



"""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:
 _lowerCAmelCase    : List[str]							=							False


_lowerCAmelCase    : str							=							logging.get_logger(__name__)
_lowerCAmelCase    : List[str]							=							"ybelkada/fonts"

def 						__snake_case						(				)  ->       Union[str, Any]:





     '''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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       Union[str, Any]:





     '''simple docstring'''

     requires_backends(SCREAMING_SNAKE_CASE__						,  ["torch"]    )
     _check_torch_version()

     _UpperCAmelCase      :     List[str]        =   image_tensor.unsqueeze(0    )
     _UpperCAmelCase      :     Optional[Any]        =   torch.nn.functional.unfold(SCREAMING_SNAKE_CASE__						,  (patch_height, patch_width)						,  stride=(patch_height, patch_width)    )
     _UpperCAmelCase      :     List[str]        =   patches.reshape(image_tensor.size(0    )						,  image_tensor.size(1    )						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  -1    )
     _UpperCAmelCase      :     int        =   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 						__snake_case						(				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						,  )  ->       Image.Image:





     '''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      :     Union[str, Any]        =   wrapper.wrap(text=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[str]        =   "\n".join(SCREAMING_SNAKE_CASE__    )

     if font_bytes is not None and font_path is None:
          _UpperCAmelCase      :     List[str]        =   io.BytesIO(SCREAMING_SNAKE_CASE__    )
     elif font_path is not None:
          _UpperCAmelCase      :     Optional[int]        =   font_path
     else:
          _UpperCAmelCase      :     Union[str, Any]        =   hf_hub_download(SCREAMING_SNAKE_CASE__						,  "Arial.TTF"    )
     _UpperCAmelCase      :     int        =   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      :     Tuple        =   ImageDraw.Draw(Image.new("RGB"						,  (1, 1)						,  SCREAMING_SNAKE_CASE__    )    )
     _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   temp_draw.textbbox((0, 0)						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     # Create the actual image with a bit of padding around the text.
     _UpperCAmelCase      :     List[str]        =   text_width + left_padding + right_padding
     _UpperCAmelCase      :     Optional[int]        =   text_height + top_padding + bottom_padding
     _UpperCAmelCase      :     Tuple        =   Image.new("RGB"						,  (image_width, image_height)						,  SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						np.ndarray						,  SCREAMING_SNAKE_CASE__     :						str						,  **SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       List[Any]:





     '''simple docstring'''

     requires_backends(SCREAMING_SNAKE_CASE__						,  "vision"    )

     # Convert to PIL image if necessary
     _UpperCAmelCase      :     Dict        =   to_pil_image(SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     str        =   render_text(SCREAMING_SNAKE_CASE__						,  **SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     str        =   max(header_image.width						,  image.width    )

     _UpperCAmelCase      :     Any        =   int(image.height * (new_width / image.width)    )
     _UpperCAmelCase      :     str        =   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      :     List[str]        =   to_numpy_array(SCREAMING_SNAKE_CASE__    )

     if infer_channel_dimension_format(SCREAMING_SNAKE_CASE__    ) == ChannelDimension.LAST:
          _UpperCAmelCase      :     int        =   to_channel_dimension_format(SCREAMING_SNAKE_CASE__						,  ChannelDimension.LAST    )

     return new_image






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			['flattened_patches']



      def __init__(					self					:     int					,     A					:     bool = True					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     int = 2_0_4_8					,     A					:     bool = False					,     **A					:     List[Any]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     Tuple        =   patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
           _UpperCAmelCase      :     Optional[int]        =   do_normalize
           _UpperCAmelCase      :     int        =   do_convert_rgb
           _UpperCAmelCase      :     Optional[int]        =   max_patches
           _UpperCAmelCase      :     int        =   is_vqa



      def 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     int					,     A					:     dict					,     **A					:     List[str]						):
           requires_backends(self.extract_flattened_patches					,     "torch"						)
           _check_torch_version()

           # convert to torch
           _UpperCAmelCase      :     Any        =   to_channel_dimension_format(A					,     ChannelDimension.FIRST						)
           _UpperCAmelCase      :     Dict        =   torch.from_numpy(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   patch_size["height"], patch_size["width"]
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   get_image_size(A						)

           # maximize scale s.t.
           _UpperCAmelCase      :     Any        =   math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)						)
           _UpperCAmelCase      :     List[Any]        =   max(min(math.floor(scale * image_height / patch_height						)					,     A						)					,     1						)
           _UpperCAmelCase      :     int        =   max(min(math.floor(scale * image_width / patch_width						)					,     A						)					,     1						)
           _UpperCAmelCase      :     Any        =   max(num_feasible_rows * patch_height					,     1						)
           _UpperCAmelCase      :     List[Any]        =   max(num_feasible_cols * patch_width					,     1						)

           _UpperCAmelCase      :     List[Any]        =   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]
           _UpperCAmelCase      :     str        =   torch_extract_patches(A					,     A					,     A						)

           _UpperCAmelCase      :     int        =   patches.shape
           _UpperCAmelCase      :     Any        =   patches_shape[1]
           _UpperCAmelCase      :     str        =   patches_shape[2]
           _UpperCAmelCase      :     Union[str, Any]        =   patches_shape[3]

           # [rows * columns, patch_height * patch_width * image_channels]
           _UpperCAmelCase      :     List[Any]        =   patches.reshape([rows * columns, depth]						)

