

"""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							:    Any		=    False


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







def 						_a				(      )    ->				Union[str, Any]:
							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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							requires_backends(_SCREAMING_SNAKE_CASE				, ["""torch"""]				)
							_check_torch_version()

							snake_case_           =		image_tensor.unsqueeze(0				)
							snake_case_           =		torch.nn.functional.unfold(_SCREAMING_SNAKE_CASE				, (patch_height, patch_width)				, stride=(patch_height, patch_width)				)
							snake_case_           =		patches.reshape(image_tensor.size(0				)				, image_tensor.size(1				)				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, -1				)
							snake_case_           =		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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 36				, _SCREAMING_SNAKE_CASE = "black"				, _SCREAMING_SNAKE_CASE = "white"				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = 5				, _SCREAMING_SNAKE_CASE = None				, _SCREAMING_SNAKE_CASE = None				, )    ->				Image.Image:
							requires_backends(_SCREAMING_SNAKE_CASE				, """vision"""				)
							# Add new lines so that each line is no more than 80 characters.

							snake_case_           =		textwrap.TextWrapper(width=80				)
							snake_case_           =		wrapper.wrap(text=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		"""\n""".join(_SCREAMING_SNAKE_CASE				)

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

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

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				)    ->				Any:
							requires_backends(_SCREAMING_SNAKE_CASE				, """vision"""				)

							# Convert to PIL image if necessary
							snake_case_           =		to_pil_image(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		render_text(_SCREAMING_SNAKE_CASE				, **_SCREAMING_SNAKE_CASE				)
							snake_case_           =		max(header_image.width				, image.width				)

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

							snake_case_           =		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
							snake_case_           =		to_numpy_array(_SCREAMING_SNAKE_CASE				)

							if infer_channel_dimension_format(_SCREAMING_SNAKE_CASE				) == ChannelDimension.LAST:
														snake_case_           =		to_channel_dimension_format(_SCREAMING_SNAKE_CASE				, ChannelDimension.LAST				)

							return new_image





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Tuple							      =    ["""flattened_patches"""]



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



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      np.ndarray		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      dict		,       **UpperCAmelCase_							:      List[Any]       )					->np.ndarray:



														"""simple docstring"""



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

														# convert to torch
														snake_case_           =		to_channel_dimension_format(UpperCAmelCase_		,       ChannelDimension.FIRST       )
														snake_case_           =		torch.from_numpy(UpperCAmelCase_       )

														snake_case_       ,						snake_case_           =		patch_size["""height"""], patch_size["""width"""]
														snake_case_       ,						snake_case_           =		get_image_size(UpperCAmelCase_       )

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

														snake_case_           =		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]
														snake_case_           =		torch_extract_patches(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

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

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

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

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

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

														snake_case_           =		to_numpy_array(UpperCAmelCase_       )

														return result



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      np.ndarray		,       UpperCAmelCase_							:      Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase_							:      Union[str, Any]       )					->np.ndarray:



														"""simple docstring"""



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

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

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



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       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]		,       )					->ImageInput:



														"""simple docstring"""



														snake_case_           =		do_normalize if do_normalize is not None else self.do_normalize
														snake_case_           =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
														snake_case_           =		patch_size if patch_size is not None else self.patch_size
														snake_case_           =		max_patches if max_patches is not None else self.max_patches
														snake_case_           =		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 """       )

														snake_case_           =		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:
																					snake_case_           =		[convert_to_rgb(UpperCAmelCase_       ) for image in images]

														# All transformations expect numpy arrays.
														snake_case_           =		[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."""       )
																					snake_case_           =		kwargs.pop("""font_bytes"""		,       UpperCAmelCase_       )
																					snake_case_           =		kwargs.pop("""font_path"""		,       UpperCAmelCase_       )

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

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

														if do_normalize:
																					snake_case_           =		[self.normalize(image=UpperCAmelCase_       ) for image in images]

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

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

														snake_case_           =		BatchFeature(
														    data={"""flattened_patches""": images, """attention_mask""": attention_masks}		,       tensor_type=UpperCAmelCase_       )

														return encoded_outputs







"""simple docstring"""
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Dict=3		,       UpperCAmelCase_							:      Tuple=18		,       UpperCAmelCase_							:      Optional[Any]=30		,       UpperCAmelCase_							:      str=400		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Optional[Any]=True		,       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""height""": 18, """width""": 18}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		image_size
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]       ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ImageGPTImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """clusters"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""height""": 18, """width""": 18}       )

														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict		,       size=42       )
														self.assertEqual(image_processor.size		,       {"""height""": 42, """width""": 42}       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														snake_case_           =		json.loads(image_processor.to_json_string()       )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       obj[key]       )       )
																					else:
																												self.assertEqual(obj[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_           =		os.path.join(UpperCAmelCase_		,       """image_processor.json"""       )
																					image_processor_first.to_json_file(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_json_file(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_pretrained(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							@unittest.skip("""ImageGPT requires clusters at initialization"""       )
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														pass







def 						_a				(      )    ->				str:
							snake_case_           =		load_dataset("""hf-internal-testing/fixtures_image_utils"""				, split="""test"""				)

							snake_case_           =		Image.open(dataset[4]["""file"""]				)
							snake_case_           =		Image.open(dataset[5]["""file"""]				)

							snake_case_           =		[imagea, imagea]

							return images





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

														snake_case_           =		prepare_images()

														# test non-batched
														snake_case_           =		image_processing(images[0]		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (1, 1_024)       )

														snake_case_           =		[306, 191, 191]
														self.assertEqual(encoding.input_ids[0, :3].tolist()		,       UpperCAmelCase_       )

														# test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (2, 1_024)       )

														snake_case_           =		[303, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()		,       UpperCAmelCase_       )



