






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__			:    Optional[Any]         =				False


A__			:    Optional[int]         =				logging.get_logger(__name__)
A__			:    Tuple         =				'ybelkada/fonts'






def 						a    (		):






				'''simple docstring'''
				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 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				requires_backends(lowerCamelCase_	,     ['''torch''']       )
				_check_torch_version()

				lowercase__     				=    image_tensor.unsqueeze(0       )
				lowercase__     				=    torch.nn.functional.unfold(lowerCamelCase_	,     (patch_height, patch_width)	,     stride=(patch_height, patch_width)       )
				lowercase__     				=    patches.reshape(image_tensor.size(0       )	,     image_tensor.size(1       )	,     lowerCamelCase_	,     lowerCamelCase_	,     -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 						a    (		lowerCamelCase_	,     lowerCamelCase_ = 36	,     lowerCamelCase_ = "black"	,     lowerCamelCase_ = "white"	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = None	,     lowerCamelCase_ = None	,     ):






				'''simple docstring'''
				requires_backends(lowerCamelCase_	,     '''vision'''       )
				# Add new lines so that each line is no more than 80 characters.

				lowercase__     				=    textwrap.TextWrapper(width=80       )
				lowercase__     				=    wrapper.wrap(text=lowerCamelCase_       )
				lowercase__     				=    '''\n'''.join(lowerCamelCase_       )

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

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

				# 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)	,     lowerCamelCase_       )
				lowercase__     				=    ImageDraw.Draw(lowerCamelCase_       )
				draw.text(xy=(left_padding, top_padding)	,     text=lowerCamelCase_	,     fill=lowerCamelCase_	,     font=lowerCamelCase_       )
				return image






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				requires_backends(lowerCamelCase_	,     '''vision'''       )

				# Convert to PIL image if necessary
				lowercase__     				=    to_pil_image(lowerCamelCase_       )

				lowercase__     				=    render_text(lowerCamelCase_	,     **lowerCamelCase_       )
				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(lowerCamelCase_       )

				if infer_channel_dimension_format(lowerCamelCase_       ) == ChannelDimension.LAST:
								lowercase__     				=    to_channel_dimension_format(lowerCamelCase_	,     ChannelDimension.LAST       )

				return new_image




class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""flattened_patches"""]




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

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								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        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.ndarray,						lowerCamelCase		:					int,						lowerCamelCase		:					dict,						**lowerCamelCase		:					Dict						):

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

								# convert to torch
								lowercase__     				=    to_channel_dimension_format(lowerCamelCase,						ChannelDimension.FIRST						)
								lowercase__     				=    torch.from_numpy(lowerCamelCase						)

								lowercase__				,		lowercase__     				=    patch_size['''height'''], patch_size['''width''']
								lowercase__				,		lowercase__     				=    get_image_size(lowerCamelCase						)

								# 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						),						lowerCamelCase						),						1						)
								lowercase__     				=    max(min(math.floor(scale * image_width / patch_width						),						lowerCamelCase						),						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=lowerCamelCase,						antialias=lowerCamelCase,						).squeeze(0						)

								# [1, rows, columns, patch_height * patch_width * image_channels]
								lowercase__     				=    torch_extract_patches(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

								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(lowerCamelCase						).reshape([rows, 1]						).repeat(1,						lowerCamelCase						).reshape([rows * columns, 1]						)
								lowercase__     				=    torch.arange(lowerCamelCase						).reshape([1, columns]						).repeat(lowerCamelCase,						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(lowerCamelCase,						[0, 0, 0, max_patches - (rows * columns)]						).float()

								lowercase__     				=    to_numpy_array(lowerCamelCase						)

								return result




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.ndarray,						lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase		:					int						):

								'''simple docstring'''
								if image.dtype == np.uinta:
												lowercase__     				=    image.astype(np.floataa						)

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

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




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					ImageInput,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					bool = None,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Dict[str, int]] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						lowerCamelCase		:					ChannelDimension = ChannelDimension.FIRST,						**lowerCamelCase		:					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''',						lowerCamelCase						) is not None:
												raise ValueError('''data_format is not an accepted input as the outputs are '''						)

								lowercase__     				=    make_list_of_images(lowerCamelCase						)

								if not valid_images(lowerCamelCase						):
												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(lowerCamelCase						) for image in images]

								# All transformations expect numpy arrays.
								lowercase__     				=    [to_numpy_array(lowerCamelCase						) 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''',						lowerCamelCase						)
												lowercase__     				=    kwargs.pop('''font_path''',						lowerCamelCase						)

												if isinstance(lowerCamelCase,						lowerCamelCase						):
																lowercase__     				=    [header_text] * len(lowerCamelCase						)

												lowercase__     				=    [
												    render_header(lowerCamelCase,						header_text[i],						font_bytes=lowerCamelCase,						font_path=lowerCamelCase						)
												    for i, image in enumerate(lowerCamelCase						)
												]

								if do_normalize:
												lowercase__     				=    [self.normalize(image=lowerCamelCase						) for image in images]

								# convert to torch tensor and permute
								lowercase__     				=    [
								    self.extract_flattened_patches(image=lowerCamelCase,						max_patches=lowerCamelCase,						patch_size=lowerCamelCase						)
								    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=lowerCamelCase						)

								return encoded_outputs












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_	,     '''wb'''       ) as f:
								f.write(json.dumps(lowerCamelCase_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_map_filter()




