


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       :       Dict						       =				False


__UpperCamelCase       :       Tuple						       =				logging.get_logger(__name__)
__UpperCamelCase       :       str						       =				"ybelkada/fonts"

def 			_a  (				):







 """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 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



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

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







 """simple docstring"""



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

 UpperCamelCase__					:    Union[str, Any]          =   textwrap.TextWrapper(width=80     )
 UpperCamelCase__					:    str          =   wrapper.wrap(text=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''\n'''.join(SCREAMING_SNAKE_CASE     )

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

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

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				np.ndarray    , SCREAMING_SNAKE_CASE	:				str    , **SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 requires_backends(SCREAMING_SNAKE_CASE    , '''vision'''     )

 # Convert to PIL image if necessary
 UpperCamelCase__					:    Union[str, Any]          =   to_pil_image(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    List[str]          =   render_text(SCREAMING_SNAKE_CASE    , **SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   max(header_image.width    , image.width     )

 UpperCamelCase__					:    Optional[Any]          =   int(image.height * (new_width / image.width)     )
 UpperCamelCase__					:    Any          =   int(header_image.height * (new_width / header_image.width)     )

 UpperCamelCase__					:    Dict          =   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__					:    Tuple          =   to_numpy_array(SCREAMING_SNAKE_CASE     )

 if infer_channel_dimension_format(SCREAMING_SNAKE_CASE     ) == ChannelDimension.LAST:
  UpperCamelCase__					:    List[Any]          =   to_channel_dimension_format(SCREAMING_SNAKE_CASE    , ChannelDimension.LAST     )

 return new_image






class       __magic_name__	(     __lowerCAmelCase):
     A:		Union[str, Any]			=      ["flattened_patches"]


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Dict[str, int] = None							,	lowerCamelCase__							:     int = 2048							,	lowerCamelCase__							:     bool = False							,	**lowerCamelCase__							:     Union[str, Any]							,	)  ->      None:





      '''simple docstring'''






      super().__init__(**lowerCamelCase__			)
      UpperCamelCase__					:    str          =   patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
      UpperCamelCase__					:    Optional[int]          =   do_normalize
      UpperCamelCase__					:    int          =   do_convert_rgb
      UpperCamelCase__					:    List[Any]          =   max_patches
      UpperCamelCase__					:    Tuple          =   is_vqa


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     np.ndarray							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     dict							,	**lowerCamelCase__							:     int			)  ->      np.ndarray:





      '''simple docstring'''






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

      # convert to torch
      UpperCamelCase__					:    Optional[int]          =   to_channel_dimension_format(lowerCamelCase__							,	ChannelDimension.FIRST			)
      UpperCamelCase__					:    List[Any]          =   torch.from_numpy(lowerCamelCase__			)

      UpperCamelCase__       ,				UpperCamelCase__					:    Optional[Any]          =   patch_size['''height'''], patch_size['''width''']
      UpperCamelCase__       ,				UpperCamelCase__					:    int          =   get_image_size(lowerCamelCase__			)

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

      UpperCamelCase__					:    Dict          =   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]
      UpperCamelCase__					:    str          =   torch_extract_patches(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    int          =   patches.shape
      UpperCamelCase__					:    Tuple          =   patches_shape[1]
      UpperCamelCase__					:    Tuple          =   patches_shape[2]
      UpperCamelCase__					:    Tuple          =   patches_shape[3]

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

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

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

      # [max_patches, 2 + patch_height * patch_width * image_channels]
      UpperCamelCase__					:    List[str]          =   torch.nn.functional.pad(lowerCamelCase__							,	[0, 0, 0, max_patches - (rows * columns)]			).float()

      UpperCamelCase__					:    Any          =   to_numpy_array(lowerCamelCase__			)

      return result


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     np.ndarray							,	lowerCamelCase__							:     Optional[Union[str, ChannelDimension]] = None							,	**lowerCamelCase__							:     List[str]			)  ->      np.ndarray:





      '''simple docstring'''






      if image.dtype == np.uinta:
       UpperCamelCase__					:    List[str]          =   image.astype(np.floataa			)

