




import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase    :		List[str]			   =       data_utils.TransfoXLTokenizer
lowerCamelCase    :		int			   =       data_utils.TransfoXLCorpus
lowerCamelCase    :		List[str]			   =       data_utils
lowerCamelCase    :		str			   =       data_utils

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]     ,							lowercase     :	List[Any]     ,							lowercase     :	str     ,							lowercase     :	Dict					):


      '''simple docstring'''



      if transfo_xl_dataset_file:
            # Convert a pre-processed corpus (see original TensorFlow repo)
            with open(lowercase     ,							'rb'					) as fp:
                  lowerCamelCase_							=   pickle.load(lowercase     ,							encoding='latin1'					)
            # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
            lowerCamelCase_							=   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
            print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""					)
            lowerCamelCase_							=   corpus.vocab.__dict__
            torch.save(lowercase     ,							lowercase					)

            lowerCamelCase_							=   corpus.__dict__
            corpus_dict_no_vocab.pop('vocab'     ,							lowercase					)
            lowerCamelCase_							=   pytorch_dump_folder_path + '/' + CORPUS_NAME
            print(f"""Save dataset to {pytorch_dataset_dump_path}"""					)
            torch.save(lowercase     ,							lowercase					)

      if tf_checkpoint_path:
            # Convert a pre-trained TensorFlow model
            lowerCamelCase_							=   os.path.abspath(lowercase					)
            lowerCamelCase_							=   os.path.abspath(lowercase					)

            print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""					)
            # Initialise PyTorch model
            if transfo_xl_config_file == "":
                  lowerCamelCase_							=   TransfoXLConfig()
            else:
                  lowerCamelCase_							=   TransfoXLConfig.from_json_file(lowercase					)
            print(f"""Building PyTorch model from configuration: {config}"""					)
            lowerCamelCase_							=   TransfoXLLMHeadModel(lowercase					)

            lowerCamelCase_							=   load_tf_weights_in_transfo_xl(lowercase     ,							lowercase     ,							lowercase					)
            # Save pytorch-model
            lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
            lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
            print(f"""Save PyTorch model to {os.path.abspath(lowercase					)}"""					)
            torch.save(model.state_dict()     ,							lowercase					)
            print(f"""Save configuration file to {os.path.abspath(lowercase					)}"""					)
            with open(lowercase     ,							'w'     ,							encoding='utf-8'					) as f:
                  f.write(config.to_json_string()					)


if __name__ == "__main__":
 lowerCamelCase    :		Optional[Any]			   =       argparse.ArgumentParser()
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     required=True,
     help="Path to the folder to store the PyTorch model or dataset/vocab.",
 )
 parser.add_argument(
     "--tf_checkpoint_path",
     default="",
     type=str,
     help="An optional path to a TensorFlow checkpoint path to be converted.",
 )
 parser.add_argument(
     "--transfo_xl_config_file",
     default="",
     type=str,
     help=(
         "An optional config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--transfo_xl_dataset_file",
     default="",
     type=str,
     help="An optional dataset file to be converted in a vocabulary.",
 )
 lowerCamelCase    :		Union[str, Any]			   =       parser.parse_args()
 convert_transfo_xl_checkpoint_to_pytorch(
     args.tf_checkpoint_path,
     args.transfo_xl_config_file,
     args.pytorch_dump_folder_path,
     args.transfo_xl_dataset_file,
 )









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    :		Optional[Any]			   =       False


lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)
lowerCamelCase    :		Any			   =       "ybelkada/fonts"

def       _SCREAMING_SNAKE_CASE      (							):


      '''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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



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

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


      '''simple docstring'''



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

      lowerCamelCase_							=   textwrap.TextWrapper(width=80					)
      lowerCamelCase_							=   wrapper.wrap(text=lowercase					)
      lowerCamelCase_							=   '\n'.join(lowercase					)

      if font_bytes is not None and font_path is None:
            lowerCamelCase_							=   io.BytesIO(lowercase					)
      elif font_path is not None:
            lowerCamelCase_							=   font_path
      else:
            lowerCamelCase_							=   hf_hub_download(lowercase     ,							'Arial.TTF'					)
      lowerCamelCase_							=   ImageFont.truetype(lowercase     ,							encoding='UTF-8'     ,							size=lowercase					)

      # Use a temporary canvas to determine the width and height in pixels when
      # rendering the text.
      lowerCamelCase_							=   ImageDraw.Draw(Image.new('RGB'     ,							(1, 1)     ,							lowercase					)					)
      lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   temp_draw.textbbox((0, 0)     ,							lowercase     ,							lowercase					)

      # 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)     ,							lowercase					)
      lowerCamelCase_							=   ImageDraw.Draw(lowercase					)
      draw.text(xy=(left_padding, top_padding)     ,							text=lowercase     ,							fill=lowercase     ,							font=lowercase					)
      return image

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	np.ndarray     ,							lowercase     :	str     ,							**lowercase     :	List[Any]					):


      '''simple docstring'''



      requires_backends(lowercase     ,							'vision'					)

      # Convert to PIL image if necessary
      lowerCamelCase_							=   to_pil_image(lowercase					)

      lowerCamelCase_							=   render_text(lowercase     ,							**lowercase					)
      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(lowercase					)

      if infer_channel_dimension_format(lowercase					) == ChannelDimension.LAST:
            lowerCamelCase_							=   to_channel_dimension_format(lowercase     ,							ChannelDimension.LAST					)

      return new_image







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''flattened_patches''']

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

       """simple docstring"""






       super().__init__(**A_  )
       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     a__			(    self :  Union[str, Any]						,							A_ :  np.ndarray						,							A_ :  int						,							A_ :  dict						,							**A_ :  Any  )       ->     np.ndarray:

       """simple docstring"""






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

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

       lowerCamelCase_					,    lowerCamelCase_							=   patch_size['height'], patch_size['width']
       lowerCamelCase_					,    lowerCamelCase_							=   get_image_size(A_  )

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

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

       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(A_  ).reshape([rows, 1]  ).repeat(1						,							A_  ).reshape([rows * columns, 1]  )
       lowerCamelCase_							=   torch.arange(A_  ).reshape([1, columns]  ).repeat(A_						,							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(A_						,							[0, 0, 0, max_patches - (rows * columns)]  ).float()

       lowerCamelCase_							=   to_numpy_array(A_  )

       return result

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

       """simple docstring"""






       if image.dtype == np.uinta:
             lowerCamelCase_							=   image.astype(np.floataa  )

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

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





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

       """simple docstring"""






       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'						,							A_  ) is not None:
             raise ValueError('data_format is not an accepted input as the outputs are '  )

       lowerCamelCase_							=   make_list_of_images(A_  )

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

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

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

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

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

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

       return encoded_outputs



