





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_snake_case           =			logging.get_logger(__name__)





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      ['pixel_values']







  def __init__(      self  :int   ,							_lowercase  :bool = True   ,							_lowercase  :Dict[str, int] = None   ,							_lowercase  :PILImageResampling = PILImageResampling.BICUBIC   ,							_lowercase  :bool = True   ,							_lowercase  :Union[int, float] = 1 / 2_55   ,							_lowercase  :bool = True   ,							_lowercase  :Optional[Union[float, List[float]]] = None   ,							_lowercase  :Optional[Union[float, List[float]]] = None   ,							_lowercase  :bool = True   ,							**_lowercase  :Optional[int]   ,							):






     '''simple docstring'''





     super().__init__(**_lowercase )
     lowercase__			       =  size if size is not None else {"height": 3_84, "width": 3_84}
     lowercase__			       =  get_size_dict(_lowercase   ,							default_to_square=_lowercase )

     lowercase__			       =  do_resize
     lowercase__			       =  size
     lowercase__			       =  resample
     lowercase__			       =  do_rescale
     lowercase__			       =  rescale_factor
     lowercase__			       =  do_normalize
     lowercase__			       =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     lowercase__			       =  image_std if image_std is not None else OPENAI_CLIP_STD
     lowercase__			       =  do_convert_rgb







  def   UpperCAmelCase					(      self  :int   ,							_lowercase  :np.ndarray   ,							_lowercase  :Dict[str, int]   ,							_lowercase  :PILImageResampling = PILImageResampling.BICUBIC   ,							_lowercase  :Optional[Union[str, ChannelDimension]] = None   ,							**_lowercase  :Tuple   ,							):






     '''simple docstring'''





     lowercase__			       =  get_size_dict(_lowercase   ,							default_to_square=_lowercase )
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
     lowercase__			       =  (size["height"], size["width"])
     return resize(_lowercase   ,							size=_lowercase   ,							resample=_lowercase   ,							data_format=_lowercase   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :np.ndarray   ,							_lowercase  :Union[int, float]   ,							_lowercase  :Optional[Union[str, ChannelDimension]] = None   ,							**_lowercase  :Tuple   ,							):






     '''simple docstring'''





     return rescale(_lowercase   ,							scale=_lowercase   ,							data_format=_lowercase   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :np.ndarray   ,							_lowercase  :Union[float, List[float]]   ,							_lowercase  :Union[float, List[float]]   ,							_lowercase  :Optional[Union[str, ChannelDimension]] = None   ,							**_lowercase  :List[Any]   ,							):






     '''simple docstring'''





     return normalize(_lowercase   ,							mean=_lowercase   ,							std=_lowercase   ,							data_format=_lowercase   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :ImageInput   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[Dict[str, int]] = None   ,							_lowercase  :PILImageResampling = None   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[float] = None   ,							_lowercase  :Optional[bool] = None   ,							_lowercase  :Optional[Union[float, List[float]]] = None   ,							_lowercase  :Optional[Union[float, List[float]]] = None   ,							_lowercase  :Optional[Union[str, TensorType]] = None   ,							_lowercase  :bool = None   ,							_lowercase  :ChannelDimension = ChannelDimension.FIRST   ,							**_lowercase  :str   ,							):






     '''simple docstring'''





     lowercase__			       =  do_resize if do_resize is not None else self.do_resize
     lowercase__			       =  resample if resample is not None else self.resample
     lowercase__			       =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__			       =  do_normalize if do_normalize is not None else self.do_normalize
     lowercase__			       =  image_mean if image_mean is not None else self.image_mean
     lowercase__			       =  image_std if image_std is not None else self.image_std
     lowercase__			       =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     lowercase__			       =  size if size is not None else self.size
     lowercase__			       =  get_size_dict(_lowercase   ,							default_to_square=_lowercase )

     lowercase__			       =  make_list_of_images(_lowercase )

     if not valid_images(_lowercase ):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray." )

     if do_resize and size is None or resample is None:
        raise ValueError("Size and resample must be specified if do_resize is True." )

     if do_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True." )

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True." )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
        lowercase__			       =  [convert_to_rgb(_lowercase ) for image in images]

