



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_									=							False


UpperCAmelCase_									=							logging.get_logger(__name__)
UpperCAmelCase_									=							"ybelkada/fonts"
def 							UpperCAmelCase__		(				)->Union[str, Any]:
      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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Any				)->str:
      requires_backends(_SCREAMING_SNAKE_CASE     ,				['''torch''']				)
      _check_torch_version()

      _lowerCAmelCase 			=  image_tensor.unsqueeze(0				)
      _lowerCAmelCase 			=  torch.nn.functional.unfold(_SCREAMING_SNAKE_CASE     ,				(patch_height, patch_width)     ,				stride=(patch_height, patch_width)				)
      _lowerCAmelCase 			=  patches.reshape(image_tensor.size(0				)     ,				image_tensor.size(1				)     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				-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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	int = 3_6     ,				_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     ,				)->Image.Image:
      requires_backends(_SCREAMING_SNAKE_CASE     ,				'''vision'''				)
      # Add new lines so that each line is no more than 80 characters.

      _lowerCAmelCase 			=  textwrap.TextWrapper(width=8_0				)
      _lowerCAmelCase 			=  wrapper.wrap(text=_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  '''\n'''.join(_SCREAMING_SNAKE_CASE				)

      if font_bytes is not None and font_path is None:
            _lowerCAmelCase 			=  io.BytesIO(_SCREAMING_SNAKE_CASE				)
      elif font_path is not None:
            _lowerCAmelCase 			=  font_path
      else:
            _lowerCAmelCase 			=  hf_hub_download(_SCREAMING_SNAKE_CASE     ,				'''Arial.TTF'''				)
      _lowerCAmelCase 			=  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.
      _lowerCAmelCase 			=  ImageDraw.Draw(Image.new('''RGB'''     ,				(1, 1)     ,				_SCREAMING_SNAKE_CASE				)				)
      _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  temp_draw.textbbox((0, 0)     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # 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)     ,				_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	np.ndarray     ,				_SCREAMING_SNAKE_CASE   :	str     ,				**_SCREAMING_SNAKE_CASE   :	Optional[Any]				)->Dict:
      requires_backends(_SCREAMING_SNAKE_CASE     ,				'''vision'''				)

      # Convert to PIL image if necessary
      _lowerCAmelCase 			=  to_pil_image(_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  render_text(_SCREAMING_SNAKE_CASE     ,				**_SCREAMING_SNAKE_CASE				)
      _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(_SCREAMING_SNAKE_CASE				)

      if infer_channel_dimension_format(_SCREAMING_SNAKE_CASE				) == ChannelDimension.LAST:
            _lowerCAmelCase 			=  to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,				ChannelDimension.LAST				)

      return new_image





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     ['''flattened_patches''']





   def __init__(	self       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = 2_048       ,     _lowerCAmelCase = False       ,     **_lowerCAmelCase       ,     ):
         super().__init__(**_lowerCAmelCase			)
         _lowerCAmelCase 			=  patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  do_convert_rgb
         _lowerCAmelCase 			=  max_patches
         _lowerCAmelCase 			=  is_vqa





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     **_lowerCAmelCase			):
         requires_backends(self.extract_flattened_patches       ,     '''torch'''			)
         _check_torch_version()

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

         _lowerCAmelCase			,   _lowerCAmelCase 			=  patch_size['''height'''], patch_size['''width''']
         _lowerCAmelCase			,   _lowerCAmelCase 			=  get_image_size(_lowerCAmelCase			)

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

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

         _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(_lowerCAmelCase			).reshape([rows, 1]			).repeat(1       ,     _lowerCAmelCase			).reshape([rows * columns, 1]			)
         _lowerCAmelCase 			=  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 			=  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(_lowerCAmelCase       ,     [0, 0, 0, max_patches - (rows * columns)]			).float()

         _lowerCAmelCase 			=  to_numpy_array(_lowerCAmelCase			)

         return result





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

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

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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = ChannelDimension.FIRST       ,     **_lowerCAmelCase       ,     ):
         _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'''       ,     _lowerCAmelCase			) is not None:
               raise ValueError('''data_format is not an accepted input as the outputs are '''			)

         _lowerCAmelCase 			=  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 			=  [convert_to_rgb(_lowerCAmelCase			) for image in images]

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

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

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

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

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

         return encoded_outputs




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
      torch.save(model.state_dict()     ,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--albert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ALBERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)