






"""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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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						:						Optional[int]					   =       16
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       32



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 16				):
				A__							 =       AutoTokenizer.from_pretrained('bert-base-cased'				)
				A__							 =       load_dataset('glue'       ,'mrpc'				)

				def tokenize_function(lowerCAmelCase__				):
								# max_length=None => use the model max length (it's actually the default)
								A__							 =       tokenizer(examples['sentence1']       ,examples['sentence2']       ,truncation=lowerCAmelCase__       ,max_length=lowerCAmelCase__				)
								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(
								    lowerCAmelCase__       ,batched=lowerCAmelCase__       ,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(lowerCAmelCase__				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__							 =       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":
												A__							 =       16
								elif accelerator.mixed_precision != "no":
												A__							 =       8
								else:
												A__							 =       None

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

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

				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						:						List[Any]					   =       mocked_dataloaders  # noqa: F811



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,lowerCAmelCase__				) == "1":
								A__							 =       2
				# New Code #
				A__							 =       int(args.gradient_accumulation_steps				)
				# Initialize accelerator
				A__							 =       Accelerator(
				    cpu=args.cpu       ,mixed_precision=args.mixed_precision       ,gradient_accumulation_steps=lowerCAmelCase__				)
				if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
								raise NotImplementedError(
								    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'				)
				# 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(lowerCAmelCase__				)
				A__ ,			A__							 =       get_dataloaders(lowerCAmelCase__       ,lowerCAmelCase__				)
				# 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=lowerCAmelCase__				)

				# 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=lowerCAmelCase__				)

				# Instantiate scheduler
				A__							 =       get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase__       ,num_warmup_steps=100       ,num_training_steps=(len(lowerCAmelCase__				) * 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(
				    lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				# Now we train the model
				for epoch in range(lowerCAmelCase__				):
								model.train()
								for step, batch in enumerate(lowerCAmelCase__				):
												# 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(lowerCAmelCase__				):
																A__							 =       model(**lowerCAmelCase__				)
																A__							 =       output.loss
																accelerator.backward(lowerCAmelCase__				)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								A__							 =       metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					):
				A__							 =       argparse.ArgumentParser(description='Simple example of training script.'				)
				parser.add_argument(
				    '--mixed_precision'       ,type=lowerCAmelCase__       ,default=lowerCAmelCase__       ,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=lowerCAmelCase__       ,default=1       ,help='The number of minibatches to be ran before gradients are accumulated.'       ,)
				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': 42, 'batch_size': 16}
				training_function(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
						main()










"""simple docstring"""






import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
					import textwrap

					from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
					import torch

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					SCREAMING_SNAKE_CASE						:						str					   =       False


SCREAMING_SNAKE_CASE						:						str					   =       logging.get_logger(__name__)
SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       '''ybelkada/fonts'''



def  __lowerCamelCase					(					):
				if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
								raise ImportError(
								    f'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
								    'Pix2StructImageProcessor. Please upgrade torch.'				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				requires_backends(lowerCAmelCase__       ,['torch']				)
				_check_torch_version()

				A__							 =       image_tensor.unsqueeze(0				)
				A__							 =       torch.nn.functional.unfold(lowerCAmelCase__       ,(patch_height, patch_width)       ,stride=(patch_height, patch_width)				)
				A__							 =       patches.reshape(image_tensor.size(0				)       ,image_tensor.size(1				)       ,lowerCAmelCase__       ,lowerCAmelCase__       ,-1				)
				A__							 =       patches.permute(0       ,4       ,2       ,3       ,1				).reshape(
				    image_tensor.size(2				) // patch_height       ,image_tensor.size(3				) // patch_width       ,image_tensor.size(1				) * patch_height * patch_width       ,)
				return patches.unsqueeze(0				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 36       ,lowerCAmelCase__ = "black"       ,lowerCAmelCase__ = "white"       ,lowerCAmelCase__ = 5       ,lowerCAmelCase__ = 5       ,lowerCAmelCase__ = 5       ,lowerCAmelCase__ = 5       ,lowerCAmelCase__ = None       ,lowerCAmelCase__ = None       ,):
				requires_backends(lowerCAmelCase__       ,'vision'				)
				# Add new lines so that each line is no more than 80 characters.

