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


UpperCAmelCase_           =				1_6
UpperCAmelCase_           =				3_2




def    lowerCAmelCase_  (		lowercase:				Accelerator	,       lowercase:				int = 16	,       lowercase:				str = "bert-base-cased"		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Optional[int]    						=			AutoTokenizer.from_pretrained(lowercase		)
 _UpperCamelCase:  str    						=			load_dataset('''glue'''	,       '''mrpc'''		)

 def tokenize_function(lowercase:				Optional[Any]		):
  # max_length=None => use the model max length (it's actually the default)
  _UpperCamelCase:  Any    						=			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
 _UpperCamelCase:  str    						=			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
 _UpperCamelCase:  Dict    						=			tokenized_datasets.rename_column('''label'''	,       '''labels'''		)

 def collate_fn(lowercase:				Optional[Any]		):
  # 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.
 _UpperCamelCase:  Dict    						=			DataLoader(
     tokenized_datasets['''train''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=lowercase		)
 _UpperCamelCase:  List[str]    						=			DataLoader(
     tokenized_datasets['''validation''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=lowercase		)

 return train_dataloader, eval_dataloader




def    lowerCAmelCase_  (		lowercase:				Any	,       lowercase:				Optional[Any]	,       lowercase:				List[str]	,       lowercase:				Any		)     ->					Optional[Any]:







 '''simple docstring'''
 model.eval()
 _UpperCamelCase:  Optional[int]    						=			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():
   _UpperCamelCase:  List[Any]    						=			model(**lowercase		)
  _UpperCamelCase:  List[Any]    						=			outputs.logits.argmax(dim=-1		)
  # It is slightly faster to call this once, than multiple times
  _UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			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:
    _UpperCamelCase:  Dict    						=			predictions[: len(eval_dataloader.dataset		) - samples_seen]
    _UpperCamelCase:  int    						=			references[: len(eval_dataloader.dataset		) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=lowercase	,       references=lowercase	,       )

 _UpperCamelCase:  Any    						=			metric.compute()
 return eval_metric["accuracy"]




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Tuple		)     ->					int:







 '''simple docstring'''
 # Initialize accelerator
 _UpperCamelCase:  int    						=			Accelerator()

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

 set_seed(lowercase		)
 _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			get_dataloaders(lowercase	,       lowercase	,       lowercase		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  Tuple    						=			AutoModelForSequenceClassification.from_pretrained(lowercase	,       return_dict=lowercase		)

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

 if accelerator.state.deepspeed_plugin is not None:
  _UpperCamelCase:  Any    						=			accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  _UpperCamelCase:  Optional[int]    						=			1
 _UpperCamelCase:  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
 ):
  _UpperCamelCase:  List[str]    						=			get_linear_schedule_with_warmup(
      optimizer=lowercase	,       num_warmup_steps=0	,       num_training_steps=lowercase	,       )
 else:
  _UpperCamelCase:  Tuple    						=			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.
 _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)

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

 if args.partial_train_epoch is not None:
  _UpperCamelCase:  Any    						=			args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint		)
  _UpperCamelCase:  Union[str, Any]    						=			args.resume_from_checkpoint.split('''epoch_'''		)[1]
  _UpperCamelCase:  str    						=			''''''
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  _UpperCamelCase:  str    						=			int(lowercase		) + 1
  _UpperCamelCase:  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:
   _UpperCamelCase:  Dict    						=			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
 _UpperCamelCase:  Any    						=			{}
 for epoch in range(lowercase	,       lowercase		):
  model.train()
  for step, batch in enumerate(lowercase		):
   _UpperCamelCase:  Union[str, Any]    						=			model(**lowercase		)
   _UpperCamelCase:  Union[str, Any]    						=			outputs.loss
   _UpperCamelCase:  Union[str, 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
  _UpperCamelCase:  Any    						=			F"""epoch_{epoch}"""
  _UpperCamelCase:  Optional[Any]    						=			os.path.join(args.output_dir	,       lowercase		)
  accelerator.save_state(lowercase		)
  _UpperCamelCase:  Optional[int]    						=			evaluation_loop(lowercase	,       lowercase	,       lowercase	,       lowercase		)
  _UpperCamelCase:  List[str]    						=			accuracy
  _UpperCamelCase:  str    						=			lr_scheduler.get_lr()[0]
  _UpperCamelCase:  List[Any]    						=			optimizer.param_groups[0]['''lr''']
  _UpperCamelCase:  List[Any]    						=			epoch
  _UpperCamelCase:  Optional[Any]    						=			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    lowerCAmelCase_  (		)     ->					List[str]:







