






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 					lowercase__				(					):


					'''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 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							List[str]					,	__snake_case   :							Any  ):


					'''simple docstring'''


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

					UpperCAmelCase_   :				List[str]						=						image_tensor.unsqueeze(0  )
					UpperCAmelCase_   :				int						=						torch.nn.functional.unfold(__snake_case					,	(patch_height, patch_width)					,	stride=(patch_height, patch_width)  )
					UpperCAmelCase_   :				Dict						=						patches.reshape(image_tensor.size(0  )					,	image_tensor.size(1  )					,	__snake_case					,	__snake_case					,	-1  )
					UpperCAmelCase_   :				Optional[int]						=						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 					lowercase__				(					__snake_case   :							str					,	__snake_case   :							int = 36					,	__snake_case   :							str = "black"					,	__snake_case   :							str = "white"					,	__snake_case   :							int = 5					,	__snake_case   :							int = 5					,	__snake_case   :							int = 5					,	__snake_case   :							int = 5					,	__snake_case   :							Optional[bytes] = None					,	__snake_case   :							Optional[str] = None					,	):


					'''simple docstring'''


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

					UpperCAmelCase_   :				str						=						textwrap.TextWrapper(width=80  )
					UpperCAmelCase_   :				Dict						=						wrapper.wrap(text=__snake_case  )
					UpperCAmelCase_   :				int						=						'\n'.join(__snake_case  )

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

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

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




def 					lowercase__				(					__snake_case   :							np.ndarray					,	__snake_case   :							str					,	**__snake_case   :							List[Any]  ):


					'''simple docstring'''


					requires_backends(__snake_case					,	'vision'  )

					# Convert to PIL image if necessary
					UpperCAmelCase_   :				List[Any]						=						to_pil_image(__snake_case  )

					UpperCAmelCase_   :				Dict						=						render_text(__snake_case					,	**__snake_case  )
					UpperCAmelCase_   :				List[str]						=						max(header_image.width					,	image.width  )

					UpperCAmelCase_   :				int						=						int(image.height * (new_width / image.width)  )
					UpperCAmelCase_   :				Any						=						int(header_image.height * (new_width / header_image.width)  )

					UpperCAmelCase_   :				List[Any]						=						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_   :				int						=						to_numpy_array(__snake_case  )

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

					return new_image






class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  ['''flattened_patches''']



		def __init__(							self    ,		_UpperCamelCase = True    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 2_0_4_8    ,		_UpperCamelCase = False    ,		**_UpperCamelCase    ,		)     ->							None:
							super().__init__(**_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[int]						=						patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
							UpperCAmelCase_   :				int						=						do_normalize
							UpperCAmelCase_   :				Tuple						=						do_convert_rgb
							UpperCAmelCase_   :				str						=						max_patches
							UpperCAmelCase_   :				Any						=						is_vqa



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							np.ndarray:
							requires_backends(self.extract_flattened_patches    ,		'torch'       )
							_check_torch_version()

							# convert to torch
							UpperCAmelCase_   :				int						=						to_channel_dimension_format(_UpperCamelCase    ,		ChannelDimension.FIRST       )
							UpperCAmelCase_   :				Optional[int]						=						torch.from_numpy(_UpperCamelCase       )

							UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						patch_size['height'], patch_size['width']
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						get_image_size(_UpperCamelCase       )

							# maximize scale s.t.
							UpperCAmelCase_   :				Dict						=						math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)       )
							UpperCAmelCase_   :				List[Any]						=						max(min(math.floor(scale * image_height / patch_height       )    ,		_UpperCamelCase       )    ,		1       )
							UpperCAmelCase_   :				Union[str, Any]						=						max(min(math.floor(scale * image_width / patch_width       )    ,		_UpperCamelCase       )    ,		1       )
							UpperCAmelCase_   :				Dict						=						max(num_feasible_rows * patch_height    ,		1       )
							UpperCAmelCase_   :				Union[str, Any]						=						max(num_feasible_cols * patch_width    ,		1       )

							UpperCAmelCase_   :				Optional[Any]						=						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]
							UpperCAmelCase_   :				Optional[int]						=						torch_extract_patches(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=						patches.shape
							UpperCAmelCase_   :				List[Any]						=						patches_shape[1]
							UpperCAmelCase_   :				Dict						=						patches_shape[2]
							UpperCAmelCase_   :				Any						=						patches_shape[3]

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

							# [rows * columns, 1]
							UpperCAmelCase_   :				Union[str, Any]						=						torch.arange(_UpperCamelCase       ).reshape([rows, 1]       ).repeat(1    ,		_UpperCamelCase       ).reshape([rows * columns, 1]       )
							UpperCAmelCase_   :				Union[str, Any]						=						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]
							UpperCAmelCase_   :				Tuple						=						row_ids.to(torch.floataa       )
							UpperCAmelCase_   :				List[Any]						=						col_ids.to(torch.floataa       )

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

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

							UpperCAmelCase_   :				int						=						to_numpy_array(_UpperCamelCase       )

							return result



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		**_UpperCamelCase       )     ->							np.ndarray:
							if image.dtype == np.uinta:
												UpperCAmelCase_   :				List[str]						=						image.astype(np.floataa       )

							# take mean across the whole `image`
							UpperCAmelCase_   :				List[str]						=						np.mean(_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						np.std(_UpperCamelCase       )
							UpperCAmelCase_   :				Optional[Any]						=						max(_UpperCamelCase    ,		1.0 / math.sqrt(np.prod(image.shape       )       )       )

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = ChannelDimension.FIRST    ,		**_UpperCamelCase    ,		)     ->							ImageInput:
							UpperCAmelCase_   :				str						=						do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_   :				Optional[int]						=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCAmelCase_   :				int						=						patch_size if patch_size is not None else self.patch_size
							UpperCAmelCase_   :				List[str]						=						max_patches if max_patches is not None else self.max_patches
							UpperCAmelCase_   :				List[str]						=						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 '       )

							UpperCAmelCase_   :				Dict						=						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:
												UpperCAmelCase_   :				List[str]						=						[convert_to_rgb(_UpperCamelCase       ) for image in images]

							# All transformations expect numpy arrays.
							UpperCAmelCase_   :				int						=						[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.'       )
												UpperCAmelCase_   :				Union[str, Any]						=						kwargs.pop('font_bytes'    ,		_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						kwargs.pop('font_path'    ,		_UpperCamelCase       )

												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				Union[str, Any]						=						[header_text] * len(_UpperCamelCase       )

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

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

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

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

							UpperCAmelCase_   :				List[str]						=						BatchFeature(
							    data={'flattened_patches': images, 'attention_mask': attention_masks}    ,		tensor_type=_UpperCamelCase       )

							return encoded_outputs














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase     							=						{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__UpperCAmelCase     							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






