


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


a					  =     logging.getLogger(__name__)


def 			UpperCAmelCase_				(      UpperCAmelCase__=2						,							UpperCAmelCase__=3						,							UpperCAmelCase__=1_6						,							UpperCAmelCase__ = 1_0						,							UpperCAmelCase__ = 2  ):

					def get_dataset(UpperCAmelCase__  ):
										lowercase_  							=			torch.randn(batch_size * n_batches						,							1  )
										return TensorDataset(UpperCAmelCase__						,							a * x + b + 0.1 * torch.randn(batch_size * n_batches						,							1  )  )

					lowercase_  							=			get_dataset(UpperCAmelCase__  )
					lowercase_  							=			get_dataset(UpperCAmelCase__  )
					lowercase_  							=			DataLoader(UpperCAmelCase__						,							shuffle=UpperCAmelCase__						,							batch_size=UpperCAmelCase__						,							num_workers=4  )
					lowercase_  							=			DataLoader(UpperCAmelCase__						,							shuffle=UpperCAmelCase__						,							batch_size=UpperCAmelCase__						,							num_workers=4  )
					return (train_dataloader, valid_dataloader)


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=None  ):
					lowercase_  							=			[]
					for epoch in range(UpperCAmelCase__  ):
										# Train quickly
										model.train()
										for batch in dataloader:
															lowercase_  							=			batch
															lowercase_  							=			model(UpperCAmelCase__  )
															lowercase_  							=			torch.nn.functional.mse_loss(UpperCAmelCase__						,							UpperCAmelCase__  )
															accelerator.backward(UpperCAmelCase__  )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()  )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands







class        UpperCamelCase__   ( nn.Module   ):

					def __init__(     self       :   Optional[Any]       ):






										'''simple docstring'''






										super().__init__()
										lowercase_  							=			nn.Parameter(torch.randn(1       )       )
										lowercase_  							=			nn.Parameter(torch.randn(1       )       )







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										return x * self.a + self.b







class        UpperCamelCase__   ( unittest.TestCase   ):

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			torch.optim.Adam(params=model.parameters()							,					lr=1e-3       )
															lowercase_  							=			dummy_dataloaders()
															lowercase_  							=			ProjectConfiguration(total_limit=1							,					project_dir=lowercase_							,					automatic_checkpoint_naming=lowercase_       )
															# Train baseline
															lowercase_  							=			Accelerator(project_config=lowercase_       )
															lowercase_  							=			accelerator.prepare(
															    lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															# Save initial
															accelerator.save_state()

															# Save second state
															accelerator.save_state()
															self.assertEqual(len(os.listdir(accelerator.project_dir       )       )							,					1       )

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			torch.optim.Adam(params=model.parameters()							,					lr=1e-3       )
															lowercase_  							=			dummy_dataloaders()
															# Train baseline
															lowercase_  							=			Accelerator()
															lowercase_  							=			accelerator.prepare(
															    lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															# Save initial
															lowercase_  							=			os.path.join(lowercase_							,					"""initial"""       )
															accelerator.save_state(lowercase_       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()
															lowercase_  							=			train(3							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()

															# Train partially
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			torch.optim.Adam(params=model.parameters()							,					lr=1e-3       )
															lowercase_  							=			dummy_dataloaders()
															lowercase_  							=			Accelerator()
															lowercase_  							=			accelerator.prepare(
															    lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															accelerator.load_state(lowercase_       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )

															lowercase_  							=			train(2							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															# Save everything
															lowercase_  							=			os.path.join(lowercase_							,					"""checkpoint"""       )
															accelerator.save_state(lowercase_       )

															# Load everything back in and make sure all states work
															accelerator.load_state(lowercase_       )
															test_rands += train(1							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			torch.optim.Adam(params=model.parameters()							,					lr=1e-3       )
															lowercase_  							=			dummy_dataloaders()
															lowercase_  							=			ProjectConfiguration(automatic_checkpoint_naming=lowercase_       )

															# Train baseline
															lowercase_  							=			Accelerator(project_dir=lowercase_							,					project_config=lowercase_       )
															lowercase_  							=			accelerator.prepare(
															    lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															# Save initial
															accelerator.save_state()
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()
															lowercase_  							=			train(3							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()

															# Train partially
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			torch.optim.Adam(params=model.parameters()							,					lr=1e-3       )
															lowercase_  							=			dummy_dataloaders()
															lowercase_  							=			ProjectConfiguration(iteration=1							,					automatic_checkpoint_naming=lowercase_       )
															lowercase_  							=			Accelerator(project_dir=lowercase_							,					project_config=lowercase_       )
															lowercase_  							=			accelerator.prepare(
															    lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															accelerator.load_state(os.path.join(lowercase_							,					"""checkpoints"""							,					"""checkpoint_0"""       )       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )

															lowercase_  							=			train(2							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															# Save everything
															accelerator.save_state()

