
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCamelCase_  :	Optional[Any] 				=					datasets.utils.logging.get_logger(__name__)

@dataclass
class     _UpperCamelCase			(   datasets.BuilderConfig					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[datasets.Features]    =	None
			__UpperCamelCase :  str    =	"utf-8"
			__UpperCamelCase :  Optional[str]    =	None
			__UpperCamelCase :  Optional[str]    =	None
			__UpperCamelCase :  bool    =	True  # deprecated
			__UpperCamelCase :  Optional[int]    =	None  # deprecated
			__UpperCamelCase :  int    =	10 << 20  # 10MB
			__UpperCamelCase :  Optional[bool]    =	None




class     _UpperCamelCase			(   datasets.ArrowBasedBuilder					):






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	JsonConfig

			def  lowerCAmelCase__		(    self	:    int ):
							if self.config.block_size is not None:
											logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""" )
											UpperCamelCase_:							List[str]		=  self.config.block_size
							if self.config.use_threads is not True:
											logger.warning(
											    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.""" )
							if self.config.newlines_in_values is not None:
											raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported""" )
							return datasets.DatasetInfo(features=self.config.features )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    str ):
							if not self.config.data_files:
											raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
							UpperCamelCase_:							Dict		=  dl_manager.download_and_extract(self.config.data_files )
							if isinstance(snake_case_  ,		(str, list, tuple) ):
											UpperCamelCase_:							List[Any]		=  data_files
											if isinstance(snake_case_  ,		snake_case_ ):
															UpperCamelCase_:							str		=  [files]
											UpperCamelCase_:							Any		=  [dl_manager.iter_files(snake_case_ ) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,		gen_kwargs={"""files""": files} )]
							UpperCamelCase_:							Dict		=  []
							for split_name, files in data_files.items():
											if isinstance(snake_case_  ,		snake_case_ ):
															UpperCamelCase_:							Tuple		=  [files]
											UpperCamelCase_:							Optional[int]		=  [dl_manager.iter_files(snake_case_ ) for file in files]
											splits.append(datasets.SplitGenerator(name=snake_case_  ,		gen_kwargs={"""files""": files} ) )
							return splits

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    pa.Table ):
							if self.config.features is not None:
											# adding missing columns
											for column_name in set(self.config.features ) - set(pa_table.column_names ):
															UpperCamelCase_:							Union[str, Any]		=  self.config.features.arrow_schema.field(snake_case_ ).type
															UpperCamelCase_:							Tuple		=  pa_table.append_column(snake_case_  ,		pa.array([None] * len(snake_case_ )  ,		type=snake_case_ ) )
											# more expensive cast to support nested structures with keys in a different order
											# allows str <-> int/float or str to Audio for example
											UpperCamelCase_:							int		=  table_cast(snake_case_  ,		self.config.features.arrow_schema )
							return pa_table




			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    Optional[Any] ):
							for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case_ ) ):
											# If the file is one json object and if we need to look at the list of items in one specific field
											if self.config.field is not None:
															with open(snake_case_  ,		encoding=self.config.encoding  ,		errors=self.config.encoding_errors ) as f:
																			UpperCamelCase_:							Dict		=  json.load(snake_case_ )

															# We keep only the field we are interested in
															UpperCamelCase_:							Optional[int]		=  dataset[self.config.field]

															# We accept two format: a list of dicts or a dict of lists
															if isinstance(snake_case_  ,		(list, tuple) ):
																			UpperCamelCase_:							Optional[int]		=  set().union(*[row.keys() for row in dataset] )
																			UpperCamelCase_:							int		=  {col: [row.get(snake_case_ ) for row in dataset] for col in keys}
															else:
																			UpperCamelCase_:							Optional[int]		=  dataset
															UpperCamelCase_:							List[str]		=  pa.Table.from_pydict(snake_case_ )
															yield file_idx, self._cast_table(snake_case_ )