           # [rows * columns, 1]
           _UpperCAmelCase      :     List[Any]        =   torch.arange(A						).reshape([rows, 1]						).repeat(1					,     A						).reshape([rows * columns, 1]						)
           _UpperCAmelCase      :     int        =   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]
           _UpperCAmelCase      :     Dict        =   row_ids.to(torch.floataa						)
           _UpperCAmelCase      :     Optional[Any]        =   col_ids.to(torch.floataa						)

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

           # [max_patches, 2 + patch_height * patch_width * image_channels]
           _UpperCAmelCase      :     Optional[int]        =   torch.nn.functional.pad(A					,     [0, 0, 0, max_patches - (rows * columns)]						).float()

           _UpperCAmelCase      :     List[str]        =   to_numpy_array(A						)

           return result



      def 						snake_case_ (					self					:     str					,     A					:     np.ndarray					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     List[str]						):
           if image.dtype == np.uinta:
                _UpperCAmelCase      :     int        =   image.astype(np.floataa						)

           # take mean across the whole `image`
           _UpperCAmelCase      :     List[str]        =   np.mean(A						)
           _UpperCAmelCase      :     Optional[int]        =   np.std(A						)
           _UpperCAmelCase      :     str        =   max(A					,     1.0 / math.sqrt(np.prod(image.shape						)						)						)

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



      def 						snake_case_ (					self					:     Tuple					,     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					:     Tuple					,     ):
           _UpperCAmelCase      :     Dict        =   do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase      :     Union[str, Any]        =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
           _UpperCAmelCase      :     Union[str, Any]        =   patch_size if patch_size is not None else self.patch_size
           _UpperCAmelCase      :     List[Any]        =   max_patches if max_patches is not None else self.max_patches
           _UpperCAmelCase      :     List[str]        =   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 "						)

           _UpperCAmelCase      :     Any        =   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:
                _UpperCAmelCase      :     Optional[Any]        =   [convert_to_rgb(A						) for image in images]

           # All transformations expect numpy arrays.
           _UpperCAmelCase      :     str        =   [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."						)
                _UpperCAmelCase      :     Optional[Any]        =   kwargs.pop("font_bytes"					,     A						)
                _UpperCAmelCase      :     List[str]        =   kwargs.pop("font_path"					,     A						)

                if isinstance(A					,     A						):
                     _UpperCAmelCase      :     List[str]        =   [header_text] * len(A						)

                _UpperCAmelCase      :     int        =   [
                    render_header(A					,     header_text[i]					,     font_bytes=A					,     font_path=A						)
                    for i, image in enumerate(A						)
                ]

           if do_normalize:
                _UpperCAmelCase      :     Any        =   [self.normalize(image=A						) for image in images]

           # convert to torch tensor and permute
           _UpperCAmelCase      :     Dict        =   [
               self.extract_flattened_patches(image=A					,     max_patches=A					,     patch_size=A						)
               for image in images
           ]

           # create attention mask in numpy
           _UpperCAmelCase      :     Union[str, Any]        =   [(image.sum(axis=-1						) != 0).astype(np.floataa						) for image in images]

           _UpperCAmelCase      :     Optional[int]        =   BatchFeature(
               data={"flattened_patches": images, "attention_mask": attention_masks}					,     tensor_type=A						)

           return encoded_outputs





"""simple docstring"""




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_lowerCAmelCase    : List[str]							=							[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   rename_state_dict_key(SCREAMING_SNAKE_CASE__    )
          if new_k not in sd:
               raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'    )

          if "dense" in k or "proj" in new_k:
               _UpperCAmelCase      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   torch.tensor(SCREAMING_SNAKE_CASE__						,  dtype=sd[new_k].dtype    )
          assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
     # make sure embedding.padding_idx is respected
     _UpperCAmelCase      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {k: torch.zeros_like(SCREAMING_SNAKE_CASE__    ) for k, v in sd.items() if k.endswith("bias"    ) and k not in mapping}
     mapping.update(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
     ]
     assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], f'no matches found for the following tf keys {extra}'
     return torch_model

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   tf.train.list_variables(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     _UpperCAmelCase      :     Optional[Any]        =   ["Adafactor", "global_step"]
     for name, shape in tqdm(SCREAMING_SNAKE_CASE__						,  desc="converting tf checkpoint to dict"    ):
          _UpperCAmelCase      :     Union[str, Any]        =   any(pat in name for pat in ignore_name    )
          if skip_key:
               continue
          _UpperCAmelCase      :     int        =   tf.train.load_variable(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   array
     return tf_weights

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,  model_max_length=SCREAMING_SNAKE_CASE__    )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(SCREAMING_SNAKE_CASE__    )

     # convert model
     _UpperCAmelCase      :     Union[str, Any]        =   get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          _UpperCAmelCase      :     Optional[int]        =   task_specific_params
     _UpperCAmelCase      :     str        =   convert_pegasus(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   torch_model.state_dict()
     sd.pop("model.decoder.embed_positions.weight"    )
     sd.pop("model.encoder.embed_positions.weight"    )
     torch.save(SCREAMING_SNAKE_CASE__						,  Path(SCREAMING_SNAKE_CASE__    ) / "pytorch_model.bin"    )


if __name__ == "__main__":
 _lowerCAmelCase    : Union[str, Any]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
 parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
 _lowerCAmelCase    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
