


"""simple docstring"""




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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__SCREAMING_SNAKE_CASE      		=    16
__SCREAMING_SNAKE_CASE      		=    32



def     __a   (	a,		a = 1_6       ):





       """simple docstring"""







       _a							 =					AutoTokenizer.from_pretrained("bert-base-cased"       )
       _a							 =					load_dataset("glue",		"mrpc"       )

       def tokenize_function(a       ):
              # max_length=None => use the model max length (it's actually the default)
              _a							 =					tokenizer(examples["sentence1"],		examples["sentence2"],		truncation=__a,		max_length=__a       )
              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():
              _a							 =					datasets.map(
                  __a,		batched=__a,		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
       _a							 =					tokenized_datasets.rename_column("label",		"labels"       )

       def collate_fn(a       ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _a							 =					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _a							 =					1_6
              elif accelerator.mixed_precision != "no":
                     _a							 =					8
              else:
                     _a							 =					None

              return tokenizer.pad(
                  __a,		padding="longest",		max_length=__a,		pad_to_multiple_of=__a,		return_tensors="pt",		)

       # Instantiate dataloaders.
       _a							 =					DataLoader(
           tokenized_datasets["train"],		shuffle=__a,		collate_fn=__a,		batch_size=__a       )
       _a							 =					DataLoader(
           tokenized_datasets["validation"],		shuffle=__a,		collate_fn=__a,		batch_size=__a       )

       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

   __SCREAMING_SNAKE_CASE      		=    mocked_dataloaders  # noqa: F811



def     __a   (	a,		a       ):





       """simple docstring"""







       if os.environ.get("TESTING_MOCKED_DATALOADERS",		__a       ) == "1":
              _a							 =					2
       # New Code #
       _a							 =					int(args.gradient_accumulation_steps       )
       _a							 =					int(args.local_sgd_steps       )
       # Initialize accelerator
       _a							 =					Accelerator(
           cpu=args.cpu,		mixed_precision=args.mixed_precision,		gradient_accumulation_steps=__a       )
       if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
              raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"       )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _a							 =					config["lr"]
       _a							 =					int(config["num_epochs"]       )
       _a							 =					int(config["seed"]       )
       _a							 =					int(config["batch_size"]       )

       _a							 =					evaluate.load("glue",		"mrpc"       )

       set_seed(__a       )
       _a ,			_a							 =					get_dataloaders(__a,		__a       )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _a							 =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased",		return_dict=__a       )

       # 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).
       _a							 =					model.to(accelerator.device       )

       # Instantiate optimizer
       _a							 =					AdamW(params=model.parameters(),		lr=__a       )

       # Instantiate scheduler
       _a							 =					get_linear_schedule_with_warmup(
           optimizer=__a,		num_warmup_steps=1_0_0,		num_training_steps=(len(__a       ) * num_epochs),		)

       # 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							 =					accelerator.prepare(
           __a,		__a,		__a,		__a,		__a       )

       # Now we train the model
       for epoch in range(__a       ):
              model.train()
              with LocalSGD(
                  accelerator=__a,		model=__a,		local_sgd_steps=__a,		enabled=local_sgd_steps is not None       ) as local_sgd:
                     for step, batch in enumerate(__a       ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device       )
                            # New code #
                            # We use the new `accumulate` context manager to perform gradient accumulation
                            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                            with accelerator.accumulate(__a       ):
                                   _a							 =					model(**__a       )
                                   _a							 =					output.loss
                                   accelerator.backward(__a       )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

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

              _a							 =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'epoch {epoch}:',		__a       )



def     __a   (	):





       """simple docstring"""







       _a							 =					argparse.ArgumentParser(description="Simple example of training script."       )
       parser.add_argument(
           "--mixed_precision",		type=__a,		default=__a,		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.",		)
       # New Code #
       parser.add_argument(
           "--gradient_accumulation_steps",		type=__a,		default=1,		help="The number of minibatches to be ran before gradients are accumulated.",		)
       parser.add_argument(
           "--local_sgd_steps",		type=__a,		default=8,		help="Number of local SGD steps or None to disable local SGD"       )
       parser.add_argument("--cpu",		action="store_true",		help="If passed, will train on the CPU."       )
       _a							 =					parser.parse_args()
       _a							 =					{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(__a,		__a       )


if __name__ == "__main__":
   main()


