
"""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:
							lowerCamelCase_  :	List[str] 				=					False


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)
lowerCamelCase_  :	Optional[int] 				=					"""ybelkada/fonts"""







def     A__   (  )					->		Dict:
				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  )					->		Optional[int]:
				requires_backends(lowerCamelCase       ,      ["""torch"""]  )
				_check_torch_version()

				UpperCamelCase_:							Tuple		=  image_tensor.unsqueeze(0  )
				UpperCamelCase_:							Any		=  torch.nn.functional.unfold(lowerCamelCase       ,      (patch_height, patch_width)       ,      stride=(patch_height, patch_width)  )
				UpperCamelCase_:							int		=  patches.reshape(image_tensor.size(0  )       ,      image_tensor.size(1  )       ,      lowerCamelCase       ,      lowerCamelCase       ,      -1  )
				UpperCamelCase_:							Any		=  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       ,      )					->		Image.Image:
				requires_backends(lowerCamelCase       ,      """vision"""  )
				# Add new lines so that each line is no more than 80 characters.

				UpperCamelCase_:							List[str]		=  textwrap.TextWrapper(width=80  )
				UpperCamelCase_:							Optional[int]		=  wrapper.wrap(text=lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  """\n""".join(lowerCamelCase  )

				if font_bytes is not None and font_path is None:
								UpperCamelCase_:							List[Any]		=  io.BytesIO(lowerCamelCase  )
				elif font_path is not None:
								UpperCamelCase_:							List[Any]		=  font_path
				else:
								UpperCamelCase_:							Tuple		=  hf_hub_download(lowerCamelCase       ,      """Arial.TTF"""  )
				UpperCamelCase_:							Optional[Any]		=  ImageFont.truetype(lowerCamelCase       ,      encoding="""UTF-8"""       ,      size=lowerCamelCase  )

				# Use a temporary canvas to determine the width and height in pixels when
				# rendering the text.
				UpperCamelCase_:							str		=  ImageDraw.Draw(Image.new("""RGB"""       ,      (1, 1)       ,      lowerCamelCase  )  )
				UpperCamelCase_:							Optional[int]		=  temp_draw.textbbox((0, 0)       ,      lowerCamelCase       ,      lowerCamelCase  )

				# Create the actual image with a bit of padding around the text.
				UpperCamelCase_:							Optional[int]		=  text_width + left_padding + right_padding
				UpperCamelCase_:							List[str]		=  text_height + top_padding + bottom_padding
				UpperCamelCase_:							Union[str, Any]		=  Image.new("""RGB"""       ,      (image_width, image_height)       ,      lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  ImageDraw.Draw(lowerCamelCase  )
				draw.text(xy=(left_padding, top_padding)       ,      text=lowerCamelCase       ,      fill=lowerCamelCase       ,      font=lowerCamelCase  )
				return image







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )					->		List[str]:
				requires_backends(lowerCamelCase       ,      """vision"""  )

				# Convert to PIL image if necessary
				UpperCamelCase_:							List[str]		=  to_pil_image(lowerCamelCase  )

				UpperCamelCase_:							Union[str, Any]		=  render_text(lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  max(header_image.width       ,      image.width  )

				UpperCamelCase_:							Tuple		=  int(image.height * (new_width / image.width)  )
				UpperCamelCase_:							Dict		=  int(header_image.height * (new_width / header_image.width)  )

				UpperCamelCase_:							str		=  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
				UpperCamelCase_:							Optional[Any]		=  to_numpy_array(lowerCamelCase  )

				if infer_channel_dimension_format(lowerCamelCase  ) == ChannelDimension.LAST:
								UpperCamelCase_:							Tuple		=  to_channel_dimension_format(lowerCamelCase       ,      ChannelDimension.LAST  )

				return new_image





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

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

			def __init__(    self	:    int  ,		snake_case_	:    bool = True  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    int = 2048  ,		snake_case_	:    bool = False  ,		**snake_case_	:    Any  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							int		=  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							List[Any]		=  do_convert_rgb
							UpperCamelCase_:							Tuple		=  max_patches
							UpperCamelCase_:							Tuple		=  is_vqa

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    int  ,		snake_case_	:    dict  ,		**snake_case_	:    Tuple ):
							requires_backends(self.extract_flattened_patches  ,		"""torch""" )
							_check_torch_version()

							# convert to torch
							UpperCamelCase_:							int		=  to_channel_dimension_format(snake_case_  ,		ChannelDimension.FIRST )
							UpperCamelCase_:							List[str]		=  torch.from_numpy(snake_case_ )

							UpperCamelCase_:							List[Any]		=  patch_size["""height"""], patch_size["""width"""]
							UpperCamelCase_:							Tuple		=  get_image_size(snake_case_ )

							# maximize scale s.t.
							UpperCamelCase_:							List[Any]		=  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
							UpperCamelCase_:							Any		=  max(min(math.floor(scale * image_height / patch_height )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							List[str]		=  max(min(math.floor(scale * image_width / patch_width )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							int		=  max(num_feasible_rows * patch_height  ,		1 )
							UpperCamelCase_:							Optional[Any]		=  max(num_feasible_cols * patch_width  ,		1 )

