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			:							Dict     =False


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)
lowerCAmelCase			:							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__  (					__A   , __A   , __A	):







    '''simple docstring'''
    requires_backends(__A   , ["""torch"""]	)
    _check_torch_version()

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







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

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

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

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

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







def 						A__  (					__A   , __A   , **__A	):







    '''simple docstring'''
    requires_backends(__A   , """vision"""	)

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

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

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

    _lowerCamelCase     :		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
    _lowerCamelCase     :		Tuple 						=						to_numpy_array(__A	)

    if infer_channel_dimension_format(__A	) == ChannelDimension.LAST:
        _lowerCamelCase     :		List[Any] 						=						to_channel_dimension_format(__A   , ChannelDimension.LAST	)

    return new_image


class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ["flattened_patches"]



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


          """simple docstring"""


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



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


          """simple docstring"""


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

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

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

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

          _lowerCamelCase     :		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]
          _lowerCamelCase     :		str 						=						torch_extract_patches(lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__)

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

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

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

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

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

          _lowerCamelCase     :		Any 						=						to_numpy_array(lowerCamelCase__)

          return result



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


          """simple docstring"""


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

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

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




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


          """simple docstring"""


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

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

          # All transformations expect numpy arrays.
          _lowerCamelCase     :		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.""")
              _lowerCamelCase     :		Optional[int] 						=						kwargs.pop("""font_bytes"""    ,  lowerCamelCase__)
              _lowerCamelCase     :		Any 						=						kwargs.pop("""font_path"""    ,  lowerCamelCase__)

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

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

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

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

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

          _lowerCamelCase     :		List[str] 						=						BatchFeature(
              data={"""flattened_patches""": images, """attention_mask""": attention_masks}    ,  tensor_type=lowerCamelCase__)

          return encoded_outputs





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




