"""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__      						=  False


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





def    lowerCAmelCase__    (			)					->				Optional[Any]:





						"""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       :				Dict ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Optional[Any]					)					->				List[str]:





						"""simple docstring"""






						requires_backends(_UpperCamelCase ,			['torch']					)
						_check_torch_version()

						snake_case								=			image_tensor.unsqueeze(0					)
						snake_case								=			torch.nn.functional.unfold(_UpperCamelCase ,			(patch_height, patch_width) ,			stride=(patch_height, patch_width)					)
						snake_case								=			patches.reshape(image_tensor.size(0					) ,			image_tensor.size(1					) ,			_UpperCamelCase ,			_UpperCamelCase ,			-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    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				int = 3_6 ,			_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.

						snake_case								=			textwrap.TextWrapper(width=8_0					)
						snake_case								=			wrapper.wrap(text=_UpperCamelCase					)
						snake_case								=			'\n'.join(_UpperCamelCase					)

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

						# 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) ,			_UpperCamelCase					)					)
						snake_case					,snake_case					,snake_case					,snake_case								=			temp_draw.textbbox((0, 0) ,			_UpperCamelCase ,			_UpperCamelCase					)

						# 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) ,			_UpperCamelCase					)
						snake_case								=			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       :				Union[str, Any]					)					->				Tuple:





						"""simple docstring"""






						requires_backends(_UpperCamelCase ,			'vision'					)

						# Convert to PIL image if necessary
						snake_case								=			to_pil_image(_UpperCamelCase					)

						snake_case								=			render_text(_UpperCamelCase ,			**_UpperCamelCase					)
						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(_UpperCamelCase					)

						if infer_channel_dimension_format(_UpperCamelCase					) == ChannelDimension.LAST:
												snake_case								=			to_channel_dimension_format(_UpperCamelCase ,			ChannelDimension.LAST					)

						return new_image



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[Any]			  =						["""flattened_patches"""]

				def __init__(				self		,    lowerCAmelCase = True		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = 20_48		,    lowerCAmelCase = False		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase    )
										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 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

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

										# convert to torch
										snake_case								=			to_channel_dimension_format(lowerCAmelCase		,    ChannelDimension.FIRST    )
										snake_case								=			torch.from_numpy(lowerCAmelCase    )

										snake_case					,snake_case								=			patch_size['height'], patch_size['width']
										snake_case					,snake_case								=			get_image_size(lowerCAmelCase    )

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

										# [1, rows, columns, patch_height * patch_width * image_channels]
										snake_case								=			torch_extract_patches(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										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(lowerCAmelCase    ).reshape([rows, 1]    ).repeat(1		,    lowerCAmelCase    ).reshape([rows * columns, 1]    )
										snake_case								=			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]
										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(lowerCAmelCase		,    [0, 0, 0, max_patches - (rows * columns)]    ).float()

										snake_case								=			to_numpy_array(lowerCAmelCase    )

										return result

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase    ):






										"""simple docstring"""

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

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

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = ChannelDimension.FIRST		,    **lowerCAmelCase		,    ):






										"""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'		,    lowerCAmelCase    ) is not None:
																raise ValueError('data_format is not an accepted input as the outputs are '    )

										snake_case								=			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:
																snake_case								=			[convert_to_rgb(lowerCAmelCase    ) for image in images]

										# All transformations expect numpy arrays.
										snake_case								=			[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.'    )
																snake_case								=			kwargs.pop('font_bytes'		,    lowerCAmelCase    )
																snake_case								=			kwargs.pop('font_path'		,    lowerCAmelCase    )

																if isinstance(lowerCAmelCase		,    lowerCAmelCase    ):
																						snake_case								=			[header_text] * len(lowerCAmelCase    )

																snake_case								=			[
																    render_header(lowerCAmelCase		,    header_text[i]		,    font_bytes=lowerCAmelCase		,    font_path=lowerCAmelCase    )
																    for i, image in enumerate(lowerCAmelCase    )
																]

										if do_normalize:
																snake_case								=			[self.normalize(image=lowerCAmelCase    ) for image in images]

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

										return encoded_outputs



"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
					from .camera import create_pan_cameras
					from .pipeline_shap_e import ShapEPipeline
					from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
					from .renderer import (
					    BoundingBoxVolume,
					    ImportanceRaySampler,
					    MLPNeRFModelOutput,
					    MLPNeRSTFModel,
					    ShapEParamsProjModel,
					    ShapERenderer,
					    StratifiedRaySampler,
					    VoidNeRFModel,
					)