     # All transformations expect numpy arrays.
     lowercase__			       =  [to_numpy_array(_lowercase ) for image in images]

     if do_resize:
        lowercase__			       =  [self.resize(image=_lowercase   ,							size=_lowercase   ,							resample=_lowercase ) for image in images]

     if do_rescale:
        lowercase__			       =  [self.rescale(image=_lowercase   ,							scale=_lowercase ) for image in images]

     if do_normalize:
        lowercase__			       =  [self.normalize(image=_lowercase   ,							mean=_lowercase   ,							std=_lowercase ) for image in images]

     lowercase__			       =  [to_channel_dimension_format(_lowercase   ,							_lowercase ) for image in images]

     lowercase__			       =  BatchFeature(data={"pixel_values": images}   ,							tensor_type=_lowercase )

     return encoded_outputs













import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)


def _A       (   __magic_name__	):
   lowercase__			       =  checkpoints.load_tax_checkpoint(__magic_name__	)
   lowercase__			       =  flatten_dict(__magic_name__	)
   return flax_params


def _A       (   __magic_name__	):
   lowercase__			       =  {}

   lowercase__			       =  {
       "token_embedder": "embeddings",
       "encoder_norm": "layernorm",
       "kernel": "weight",
       ".out": ".output",
       "scale": "weight",
       "embedders_0.pos_embedding": "row_embedder.weight",
       "embedders_1.pos_embedding": "column_embedder.weight",
   }

   lowercase__			       =  {
       "query": "attention.query",
       "key": "attention.key",
       "value": "attention.value",
       "output.dense": "output",
       "encoder_decoder_attention.o": "encoder_decoder_attention.attention.o",
       "pre_self_attention_layer_norm": "self_attention.layer_norm",
       "pre_cross_attention_layer_norm": "encoder_decoder_attention.layer_norm",
       "mlp.": "mlp.DenseReluDense.",
       "pre_mlp_layer_norm": "mlp.layer_norm",
       "self_attention.o": "self_attention.attention.o",
       "decoder.embeddings.embedding": "decoder.embed_tokens.weight",
       "decoder.relpos_bias.rel_embedding": "decoder.layer.0.self_attention.attention.relative_attention_bias.weight",
       "decoder.decoder_norm.weight": "decoder.final_layer_norm.weight",
       "decoder.logits_dense.weight": "decoder.lm_head.weight",
   }

   for key in flax_dict.keys():
      if "target" in key:
         # remove the first prefix from the key
         lowercase__			       =  ".".join(key[1:]	)

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            lowercase__			       =  new_key.replace(__magic_name__  , __magic_name__	)

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               lowercase__			       =  new_key.replace(__magic_name__  , __magic_name__	)

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            lowercase__			       =  re.sub(R"layers_(\d+)"  , R"layer.\1"  , __magic_name__	)
            lowercase__			       =  new_key.replace("encoder"  , "encoder.encoder"	)

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            lowercase__			       =  re.sub(R"layers_(\d+)"  , R"layer.\1"  , __magic_name__	)

         lowercase__			       =  flax_dict[key]

   lowercase__			       =  {}
   # convert converted_dict into torch format
   for key in converted_dict.keys():
      if ("embed_tokens" not in key) and ("embedder" not in key):
         lowercase__			       =  torch.from_numpy(converted_dict[key].T	)
      else:
         lowercase__			       =  torch.from_numpy(converted_dict[key]	)

   return converted_torch_dict


def _A       (   __magic_name__  , __magic_name__  , __magic_name__=False  , __magic_name__=False	):
   lowercase__			       =  get_flax_param(__magic_name__	)

   if not use_large:
      lowercase__			       =  PixaStructVisionConfig()
      lowercase__			       =  PixaStructTextConfig()
   else:
      lowercase__			       =  PixaStructVisionConfig(
          hidden_size=1536  , d_ff=3968  , num_attention_heads=24  , num_hidden_layers=18	)
      lowercase__			       =  PixaStructTextConfig(hidden_size=1536  , d_ff=3968  , num_heads=24  , num_layers=18	)
   lowercase__			       =  PixaStructConfig(
       vision_config=encoder_config.to_dict()  , text_config=decoder_config.to_dict()  , is_vqa=__magic_name__	)

   lowercase__			       =  PixaStructForConditionalGeneration(__magic_name__	)

   lowercase__			       =  rename_and_convert_flax_params(__magic_name__	)
   model.load_state_dict(__magic_name__	)

   lowercase__			       =  AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"	)
   lowercase__			       =  PixaStructImageProcessor()
   lowercase__			       =  PixaStructProcessor(image_processor=__magic_name__  , tokenizer=__magic_name__	)

   if use_large:
      lowercase__			       =  4096

   lowercase__			       =  True

   # mkdir if needed
   os.makedirs(__magic_name__  , exist_ok=__magic_name__	)

   model.save_pretrained(__magic_name__	)
   processor.save_pretrained(__magic_name__	)

   print("Model saved in {}".format(__magic_name__	)	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
  parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
  _snake_case           =			parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )