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

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


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     ImageInput							,	lowerCamelCase__							:     Optional[str] = None							,	lowerCamelCase__							:     bool = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     Optional[Dict[str, int]] = None							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	lowerCamelCase__							:     ChannelDimension = ChannelDimension.FIRST							,	**lowerCamelCase__							:     Tuple							,	)  ->      ImageInput:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase__					:    List[str]          =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
      UpperCamelCase__					:    Any          =   patch_size if patch_size is not None else self.patch_size
      UpperCamelCase__					:    Dict          =   max_patches if max_patches is not None else self.max_patches
      UpperCamelCase__					:    Optional[Any]          =   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 '''			)

      UpperCamelCase__					:    Optional[int]          =   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:
       UpperCamelCase__					:    Union[str, Any]          =   [convert_to_rgb(lowerCamelCase__			) for image in images]

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

       if isinstance(lowerCamelCase__							,	lowerCamelCase__			):
        UpperCamelCase__					:    Union[str, Any]          =   [header_text] * len(lowerCamelCase__			)

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

      if do_normalize:
       UpperCamelCase__					:    Tuple          =   [self.normalize(image=lowerCamelCase__			) for image in images]

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

      # create attention mask in numpy
      UpperCamelCase__					:    Tuple          =   [(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=lowerCamelCase__			)

      return encoded_outputs






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 if isinstance(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Dict          =   np.full((len(SCREAMING_SNAKE_CASE     ), sequence_length, 2)    , SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    Dict          =   np.full((len(SCREAMING_SNAKE_CASE     ), sequence_length)    , SCREAMING_SNAKE_CASE     )

 for i, tensor in enumerate(SCREAMING_SNAKE_CASE     ):
  if padding_side == "right":
   if isinstance(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
    UpperCamelCase__					:    str          =   tensor[:sequence_length]
   else:
    UpperCamelCase__					:    Union[str, Any]          =   tensor[:sequence_length]
  else:
   if isinstance(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
    UpperCamelCase__					:    str          =   tensor[:sequence_length]
   else:
    UpperCamelCase__					:    str          =   tensor[:sequence_length]

 return out_tensor.tolist()

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    Union[str, Any]          =   ord(SCREAMING_SNAKE_CASE     )
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 UpperCamelCase__					:    Any          =   unicodedata.category(SCREAMING_SNAKE_CASE     )
 if cat.startswith('''P'''     ):
  return True
 return False






@dataclass
class       __magic_name__	(     __lowerCAmelCase):
     A:		PreTrainedTokenizerBase
     A:		Union[bool, str, PaddingStrategy]			=      True
     A:		Optional[int]			=      None
     A:		Optional[int]			=      None
     A:		int			=      -1_0_0
     A:		str			=      "pt"


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     List[Any]			)  ->      Any:





      '''simple docstring'''






      import torch

      UpperCamelCase__					:    Tuple          =   '''label''' if '''label''' in features[0].keys() else '''labels'''
      UpperCamelCase__					:    Optional[Any]          =   [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      UpperCamelCase__					:    Optional[int]          =   self.tokenizer.pad(
          lowerCamelCase__							,	padding=self.padding							,	max_length=self.max_length							,	pad_to_multiple_of=self.pad_to_multiple_of							,	return_tensors='''pt''' if labels is None else None							,	)

      if labels is None:
       return batch

      UpperCamelCase__					:    Dict          =   torch.tensor(batch['''entity_ids''']			).shape[1]
      UpperCamelCase__					:    Union[str, Any]          =   self.tokenizer.padding_side
      if padding_side == "right":
       UpperCamelCase__					:    Optional[int]          =   [
           list(lowerCamelCase__			) + [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase__			)) for label in labels
       ]
      else:
       UpperCamelCase__					:    int          =   [
           [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase__			)) + list(lowerCamelCase__			) for label in labels
       ]

      UpperCamelCase__					:    Union[str, Any]          =   [feature['''ner_tags'''] for feature in features]
      UpperCamelCase__					:    List[str]          =   padding_tensor(lowerCamelCase__							,	-1							,	lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   [feature['''original_entity_spans'''] for feature in features]
      UpperCamelCase__					:    Any          =   padding_tensor(lowerCamelCase__							,	(-1, -1)							,	lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    str          =   {k: torch.tensor(lowerCamelCase__							,	dtype=torch.intaa			) for k, v in batch.items()}

      return batch