				A__							 =       textwrap.TextWrapper(width=80				)
				A__							 =       wrapper.wrap(text=lowerCAmelCase__				)
				A__							 =       '\n'.join(lowerCAmelCase__				)

				if font_bytes is not None and font_path is None:
								A__							 =       io.BytesIO(lowerCAmelCase__				)
				elif font_path is not None:
								A__							 =       font_path
				else:
								A__							 =       hf_hub_download(lowerCAmelCase__       ,'Arial.TTF'				)
				A__							 =       ImageFont.truetype(lowerCAmelCase__       ,encoding='UTF-8'       ,size=lowerCAmelCase__				)

				# Use a temporary canvas to determine the width and height in pixels when
				# rendering the text.
				A__							 =       ImageDraw.Draw(Image.new('RGB'       ,(1, 1)       ,lowerCAmelCase__				)				)
				A__ ,			A__ ,			A__ ,			A__							 =       temp_draw.textbbox((0, 0)       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				# Create the actual image with a bit of padding around the text.
				A__							 =       text_width + left_padding + right_padding
				A__							 =       text_height + top_padding + bottom_padding
				A__							 =       Image.new('RGB'       ,(image_width, image_height)       ,lowerCAmelCase__				)
				A__							 =       ImageDraw.Draw(lowerCAmelCase__				)
				draw.text(xy=(left_padding, top_padding)       ,text=lowerCAmelCase__       ,fill=lowerCAmelCase__       ,font=lowerCAmelCase__				)
				return image



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,**lowerCAmelCase__				):
				requires_backends(lowerCAmelCase__       ,'vision'				)

				# Convert to PIL image if necessary
				A__							 =       to_pil_image(lowerCAmelCase__				)

				A__							 =       render_text(lowerCAmelCase__       ,**lowerCAmelCase__				)
				A__							 =       max(header_image.width       ,image.width				)

				A__							 =       int(image.height * (new_width / image.width)				)
				A__							 =       int(header_image.height * (new_width / header_image.width)				)

				A__							 =       Image.new('RGB'       ,(new_width, new_height + new_header_height)       ,'white'				)
				new_image.paste(header_image.resize((new_width, new_header_height)				)       ,(0, 0)				)
				new_image.paste(image.resize((new_width, new_height)				)       ,(0, new_header_height)				)

				# Convert back to the original framework if necessary
				A__							 =       to_numpy_array(lowerCAmelCase__				)

				if infer_channel_dimension_format(lowerCAmelCase__				) == ChannelDimension.LAST:
								A__							 =       to_channel_dimension_format(lowerCAmelCase__       ,ChannelDimension.LAST				)

				return new_image







class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					["""flattened_patches"""]


	def __init__(      self  ,					__a = True  ,					__a = True  ,					__a = None  ,					__a = 2048  ,					__a = False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)
					A__							 =       patch_size if patch_size is not None else {'height': 16, 'width': 16}
					A__							 =       do_normalize
					A__							 =       do_convert_rgb
					A__							 =       max_patches
					A__							 =       is_vqa


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(self.extract_flattened_patches  ,					'torch'						)
					_check_torch_version()

					# convert to torch
					A__							 =       to_channel_dimension_format(__a  ,					ChannelDimension.FIRST						)
					A__							 =       torch.from_numpy(__a						)

					A__ ,			A__							 =       patch_size['height'], patch_size['width']
					A__ ,			A__							 =       get_image_size(__a						)

					# maximize scale s.t.
					A__							 =       math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)						)
					A__							 =       max(min(math.floor(scale * image_height / patch_height						)  ,					__a						)  ,					1						)
					A__							 =       max(min(math.floor(scale * image_width / patch_width						)  ,					__a						)  ,					1						)
					A__							 =       max(num_feasible_rows * patch_height  ,					1						)
					A__							 =       max(num_feasible_cols * patch_width  ,					1						)

