=lowercase,		default=lowercase,		help='If passed, the training will stop after this number of epochs.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=2,		help='Number of train epochs.',		)
       A						:     str         =parser.parse_args()
       A						:     Optional[int]         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