															# Load everything back in and make sure all states work
															accelerator.load_state(os.path.join(lowercase_							,					"""checkpoints"""							,					"""checkpoint_1"""       )       )
															test_rands += train(1							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															(lowercase_)  							=			model.a.item(), model.b.item()
															lowercase_  							=			optimizer.state_dict()
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )
															self.assertEqual(lowercase_							,					lowercase_       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			torch.tensor([1, 2, 3]       )
										lowercase_  							=			torch.tensor([2, 3, 4]       )
										lowercase_  							=			DummyModel()
										lowercase_  							=			torch.optim.Adam(net.parameters()       )
										lowercase_  							=			Accelerator()
										with self.assertRaises(lowercase_       ) as ve:
															accelerator.register_for_checkpointing(lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
										lowercase_  							=			str(ve.exception       )
										self.assertTrue("""Item at index 0""" in message       )
										self.assertTrue("""Item at index 1""" in message       )
										self.assertFalse("""Item at index 2""" in message       )
										self.assertFalse("""Item at index 3""" in message       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			torch.optim.Adam(params=model.parameters()							,					lr=1e-3       )
															lowercase_  							=			torch.optim.lr_scheduler.StepLR(lowercase_							,					step_size=1							,					gamma=0.99       )
															lowercase_  							=			dummy_dataloaders()
															lowercase_  							=			ProjectConfiguration(automatic_checkpoint_naming=lowercase_       )
															# Train baseline
															lowercase_  							=			Accelerator(project_dir=lowercase_							,					project_config=lowercase_       )
															lowercase_  							=			accelerator.prepare(
															    lowercase_							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															# Save initial
															accelerator.save_state()
															lowercase_  							=			scheduler.state_dict()
															train(3							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_							,					lowercase_       )
															self.assertNotEqual(lowercase_							,					scheduler.state_dict()       )

															# Load everything back in and make sure all states work
															accelerator.load_state(os.path.join(lowercase_							,					"""checkpoints"""							,					"""checkpoint_0"""       )       )
															self.assertEqual(lowercase_							,					scheduler.state_dict()       )

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42       )
															lowercase_  							=			DummyModel()
															lowercase_  							=			ProjectConfiguration(automatic_checkpoint_naming=lowercase_							,					total_limit=2       )
															# Train baseline
															lowercase_  							=			Accelerator(project_dir=lowercase_							,					project_config=lowercase_       )
															lowercase_  							=			accelerator.prepare(lowercase_       )
															# Save 3 states:
															for _ in range(11       ):
																				accelerator.save_state()
															self.assertTrue(not os.path.exists(os.path.join(lowercase_							,					"""checkpoints"""							,					"""checkpoint_0"""       )       )       )
															self.assertTrue(os.path.exists(os.path.join(lowercase_							,					"""checkpoints"""							,					"""checkpoint_9"""       )       )       )
															self.assertTrue(os.path.exists(os.path.join(lowercase_							,					"""checkpoints"""							,					"""checkpoint_10"""       )       )       )







					@require_cuda
					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__       )]
										execute_subprocess_async(lowercase_							,					env=os.environ.copy()       )


if __name__ == "__main__":
							a					  =     "/tmp/accelerate/state_checkpointing"
							a					  =     DummyModel()
							a					  =     torch.optim.Adam(params=model.parameters(), lr=1E-3)
							a					  =     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
							a					  =     dummy_dataloaders()
							a					  =     ProjectConfiguration(automatic_checkpoint_naming=True)
							# Train baseline
							a					  =     Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
							if accelerator.process_index == 0:
														if os.path.exists(savedir):
																					shutil.rmtree(savedir)
														os.makedirs(savedir)
							a					  =     accelerator.prepare(
							    model, optimizer, train_dataloader, valid_dataloader, scheduler
							)
							a					  =     accelerator.prepare(model, optimizer)
							train(3, model, train_dataloader, optimizer, accelerator, scheduler)
							# Check that the intial optimizer is loaded on the GPU
							for group in optimizer.param_groups:
														a					  =     group["params"][0].device
														break
							assert param_device.type == accelerator.device.type
							a					  =     model.cpu()
							accelerator.wait_for_everyone()
							accelerator.save_state()
							accelerator.wait_for_everyone()

							# Check CPU state
							accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
							for group in optimizer.param_groups:
														a					  =     group["params"][0].device
														break
							assert (
							    param_device.type == torch.device('cpu').type
							), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

							# Check device state
							model.to(accelerator.device)
							accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
							for group in optimizer.param_groups:
														a					  =     group["params"][0].device
														break
							assert (
							    param_device.type == accelerator.device.type
							), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