											# If the file has one json object per line
											else:
															with open(snake_case_  ,		"""rb""" ) as f:
																			UpperCamelCase_:							Optional[int]		=  0
																			# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																			# Set a default minimum value of 16kB if the chunk size is really small
																			UpperCamelCase_:							Optional[int]		=  max(self.config.chunksize // 32  ,		16 << 10 )
																			UpperCamelCase_:							Tuple		=  (
																			    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
																			)
																			while True:
																							UpperCamelCase_:							int		=  f.read(self.config.chunksize )
																							if not batch:
																											break
																							# Finish current line
																							try:
																											batch += f.readline()
																							except (AttributeError, io.UnsupportedOperation):
																											batch += readline(snake_case_ )
																							# PyArrow only accepts utf-8 encoded bytes
																							if self.config.encoding != "utf-8":
																											UpperCamelCase_:							Tuple		=  batch.decode(self.config.encoding  ,		errors=snake_case_ ).encode("""utf-8""" )
																							try:
																											while True:
																															try:
																																			UpperCamelCase_:							Tuple		=  paj.read_json(
																																			    io.BytesIO(snake_case_ )  ,		read_options=paj.ReadOptions(block_size=snake_case_ ) )
																																			break
																															except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																			if (
																																			    isinstance(snake_case_  ,		pa.ArrowInvalid )
																																			    and "straddling" not in str(snake_case_ )
																																			    or block_size > len(snake_case_ )
																																			):
																																							raise
																																			else:
																																							# Increase the block size in case it was too small.
																																							# The block size will be reset for the next file.
																																							logger.debug(
																																							    f'''Batch of {len(snake_case_ )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
																																							block_size *= 2
																							except pa.ArrowInvalid as e:
																											try:
																															with open(
																															    snake_case_  ,		encoding=self.config.encoding  ,		errors=self.config.encoding_errors ) as f:
																																			UpperCamelCase_:							Optional[Any]		=  json.load(snake_case_ )
																											except json.JSONDecodeError:
																															logger.error(f'''Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}''' )
																															raise e
																											# If possible, parse the file as a list of json objects and exit the loop
																											if isinstance(snake_case_  ,		snake_case_ ):  # list is the only sequence type supported in JSON
																															try:
																																			UpperCamelCase_:							Any		=  set().union(*[row.keys() for row in dataset] )
																																			UpperCamelCase_:							List[str]		=  {col: [row.get(snake_case_ ) for row in dataset] for col in keys}
																																			UpperCamelCase_:							int		=  pa.Table.from_pydict(snake_case_ )
																															except (pa.ArrowInvalid, AttributeError) as e:
																																			logger.error(f'''Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}''' )
																																			raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
																															yield file_idx, self._cast_table(snake_case_ )
																															break
																											else:
																															logger.error(f'''Failed to read file \'{file}\' with error {type(snake_case_ )}: {e}''' )
																															raise ValueError(
																															    f'''Not able to read records in the JSON file at {file}. '''
																															    f'''You should probably indicate the field of the JSON file containing your records. '''
																															    f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
																															    f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																							yield (file_idx, batch_idx), self._cast_table(snake_case_ )
																							batch_idx += 1







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_  :	List[str] 				=					False


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)
lowerCamelCase_  :	Optional[int] 				=					"""ybelkada/fonts"""







def     A__   (  )					->		Dict:
				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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				requires_backends(lowerCamelCase       ,      ["""torch"""]  )
				_check_torch_version()

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

				UpperCamelCase_:							List[str]		=  textwrap.TextWrapper(width=80  )
				UpperCamelCase_:							Optional[int]		=  wrapper.wrap(text=lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  """\n""".join(lowerCamelCase  )

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

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

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







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )					->		List[str]:
				requires_backends(lowerCamelCase       ,      """vision"""  )

				# Convert to PIL image if necessary
				UpperCamelCase_:							List[str]		=  to_pil_image(lowerCamelCase  )

				UpperCamelCase_:							Union[str, Any]		=  render_text(lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  max(header_image.width       ,      image.width  )

				UpperCamelCase_:							Tuple		=  int(image.height * (new_width / image.width)  )
				UpperCamelCase_:							Dict		=  int(header_image.height * (new_width / header_image.width)  )

				UpperCamelCase_:							str		=  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_:							Optional[Any]		=  to_numpy_array(lowerCamelCase  )

				if infer_channel_dimension_format(lowerCamelCase  ) == ChannelDimension.LAST:
								UpperCamelCase_:							Tuple		=  to_channel_dimension_format(lowerCamelCase       ,      ChannelDimension.LAST  )

				return new_image





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

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

			def __init__(    self	:    int  ,		snake_case_	:    bool = True  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    int = 2048  ,		snake_case_	:    bool = False  ,		**snake_case_	:    Any  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							int		=  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							List[Any]		=  do_convert_rgb
							UpperCamelCase_:							Tuple		=  max_patches
							UpperCamelCase_:							Tuple		=  is_vqa

