



"""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:
			UpperCamelCase     =							False


UpperCamelCase     =							logging.get_logger(__name__)
UpperCamelCase     =							"""ybelkada/fonts"""

def       _lowerCamelCase				(							)	-> int:






	"""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       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       Dict     )	-> Optional[int]:






	"""simple docstring"""





	requires_backends(UpperCAmelCase_,	["torch"]     )
	_check_torch_version()

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

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       int = 36,	UpperCAmelCase_ :       str = "black",	UpperCAmelCase_ :       str = "white",	UpperCAmelCase_ :       int = 5,	UpperCAmelCase_ :       int = 5,	UpperCAmelCase_ :       int = 5,	UpperCAmelCase_ :       int = 5,	UpperCAmelCase_ :       Optional[bytes] = None,	UpperCAmelCase_ :       Optional[str] = None,	)	-> Image.Image:






	"""simple docstring"""





	requires_backends(UpperCAmelCase_,	"vision"     )
	# Add new lines so that each line is no more than 80 characters.

	A__											=					textwrap.TextWrapper(width=80     )
	A__											=					wrapper.wrap(text=UpperCAmelCase_     )
	A__											=					"\n".join(UpperCAmelCase_     )

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

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

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

def       _lowerCamelCase				(							UpperCAmelCase_ :       np.ndarray,	UpperCAmelCase_ :       str,	**UpperCAmelCase_ :       Dict     )	-> Optional[Any]:






	"""simple docstring"""





	requires_backends(UpperCAmelCase_,	"vision"     )

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

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

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

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

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

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

	return new_image






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     ["flattened_patches"]


			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 2048						,	SCREAMING_SNAKE_CASE__ = False						,	**SCREAMING_SNAKE_CASE__						,	)      ->	None:
				super().__init__(**SCREAMING_SNAKE_CASE__		)
				A__											=					patch_size if patch_size is not None else {"height": 16, "width": 16}
				A__											=					do_normalize
				A__											=					do_convert_rgb
				A__											=					max_patches
				A__											=					is_vqa


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	np.ndarray:
				requires_backends(self.extract_flattened_patches						,	"torch"		)
				_check_torch_version()

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

				A__							,							A__											=					patch_size["height"], patch_size["width"]
				A__							,							A__											=					get_image_size(SCREAMING_SNAKE_CASE__		)

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

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

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

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

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

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

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

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

				A__											=					to_numpy_array(SCREAMING_SNAKE_CASE__		)

				return result


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__		)      ->	np.ndarray:
				if image.dtype == np.uinta:
					A__											=					image.astype(np.floataa		)

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

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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST						,	**SCREAMING_SNAKE_CASE__						,	)      ->	ImageInput:
				A__											=					do_normalize if do_normalize is not None else self.do_normalize
				A__											=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
				A__											=					patch_size if patch_size is not None else self.patch_size
				A__											=					max_patches if max_patches is not None else self.max_patches
				A__											=					self.is_vqa

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

				A__											=					make_list_of_images(SCREAMING_SNAKE_CASE__		)

				if not valid_images(SCREAMING_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:
					A__											=					[convert_to_rgb(SCREAMING_SNAKE_CASE__		) for image in images]

				# All transformations expect numpy arrays.
				A__											=					[to_numpy_array(SCREAMING_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."		)
					A__											=					kwargs.pop("font_bytes"						,	SCREAMING_SNAKE_CASE__		)
					A__											=					kwargs.pop("font_path"						,	SCREAMING_SNAKE_CASE__		)

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

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

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

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

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

				A__											=					BatchFeature(
				    data={"flattened_patches": images, "attention_mask": attention_masks}						,	tensor_type=SCREAMING_SNAKE_CASE__		)

				return encoded_outputs









"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					[
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				A__											=					os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]		)
				with open(self.vocab_file						,	"w"						,	encoding="utf-8"		) as vocab_writer:
					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

				A__											=					{
				    "do_resize": True,
				    "size": 20,
				    "do_center_crop": True,
				    "crop_size": 18,
				    "do_normalize": True,
				    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
				    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
				}
				A__											=					os.path.join(self.tmpdirname						,	SCREAMING_SNAKE_CASE__		)
				with open(self.image_processor_file						,	"w"						,	encoding="utf-8"		) as fp:
					json.dump(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				return BertTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				return BertTokenizerFast.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]
				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]
				return image_inputs


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.get_tokenizer()
				A__											=					self.get_rust_tokenizer()
				A__											=					self.get_image_processor()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_slow.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname						,	use_fast=SCREAMING_SNAKE_CASE__		)

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_fast.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname		)

				self.assertEqual(processor_slow.tokenizer.get_vocab()						,	tokenizer_slow.get_vocab()		)
				self.assertEqual(processor_fast.tokenizer.get_vocab()						,	tokenizer_fast.get_vocab()		)
				self.assertEqual(tokenizer_slow.get_vocab()						,	tokenizer_fast.get_vocab()		)
				self.assertIsInstance(processor_slow.tokenizer						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				self.assertEqual(processor_slow.image_processor.to_json_string()						,	image_processor.to_json_string()		)
				self.assertEqual(processor_fast.image_processor.to_json_string()						,	image_processor.to_json_string()		)
				self.assertIsInstance(processor_slow.image_processor						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					AlignProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					AlignProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)

				for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()						,	input_processor[key].sum()						,	delta=1e-2		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	padding="max_length"						,	max_length=64		)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(list(inputs.keys()		)						,	["input_ids", "token_type_ids", "attention_mask", "pixel_values"]		)

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(list(inputs.keys()		)						,	processor.model_input_names		)