							# Check error
							with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
														accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
							accelerator.wait_for_everyone()
							if accelerator.process_index == 0:
														shutil.rmtree(savedir)
							accelerator.wait_for_everyone()






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:
							a					  =     False


a					  =     logging.get_logger(__name__)
a					  =     'ybelkada/fonts'


def 			UpperCAmelCase_				(      ):
					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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					requires_backends(UpperCAmelCase__						,							["""torch"""]  )
					_check_torch_version()

					lowercase_  							=			image_tensor.unsqueeze(0  )
					lowercase_  							=			torch.nn.functional.unfold(UpperCAmelCase__						,							(patch_height, patch_width)						,							stride=(patch_height, patch_width)  )
					lowercase_  							=			patches.reshape(image_tensor.size(0  )						,							image_tensor.size(1  )						,							UpperCAmelCase__						,							UpperCAmelCase__						,							-1  )
					lowercase_  							=			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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__ = 3_6						,							UpperCAmelCase__ = "black"						,							UpperCAmelCase__ = "white"						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = None						,							):
					requires_backends(UpperCAmelCase__						,							"""vision"""  )
					# Add new lines so that each line is no more than 80 characters.

					lowercase_  							=			textwrap.TextWrapper(width=8_0  )
					lowercase_  							=			wrapper.wrap(text=UpperCAmelCase__  )
					lowercase_  							=			"""\n""".join(UpperCAmelCase__  )

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

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							**UpperCAmelCase__  ):
					requires_backends(UpperCAmelCase__						,							"""vision"""  )

					# Convert to PIL image if necessary
					lowercase_  							=			to_pil_image(UpperCAmelCase__  )

					lowercase_  							=			render_text(UpperCAmelCase__						,							**UpperCAmelCase__  )
					lowercase_  							=			max(header_image.width						,							image.width  )

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

					lowercase_  							=			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
					lowercase_  							=			to_numpy_array(UpperCAmelCase__  )

					if infer_channel_dimension_format(UpperCAmelCase__  ) == ChannelDimension.LAST:
										lowercase_  							=			to_channel_dimension_format(UpperCAmelCase__						,							ChannelDimension.LAST  )

					return new_image







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Tuple						=   ['flattened_patches']

					def __init__(     self       :   str							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   int = 2_048							,					UpperCamelCase__       :   bool = False							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   dict							,					**UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






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

										# convert to torch
										lowercase_  							=			to_channel_dimension_format(UpperCamelCase__							,					ChannelDimension.FIRST       )
										lowercase_  							=			torch.from_numpy(UpperCamelCase__       )

										lowercase_							,					lowercase_  							=			patch_size["""height"""], patch_size["""width"""]
										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )

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

										lowercase_  							=			torch.nn.functional.interpolate(
										    image.unsqueeze(0       )							,					size=(resized_height, resized_width)							,					mode="""bilinear"""							,					align_corners=UpperCamelCase__							,					antialias=UpperCamelCase__							,					).squeeze(0       )

										# [1, rows, columns, patch_height * patch_width * image_channels]
										lowercase_  							=			torch_extract_patches(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )

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

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

										# [rows * columns, 1]
										lowercase_  							=			torch.arange(UpperCamelCase__       ).reshape([rows, 1]       ).repeat(1							,					UpperCamelCase__       ).reshape([rows * columns, 1]       )
										lowercase_  							=			torch.arange(UpperCamelCase__       ).reshape([1, columns]       ).repeat(UpperCamelCase__							,					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]
										lowercase_  							=			row_ids.to(torch.floataa       )
										lowercase_  							=			col_ids.to(torch.floataa       )

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

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

										lowercase_  							=			to_numpy_array(UpperCamelCase__       )

										return result

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






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

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

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







					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[str] = None							,					UpperCamelCase__       :   bool = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   ChannelDimension = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Union[str, Any]							,					):






										'''simple docstring'''






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

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

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															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:
															lowercase_  							=			[convert_to_rgb(UpperCamelCase__       ) for image in images]

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

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

															if isinstance(UpperCamelCase__							,					UpperCamelCase__       ):
																				lowercase_  							=			[header_text] * len(UpperCamelCase__       )

															lowercase_  							=			[
															    render_header(UpperCamelCase__							,					header_text[i]							,					font_bytes=UpperCamelCase__							,					font_path=UpperCamelCase__       )
															    for i, image in enumerate(UpperCamelCase__       )
															]

										if do_normalize:
															lowercase_  							=			[self.normalize(image=UpperCamelCase__       ) for image in images]

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

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

										lowercase_  							=			BatchFeature(
										    data={"""flattened_patches""": images, """attention_mask""": attention_masks}							,					tensor_type=UpperCamelCase__       )

										return encoded_outputs