							UpperCamelCase_:							str		=  torch.nn.functional.interpolate(
							    image.unsqueeze(0 )  ,		size=(resized_height, resized_width)  ,		mode="""bilinear"""  ,		align_corners=snake_case_  ,		antialias=snake_case_  ,		).squeeze(0 )

							# [1, rows, columns, patch_height * patch_width * image_channels]
							UpperCamelCase_:							List[str]		=  torch_extract_patches(snake_case_  ,		snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  patches.shape
							UpperCamelCase_:							List[str]		=  patches_shape[1]
							UpperCamelCase_:							Optional[Any]		=  patches_shape[2]
							UpperCamelCase_:							List[str]		=  patches_shape[3]

							# [rows * columns, patch_height * patch_width * image_channels]
							UpperCamelCase_:							Union[str, Any]		=  patches.reshape([rows * columns, depth] )

							# [rows * columns, 1]
							UpperCamelCase_:							Optional[Any]		=  torch.arange(snake_case_ ).reshape([rows, 1] ).repeat(1  ,		snake_case_ ).reshape([rows * columns, 1] )
							UpperCamelCase_:							Optional[int]		=  torch.arange(snake_case_ ).reshape([1, columns] ).repeat(snake_case_  ,		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]
							UpperCamelCase_:							Union[str, Any]		=  row_ids.to(torch.floataa )
							UpperCamelCase_:							str		=  col_ids.to(torch.floataa )

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

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

							UpperCamelCase_:							List[Any]		=  to_numpy_array(snake_case_ )

							return result

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Tuple ):
							if image.dtype == np.uinta:
											UpperCamelCase_:							List[str]		=  image.astype(np.floataa )

							# take mean across the whole `image`
							UpperCamelCase_:							str		=  np.mean(snake_case_ )
							UpperCamelCase_:							str		=  np.std(snake_case_ )
							UpperCamelCase_:							str		=  max(snake_case_  ,		1.0 / math.sqrt(np.prod(image.shape ) ) )

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




			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    ImageInput  ,		snake_case_	:    Optional[str] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Optional[Dict[str, int]] = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Tuple		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Tuple		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCamelCase_:							Optional[Any]		=  patch_size if patch_size is not None else self.patch_size
							UpperCamelCase_:							Optional[int]		=  max_patches if max_patches is not None else self.max_patches
							UpperCamelCase_:							Tuple		=  self.is_vqa

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

							UpperCamelCase_:							Dict		=  make_list_of_images(snake_case_ )

							if not valid_images(snake_case_ ):
											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:
											UpperCamelCase_:							str		=  [convert_to_rgb(snake_case_ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Union[str, Any]		=  [to_numpy_array(snake_case_ ) for image in images]

							if is_vqa:
											if header_text is None:
															raise ValueError("""A header text must be provided for VQA models.""" )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_bytes"""  ,		snake_case_ )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_path"""  ,		snake_case_ )

											if isinstance(snake_case_  ,		snake_case_ ):
															UpperCamelCase_:							str		=  [header_text] * len(snake_case_ )

											UpperCamelCase_:							str		=  [
											    render_header(snake_case_  ,		header_text[i]  ,		font_bytes=snake_case_  ,		font_path=snake_case_ )
											    for i, image in enumerate(snake_case_ )
											]

							if do_normalize:
											UpperCamelCase_:							Union[str, Any]		=  [self.normalize(image=snake_case_ ) for image in images]

							# convert to torch tensor and permute
							UpperCamelCase_:							str		=  [
							    self.extract_flattened_patches(image=snake_case_  ,		max_patches=snake_case_  ,		patch_size=snake_case_ )
							    for image in images
							]

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

							UpperCamelCase_:							Optional[Any]		=  BatchFeature(
							    data={"""flattened_patches""": images, """attention_mask""": attention_masks}  ,		tensor_type=snake_case_ )

							return encoded_outputs








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

lowerCamelCase_				,    lowerCamelCase_  :	Tuple 				=					os.path.split(__file__)
lowerCamelCase_  :	int 				=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, Any]		=  [
				    (read, {"""length""": SMALL_TEST}),
				    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
				    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (read, {"""length""": SMALL_TEST}),
				    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
				    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10_00}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("""generating dataset"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  generate_example_dataset(
								    os.path.join(lowerCamelCase       ,      """dataset.arrow"""  )       ,      lowerCamelCase       ,      num_examples=lowerCamelCase       ,      seq_shapes={"""list""": (1_00,)}       ,      )
								print("""first set of iterations"""  )
								for func, kwargs in functions:
												print(func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

								print("""shuffling dataset"""  )
								UpperCamelCase_:							Dict		=  dataset.shuffle()
								print("""Second set of iterations (after shuffling"""  )
								for func, kwargs in functions_shuffled:
												print("""shuffled """       ,      func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(
												    lowerCamelCase       ,      **lowerCamelCase  )

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


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






