






"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


SCREAMING_SNAKE_CASE						:						List[Any]					   =       logging.get_logger(__name__)



def  __lowerCamelCase					(					lowerCAmelCase__				):


				if isinstance(lowerCamelCase__       ,np.ndarray				):
								return list(tensor.shape				)

				A__							 =       tf.shape(lowerCamelCase__				)

				if tensor.shape == tf.TensorShape(lowerCamelCase__				):
								return dynamic

				A__							 =       tensor.shape.as_list()

				return [dynamic[i] if s is None else s for i, s in enumerate(lowerCamelCase__				)]



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = None       ,lowerCAmelCase__ = None				):
				return tf.nn.softmax(logits=logits + 1E-9       ,axis=lowerCamelCase__       ,name=lowerCamelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=1E-5       ,lowerCAmelCase__=-1				):


				if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCamelCase__       ,lowerCamelCase__				):
								raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'				)

				# Get mean and variance on the axis to be normalized
				A__							 =       tf.nn.moments(lowerCamelCase__       ,axes=[axis]       ,keepdims=lowerCamelCase__				)

				if axis != -1:
								# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
								# on every dimension except axis
								A__							 =       [1] * inputs.shape.rank
								A__							 =       shape_list(lowerCamelCase__				)[axis]
								A__							 =       tf.reshape(lowerCamelCase__       ,lowerCamelCase__				)
								A__							 =       tf.reshape(lowerCamelCase__       ,lowerCamelCase__				)

				# Compute layer normalization using the batch_normalization
				# function.
				A__							 =       tf.nn.batch_normalization(
				    lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,offset=lowerCamelCase__       ,scale=lowerCamelCase__       ,variance_epsilon=lowerCamelCase__       ,)
				return outputs



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=0       ,lowerCAmelCase__=-1				):


				if end_dim < 0:
								end_dim += input.shape.rank
				if start_dim < 0:
								start_dim += input.shape.rank

				if start_dim == end_dim:
								return input

				A__							 =       tf.shape(lowerCamelCase__				)
				A__							 =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
				A__							 =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]       ,axis=0				)
				return tf.reshape(lowerCamelCase__       ,lowerCamelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__				):


				if not isinstance(lowerCamelCase__       ,tf.Tensor				):
								A__							 =       tf.convert_to_tensor(lowerCamelCase__				)  # Catches stray NumPy inputs
				if encoder_attention_mask.shape.rank == 3:
								A__							 =       encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.shape.rank == 2:
								A__							 =       encoder_attention_mask[:, None, None, :]
				# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
				# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
				# /transformer/transformer_layers.py#L270
				# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
				# encoder_extended_attention_mask.transpose(-1, -2))
				A__							 =       (
				    tf.cast(1       ,encoder_attention_mask.dtype				) - encoder_extended_attention_mask
				) * encoder_extended_attention_mask.dtype.min

				return encoder_extended_attention_mask



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ = "input_ids"				):
				tf.debugging.assert_less(
				    lowerCamelCase__       ,tf.cast(lowerCamelCase__       ,dtype=tensor.dtype				)       ,message=(
				        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCamelCase__				)}) must be smaller than the embedding '''
				        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
				    )       ,)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       6_4512
				# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
				# because in that case even chunking the array would not make the saving
				# possible.
				A__							 =       [x for x in data if len(lowerCamelCase__				) > HDF5_OBJECT_HEADER_LIMIT]

				# Expecting this to never be true.
				if bad_attributes:
								raise RuntimeError(
								    'The following attributes cannot be saved to HDF5 file because '
								    f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
								    f'''bytes: {bad_attributes}'''				)

				A__							 =       np.asarray(lowerCamelCase__				)

				A__							 =       1
				A__							 =       np.array_split(lowerCamelCase__       ,lowerCamelCase__				)

				# This will never loop forever thanks to the test above.
				while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data				):
								num_chunks += 1
								A__							 =       np.array_split(lowerCamelCase__       ,lowerCamelCase__				)

				if num_chunks > 1:
								for chunk_id, chunk_data in enumerate(lowerCamelCase__				):
												A__							 =       chunk_data
				else:
								A__							 =       data



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):


				if name in group.attrs:
								A__							 =       [n.decode('utf8'				) if hasattr(lowerCamelCase__       ,'decode'				) else n for n in group.attrs[name]]
				else:
								A__							 =       []
								A__							 =       0
								while "%s%d" % (name, chunk_id) in group.attrs:
												data.extend(
												    [n.decode('utf8'				) if hasattr(lowerCamelCase__       ,'decode'				) else n for n in group.attrs['%s%d' % (name, chunk_id)]]				)
												chunk_id += 1
				return data







def  __lowerCamelCase					(					lowerCAmelCase__				):


				def _expand_single_ad_tensor(lowerCAmelCase__				):
								if isinstance(lowerCamelCase__       ,tf.Tensor				) and t.shape.rank == 1:
												return tf.expand_dims(lowerCamelCase__       ,axis=-1				)
								return t

				return tf.nest.map_structure(_expand_single_ad_tensor       ,lowerCamelCase__				)










"""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						:						str					   =       False


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



def  __lowerCamelCase					(					):
				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					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				requires_backends(lowerCAmelCase__       ,['torch']				)
				_check_torch_version()

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

				A__							 =       textwrap.TextWrapper(width=80				)
				A__							 =       wrapper.wrap(text=lowerCAmelCase__				)
				A__							 =       '\n'.join(lowerCAmelCase__				)

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

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

				# 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)       ,lowerCAmelCase__				)
				A__							 =       ImageDraw.Draw(lowerCAmelCase__				)
				draw.text(xy=(left_padding, top_padding)       ,text=lowerCAmelCase__       ,fill=lowerCAmelCase__       ,font=lowerCAmelCase__				)
				return image



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,**lowerCAmelCase__				):
				requires_backends(lowerCAmelCase__       ,'vision'				)

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

				A__							 =       render_text(lowerCAmelCase__       ,**lowerCAmelCase__				)
				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(lowerCAmelCase__				)

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

				return new_image







class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					["""flattened_patches"""]


	def __init__(      self  ,					__a = True  ,					__a = True  ,					__a = None  ,					__a = 2048  ,					__a = False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)
					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       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					**__a						):


					"""simple docstring"""

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

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

					A__ ,			A__							 =       patch_size['height'], patch_size['width']
					A__ ,			A__							 =       get_image_size(__a						)

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

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

					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(__a						).reshape([rows, 1]						).repeat(1  ,					__a						).reshape([rows * columns, 1]						)
					A__							 =       torch.arange(__a						).reshape([1, columns]						).repeat(__a  ,					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(__a  ,					[0, 0, 0, max_patches - (rows * columns)]						).float()

					A__							 =       to_numpy_array(__a						)

					return result


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					**__a						):


					"""simple docstring"""

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

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

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






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = ChannelDimension.FIRST  ,					**__a  ,					):


					"""simple docstring"""

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

					A__							 =       make_list_of_images(__a						)

					if not valid_images(__a						):
									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(__a						) for image in images]

					# All transformations expect numpy arrays.
					A__							 =       [to_numpy_array(__a						) 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'  ,					__a						)
									A__							 =       kwargs.pop('font_path'  ,					__a						)

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

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

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

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

					return encoded_outputs