"""simple docstring"""




from typing import Dict, Iterable, Optional, 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, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		return [
		    int(1_000 * (box[0] / width)     ),
		    int(1_000 * (box[1] / height)     ),
		    int(1_000 * (box[2] / width)     ),
		    int(1_000 * (box[3] / height)     ),
		]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			to_pil_image(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			pil_image.size
		SCREAMING_SNAKE_CASE_    			=			pytesseract.image_to_data(__a,				lang=__a,				output_type='''dict''',				config=__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

		# filter empty words and corresponding coordinates
		SCREAMING_SNAKE_CASE_    			=			[idx for idx, word in enumerate(__a     ) if not word.strip()]
		SCREAMING_SNAKE_CASE_    			=			[word for idx, word in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]
		SCREAMING_SNAKE_CASE_    			=			[coord for idx, coord in enumerate(__a     ) if idx not in irrelevant_indices]

		# turn coordinates into (left, top, left+width, top+height) format
		SCREAMING_SNAKE_CASE_    			=			[]
		for x, y, w, h in zip(__a,				__a,				__a,				__a     ):
				SCREAMING_SNAKE_CASE_    			=			[x, y, x + w, y + h]
				actual_boxes.append(__a     )

		# finally, normalize the bounding boxes
		SCREAMING_SNAKE_CASE_    			=			[]
		for box in actual_boxes:
				normalized_boxes.append(normalize_box(__a,				__a,				__a     )     )

		assert len(__a     ) == len(__a     ), "Not as many words as there are bounding boxes"

		return words, normalized_boxes





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   ['''pixel_values''']

							def __init__(self							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = 1 / 2_55							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = ""							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			do_resize
									SCREAMING_SNAKE_CASE_    			=			size
									SCREAMING_SNAKE_CASE_    			=			resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_value
									SCREAMING_SNAKE_CASE_    			=			do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else IMAGENET_STANDARD_STD
									SCREAMING_SNAKE_CASE_    			=			apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			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()}'     )
									SCREAMING_SNAKE_CASE_    			=			(size['''height'''], size['''width'''])
									return resize(SCREAMING_SNAKE_CASE_							,							size=SCREAMING_SNAKE_CASE_							,							resample=SCREAMING_SNAKE_CASE_							,							data_format=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







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







							def _lowercase       (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_ = None							,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_    			=			size if size is not None else self.size
									SCREAMING_SNAKE_CASE_    			=			get_size_dict(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_    			=			do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_    			=			rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_    			=			do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_    			=			image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_    			=			image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_    			=			apply_ocr if apply_ocr is not None else self.apply_ocr
									SCREAMING_SNAKE_CASE_    			=			ocr_lang if ocr_lang is not None else self.ocr_lang
									SCREAMING_SNAKE_CASE_    			=			tesseract_config if tesseract_config is not None else self.tesseract_config

									SCREAMING_SNAKE_CASE_    			=			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_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('''If do_normalize is True, image_mean and image_std must be specified.'''     )

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

									# Tesseract OCR to get words + normalized bounding boxes
									if apply_ocr:
											requires_backends(self							,							'''pytesseract'''     )
											SCREAMING_SNAKE_CASE_    			=			[]
											SCREAMING_SNAKE_CASE_    			=			[]
											for image in images:
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			apply_tesseract(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
													words_batch.append(SCREAMING_SNAKE_CASE_     )
													boxes_batch.append(SCREAMING_SNAKE_CASE_     )

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

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

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

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

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

									if apply_ocr:
											SCREAMING_SNAKE_CASE_    			=			words_batch
											SCREAMING_SNAKE_CASE_    			=			boxes_batch
									return data