					A__							 =       torch.nn.functional.interpolate(
					    image.unsqueeze(0						)  ,					size=(resized_height, resized_width)  ,					mode='bilinear'  ,					align_corners=__a  ,					antialias=__a  ,					).squeeze(0						)

					# [1, rows, columns, patch_height * patch_width * image_channels]
					A__							 =       torch_extract_patches(__a  ,					__a  ,					__a						)

					A__							 =       patches.shape
					A__							 =       patches_shape[1]
					A__							 =       patches_shape[2]
					A__							 =       patches_shape[3]

					# [rows * columns, patch_height * patch_width * image_channels]
					A__							 =       patches.reshape([rows * columns, depth]						)

					# [rows * columns, 1]
					A__							 =       torch.arange(__a						).reshape([rows, 1]						).repeat(1  ,					__a						).reshape([rows * columns, 1]						)
					A__							 =       torch.arange(__a						).reshape([1, columns]						).repeat(__a  ,					1						).reshape([rows * columns, 1]						)

					# Offset by 1 so the ids do not contain zeros, which represent padding.
					row_ids += 1
					col_ids += 1

					# Prepare additional patch features.
					# [rows * columns, 1]
					A__							 =       row_ids.to(torch.floataa						)
					A__							 =       col_ids.to(torch.floataa						)

					# [rows * columns, 2 + patch_height * patch_width * image_channels]
					A__							 =       torch.cat([row_ids, col_ids, patches]  ,					-1						)

					# [max_patches, 2 + patch_height * patch_width * image_channels]
					A__							 =       torch.nn.functional.pad(__a  ,					[0, 0, 0, max_patches - (rows * columns)]						).float()

					A__							 =       to_numpy_array(__a						)

					return result


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					**__a						):


					"""simple docstring"""

					if image.dtype == np.uinta:
									A__							 =       image.astype(np.floataa						)

					# take mean across the whole `image`
					A__							 =       np.mean(__a						)
					A__							 =       np.std(__a						)
					A__							 =       max(__a  ,					1.0 / math.sqrt(np.prod(image.shape						)						)						)

					return normalize(__a  ,					mean=__a  ,					std=__a  ,					**__a						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = ChannelDimension.FIRST  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       do_normalize if do_normalize is not None else self.do_normalize
					A__							 =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
					A__							 =       patch_size if patch_size is not None else self.patch_size
					A__							 =       max_patches if max_patches is not None else self.max_patches
					A__							 =       self.is_vqa

					if kwargs.get('data_format'  ,					__a						) is not None:
									raise ValueError('data_format is not an accepted input as the outputs are '						)

					A__							 =       make_list_of_images(__a						)

					if not valid_images(__a						):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
									A__							 =       [convert_to_rgb(__a						) for image in images]

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

					if is_vqa:
									if header_text is None:
													raise ValueError('A header text must be provided for VQA models.'						)
									A__							 =       kwargs.pop('font_bytes'  ,					__a						)
									A__							 =       kwargs.pop('font_path'  ,					__a						)

									if isinstance(__a  ,					__a						):
													A__							 =       [header_text] * len(__a						)

									A__							 =       [
									    render_header(__a  ,					header_text[i]  ,					font_bytes=__a  ,					font_path=__a						)
									    for i, image in enumerate(__a						)
									]

					if do_normalize:
									A__							 =       [self.normalize(image=__a						) for image in images]

					# convert to torch tensor and permute
					A__							 =       [
					    self.extract_flattened_patches(image=__a  ,					max_patches=__a  ,					patch_size=__a						)
					    for image in images
					]

					# create attention mask in numpy
					A__							 =       [(image.sum(axis=-1						) != 0).astype(np.floataa						) for image in images]

					A__							 =       BatchFeature(
					    data={'flattened_patches': images, 'attention_mask': attention_masks}  ,					tensor_type=__a						)

					return encoded_outputs


