


"""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:
   __snake_case		=       False


__snake_case		=       logging.get_logger(__name__)
__snake_case		=       '''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_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					Tuple       ):






     """simple docstring"""

     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       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int = 36,     _lowerCAmelCase     :					str = "black",     _lowerCAmelCase     :					str = "white",     _lowerCAmelCase     :					int = 5,     _lowerCAmelCase     :					int = 5,     _lowerCAmelCase     :					int = 5,     _lowerCAmelCase     :					int = 5,     _lowerCAmelCase     :					Optional[bytes] = None,     _lowerCAmelCase     :					Optional[str] = None,     ):






     """simple docstring"""

     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       A_   (   _lowerCAmelCase     :					np.ndarray,     _lowerCAmelCase     :					str,     **_lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

     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     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		['flattened_patches']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = 2048    ,				__UpperCAmelCase = False    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _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    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							np.ndarray:
          requires_backends(self.extract_flattened_patches    ,				'''torch'''							)
          _check_torch_version()

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

          _a	,      _a    	=				patch_size['''height'''], patch_size['''width''']
          _a	,      _a    	=				get_image_size(__UpperCAmelCase							)

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

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

          _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(__UpperCAmelCase							).reshape([rows, 1]							).repeat(1    ,				__UpperCAmelCase							).reshape([rows * columns, 1]							)
          _a    	=				torch.arange(__UpperCAmelCase							).reshape([1, columns]							).repeat(__UpperCAmelCase    ,				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(__UpperCAmelCase    ,				[0, 0, 0, max_patches - (rows * columns)]							).float()

          _a    	=				to_numpy_array(__UpperCAmelCase							)

          return result


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase							)       ->							np.ndarray:
          if image.dtype == np.uinta:
               _a    	=				image.astype(np.floataa							)

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

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





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							ImageInput:
          _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'''    ,				__UpperCAmelCase							) is not None:
               raise ValueError('''data_format is not an accepted input as the outputs are '''							)

          _a    	=				make_list_of_images(__UpperCAmelCase							)

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

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

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

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

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

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

          return encoded_outputs



"""simple docstring"""






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				BertForPreTraining(_lowerCAmelCase       )

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

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


if __name__ == "__main__":
   __snake_case		=       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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT 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.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)