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    int  ,		snake_case_	:    dict  ,		**snake_case_	:    Tuple ):
							requires_backends(self.extract_flattened_patches  ,		"""torch""" )
							_check_torch_version()

							# convert to torch
							UpperCamelCase_:							int		=  to_channel_dimension_format(snake_case_  ,		ChannelDimension.FIRST )
							UpperCamelCase_:							List[str]		=  torch.from_numpy(snake_case_ )

							UpperCamelCase_,				UpperCamelCase_:							List[Any]		=  patch_size["""height"""], patch_size["""width"""]
							UpperCamelCase_,				UpperCamelCase_:							Tuple		=  get_image_size(snake_case_ )

							# maximize scale s.t.
							UpperCamelCase_:							List[Any]		=  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
							UpperCamelCase_:							Any		=  max(min(math.floor(scale * image_height / patch_height )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							List[str]		=  max(min(math.floor(scale * image_width / patch_width )  ,		snake_case_ )  ,		1 )
							UpperCamelCase_:							int		=  max(num_feasible_rows * patch_height  ,		1 )
							UpperCamelCase_:							Optional[Any]		=  max(num_feasible_cols * patch_width  ,		1 )

							UpperCamelCase_:							str		=  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]
							UpperCamelCase_:							List[str]		=  torch_extract_patches(snake_case_  ,		snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  patches.shape
							UpperCamelCase_:							List[str]		=  patches_shape[1]
							UpperCamelCase_:							Optional[Any]		=  patches_shape[2]
							UpperCamelCase_:							List[str]		=  patches_shape[3]

							# [rows * columns, patch_height * patch_width * image_channels]
							UpperCamelCase_:							Union[str, Any]		=  patches.reshape([rows * columns, depth] )

							# [rows * columns, 1]
							UpperCamelCase_:							Optional[Any]		=  torch.arange(snake_case_ ).reshape([rows, 1] ).repeat(1  ,		snake_case_ ).reshape([rows * columns, 1] )
							UpperCamelCase_:							Optional[int]		=  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]
							UpperCamelCase_:							Union[str, Any]		=  row_ids.to(torch.floataa )
							UpperCamelCase_:							str		=  col_ids.to(torch.floataa )

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

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

							UpperCamelCase_:							List[Any]		=  to_numpy_array(snake_case_ )

							return result

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Tuple ):
							if image.dtype == np.uinta:
											UpperCamelCase_:							List[str]		=  image.astype(np.floataa )

							# take mean across the whole `image`
							UpperCamelCase_:							str		=  np.mean(snake_case_ )
							UpperCamelCase_:							str		=  np.std(snake_case_ )
							UpperCamelCase_:							str		=  max(snake_case_  ,		1.0 / math.sqrt(np.prod(image.shape ) ) )

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




			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    ImageInput  ,		snake_case_	:    Optional[str] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Optional[Dict[str, int]] = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Tuple		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Tuple		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCamelCase_:							Optional[Any]		=  patch_size if patch_size is not None else self.patch_size
							UpperCamelCase_:							Optional[int]		=  max_patches if max_patches is not None else self.max_patches
							UpperCamelCase_:							Tuple		=  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 """ )

							UpperCamelCase_:							Dict		=  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:
											UpperCamelCase_:							str		=  [convert_to_rgb(snake_case_ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Union[str, Any]		=  [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.""" )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_bytes"""  ,		snake_case_ )
											UpperCamelCase_:							List[Any]		=  kwargs.pop("""font_path"""  ,		snake_case_ )

											if isinstance(snake_case_  ,		snake_case_ ):
															UpperCamelCase_:							str		=  [header_text] * len(snake_case_ )

											UpperCamelCase_:							str		=  [
											    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:
											UpperCamelCase_:							Union[str, Any]		=  [self.normalize(image=snake_case_ ) for image in images]

							# convert to torch tensor and permute
							UpperCamelCase_:							str		=  [
							    self.extract_flattened_patches(image=snake_case_  ,		max_patches=snake_case_  ,		patch_size=snake_case_ )
							    for image in images
							]

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

							UpperCamelCase_:							Optional[Any]		=  BatchFeature(
							    data={"""flattened_patches""": images, """attention_mask""": attention_masks}  ,		tensor_type=snake_case_ )

							return encoded_outputs