 '''simple docstring'''
 _UpperCamelCase:  List[Any]    						=			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.'''	,       )
 _UpperCamelCase:  List[str]    						=			parser.parse_args()
 _UpperCamelCase:  str    						=			{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_           =				logging.get_logger(__name__)




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Union[str, Any]		)     ->					Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			b.T
 _UpperCamelCase:  Optional[Any]    						=			np.sum(np.square(lowercase		)	,       axis=1		)
 _UpperCamelCase:  List[Any]    						=			np.sum(np.square(lowercase		)	,       axis=0		)
 _UpperCamelCase:  Tuple    						=			np.matmul(lowercase	,       lowercase		)
 _UpperCamelCase:  Tuple    						=			aa[:, None] - 2 * ab + ba[None, :]
 return d




def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				List[Any]		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			x.reshape(-1	,       3		)
 _UpperCamelCase:  Dict    						=			squared_euclidean_distance(lowercase	,       lowercase		)
 return np.argmin(lowercase	,       axis=1		)





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = ['''pixel_values''']



   def __init__(						self       :	List[str]		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	bool = True		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	bool = True		,	_lowercase       :	bool = True		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  Tuple    						=			size if size is not None else {'''height''': 256, '''width''': 256}
    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Dict    						=			np.array(_lowercase				) if clusters is not None else None
    _UpperCamelCase:  Optional[Any]    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			size
    _UpperCamelCase:  Optional[int]    						=			resample
    _UpperCamelCase:  Any    						=			do_normalize
    _UpperCamelCase:  int    						=			do_color_quantize



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Dict[str, int]		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""				)
    return resize(
        _lowercase		,	size=(size['''height'''], size['''width'''])		,	resample=_lowercase		,	data_format=_lowercase		,	**_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			rescale(image=_lowercase		,	scale=1 / 127.5		,	data_format=_lowercase				)
    _UpperCamelCase:  List[Any]    						=			image - 1
    return image



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	ImageInput		,	_lowercase       :	bool = None		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = None		,	_lowercase       :	bool = None		,	_lowercase       :	Optional[bool] = None		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	Optional[Union[str, TensorType]] = None		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST		,	**_lowercase       :	List[str]		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase:  Optional[Any]    						=			size if size is not None else self.size
    _UpperCamelCase:  Optional[Any]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			resample if resample is not None else self.resample
    _UpperCamelCase:  Union[str, Any]    						=			do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:  Any    						=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    _UpperCamelCase:  Dict    						=			clusters if clusters is not None else self.clusters
    _UpperCamelCase:  Optional[Any]    						=			np.array(_lowercase				)

    _UpperCamelCase:  List[str]    						=			make_list_of_images(_lowercase				)

    if not valid_images(_lowercase				):
     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_color_quantize and clusters is None:
     raise ValueError('''Clusters must be specified if do_color_quantize is True.'''				)

    # All transformations expect numpy arrays.
    _UpperCamelCase:  Optional[int]    						=			[to_numpy_array(_lowercase				) for image in images]

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

    if do_normalize:
     _UpperCamelCase:  Any    						=			[self.normalize(image=_lowercase				) for image in images]

    if do_color_quantize:
     _UpperCamelCase:  Tuple    						=			[to_channel_dimension_format(_lowercase		,	ChannelDimension.LAST				) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     _UpperCamelCase:  Tuple    						=			np.array(_lowercase				)
     _UpperCamelCase:  List[Any]    						=			color_quantize(_lowercase		,	_lowercase				).reshape(images.shape[:-1]				)

     # flatten to (batch_size, height*width)
     _UpperCamelCase:  Any    						=			images.shape[0]
     _UpperCamelCase:  Optional[int]    						=			images.reshape(_lowercase		,	-1				)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     _UpperCamelCase:  Tuple    						=			list(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			[to_channel_dimension_format(_lowercase		,	_lowercase				) for image in images]

    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': images}
    return BatchFeature(data=_lowercase		,	tensor_type=_lowercase				)