

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      				=     False


lowerCamelCase      				=     logging.get_logger(__name__)
lowerCamelCase      				=     """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    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							image_tensor.unsqueeze(0      )
					__lowerCAmelCase					=							torch.nn.functional.unfold(UpperCAmelCase__		,					(patch_height, patch_width)		,					stride=(patch_height, patch_width)      )
					__lowerCAmelCase					=							patches.reshape(image_tensor.size(0      )		,					image_tensor.size(1      )		,					UpperCAmelCase__		,					UpperCAmelCase__		,					-1      )
					__lowerCAmelCase					=							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    (  UpperCAmelCase__		,					UpperCAmelCase__ = 36		,					UpperCAmelCase__ = "black"		,					UpperCAmelCase__ = "white"		,					UpperCAmelCase__ = 5		,					UpperCAmelCase__ = 5		,					UpperCAmelCase__ = 5		,					UpperCAmelCase__ = 5		,					UpperCAmelCase__ = None		,					UpperCAmelCase__ = None		,					):

					"""simple docstring"""






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

					__lowerCAmelCase					=							textwrap.TextWrapper(width=80      )
					__lowerCAmelCase					=							wrapper.wrap(text=UpperCAmelCase__      )
					__lowerCAmelCase					=							"\n".join(UpperCAmelCase__      )

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

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

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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					**UpperCAmelCase__      ):

					"""simple docstring"""






					requires_backends(UpperCAmelCase__		,					'vision'      )

					# Convert to PIL image if necessary
					__lowerCAmelCase					=							to_pil_image(UpperCAmelCase__      )

					__lowerCAmelCase					=							render_text(UpperCAmelCase__		,					**UpperCAmelCase__      )
					__lowerCAmelCase					=							max(header_image.width		,					image.width      )

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

					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							to_numpy_array(UpperCAmelCase__      )

					if infer_channel_dimension_format(UpperCAmelCase__      ) == ChannelDimension.LAST:
										__lowerCAmelCase					=							to_channel_dimension_format(UpperCAmelCase__		,					ChannelDimension.LAST      )

					return new_image





class     snake_case_			(				SCREAMING_SNAKE_CASE__							):


							"""simple docstring"""





							__UpperCAmelCase       =["""flattened_patches"""]




							def __init__( self					,						_A = True					,						_A = True					,						_A = None					,						_A = 2_0_4_8					,						_A = False					,						**_A					,						):
												super().__init__(**snake_case__							)
												__lowerCAmelCase					=							patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							do_convert_rgb
												__lowerCAmelCase					=							max_patches
												__lowerCAmelCase					=							is_vqa




							def        A__  ( self					,						_A					,						_A					,						_A					,						**_A							):
												requires_backends(self.extract_flattened_patches					,						'torch'							)
												_check_torch_version()

												# convert to torch
												__lowerCAmelCase					=							to_channel_dimension_format(snake_case__					,						ChannelDimension.FIRST							)
												__lowerCAmelCase					=							torch.from_numpy(snake_case__							)

												__lowerCAmelCase					=							patch_size["height"], patch_size["width"]
												__lowerCAmelCase					=							get_image_size(snake_case__							)

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

												__lowerCAmelCase					=							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]
												__lowerCAmelCase					=							torch_extract_patches(snake_case__					,						snake_case__					,						snake_case__							)

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

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

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

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

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

												__lowerCAmelCase					=							to_numpy_array(snake_case__							)

												return result




							def        A__  ( self					,						_A					,						_A = None					,						**_A							):
												if image.dtype == np.uinta:
																	__lowerCAmelCase					=							image.astype(np.floataa							)

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

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




							def        A__  ( self					,						_A					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = ChannelDimension.FIRST					,						**_A					,						):
												__lowerCAmelCase					=							do_normalize if do_normalize is not None else self.do_normalize
												__lowerCAmelCase					=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
												__lowerCAmelCase					=							patch_size if patch_size is not None else self.patch_size
												__lowerCAmelCase					=							max_patches if max_patches is not None else self.max_patches
												__lowerCAmelCase					=							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 '							)

												__lowerCAmelCase					=							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:
																	__lowerCAmelCase					=							[convert_to_rgb(snake_case__							) for image in images]

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

																	if isinstance(snake_case__					,						snake_case__							):
																						__lowerCAmelCase					=							[header_text] * len(snake_case__							)

																	__lowerCAmelCase					=							[
																	    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:
																	__lowerCAmelCase					=							[self.normalize(image=snake_case__							) for image in images]

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

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

												__lowerCAmelCase					=							BatchFeature(
												    data={'flattened_patches': images, 'attention_mask': attention_masks}					,						tensor_type=snake_case__							)

												return encoded_outputs









from typing import TYPE_CHECKING

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


lowerCamelCase      				=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecAudioForAudioFrameClassification''',
		    '''Data2VecAudioForCTC''',
		    '''Data2VecAudioForSequenceClassification''',
		    '''Data2VecAudioForXVector''',
		    '''Data2VecAudioModel''',
		    '''Data2VecAudioPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecTextForCausalLM''',
		    '''Data2VecTextForMaskedLM''',
		    '''Data2VecTextForMultipleChoice''',
		    '''Data2VecTextForQuestionAnswering''',
		    '''Data2VecTextForSequenceClassification''',
		    '''Data2VecTextForTokenClassification''',
		    '''Data2VecTextModel''',
		    '''Data2VecTextPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecVisionForImageClassification''',
		    '''Data2VecVisionForMaskedImageModeling''',
		    '''Data2VecVisionForSemanticSegmentation''',
		    '''Data2VecVisionModel''',
		    '''Data2VecVisionPreTrainedModel''',
		]

if is_tf_available():
		lowerCamelCase      				=     [
		    '''TFData2VecVisionForImageClassification''',
		    '''TFData2VecVisionForSemanticSegmentation''',
		    '''TFData2VecVisionModel''',
		    '''TFData2VecVisionPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

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






