

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class   lowercase_   :



						def __init__(      self				:    str      , A__				:    int      , A__				:    Any=sys.maxsize		)   ->					Optional[Any]:
								_snake_case						=							'''bilinear'''
								_snake_case						=							max_size
								_snake_case						=							short_edge_length







						def __call__(      self				:    Optional[Any]      , A__				:    List[str]		)   ->					Dict:
								_snake_case						=							[]
								for img in imgs:
										_snake_case,					_snake_case						=							img.shape[:2]
										# later: provide list and randomly choose index for resize
										_snake_case						=							np.random.randint(self.short_edge_length[0]      , self.short_edge_length[1] + 1		)
										if size == 0:
												return img
										_snake_case						=							size * 1.0 / min(__a      , __a		)
										if h < w:
												_snake_case,					_snake_case						=							size, scale * w
										else:
												_snake_case,					_snake_case						=							scale * h, size
										if max(__a      , __a		) > self.max_size:
												_snake_case						=							self.max_size * 1.0 / max(__a      , __a		)
												_snake_case						=							newh * scale
												_snake_case						=							neww * scale
										_snake_case						=							int(neww + 0.5		)
										_snake_case						=							int(newh + 0.5		)

										if img.dtype == np.uinta:
												_snake_case						=							Image.fromarray(__a		)
												_snake_case						=							pil_image.resize((neww, newh)      , PILImageResampling.BILINEAR		)
												_snake_case						=							np.asarray(__a		)
										else:
												_snake_case						=							img.permute(2      , 0      , 1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												_snake_case						=							nn.functional.interpolate(
												    __a      , (newh, neww)      , mode=self.interp_method      , align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs



class   lowercase_   :



						def __init__(      self				:    Dict      , A__				:    Union[str, Any]		)   ->					int:
								_snake_case						=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      , cfg.INPUT.MAX_SIZE_TEST		)
								_snake_case						=							cfg.INPUT.FORMAT
								_snake_case						=							cfg.SIZE_DIVISIBILITY
								_snake_case						=							cfg.PAD_VALUE
								_snake_case						=							cfg.INPUT.MAX_SIZE_TEST
								_snake_case						=							cfg.MODEL.DEVICE
								_snake_case						=							torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)      , 1      , 1		)
								_snake_case						=							torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)      , 1      , 1		)
								_snake_case						=							lambda A__		: (x - self.pixel_mean) / self.pixel_std



						def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str		)   ->					Optional[Any]:
								_snake_case						=							tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								_snake_case						=							[im.shape[-2:] for im in images]
								_snake_case						=							[
								    nn.functional.pad(
								        __a      , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      , value=self.pad_value      , )
								    for size, im in zip(__a      , __a		)
								]

								return torch.stack(__a		), torch.tensor(__a		)







						def __call__(      self				:    Tuple      , A__				:    Union[str, Any]      , A__				:    int=False		)   ->					Any:
								with torch.no_grad():
										if not isinstance(__a      , __a		):
												_snake_case						=							[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i]      , torch.Tensor		):
														images.insert(__a      , images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i]      , torch.Tensor		):
														images.insert(
														    __a      , torch.as_tensor(img_tensorize(images.pop(__a		)      , input_format=self.input_format		)		)
														    .to(self.device		)
														    .float()      , )
            # resize smallest edge
										_snake_case						=							torch.tensor([im.shape[:2] for im in images]		)
										_snake_case						=							self.aug(__a		)
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										_snake_case						=							[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										_snake_case,					_snake_case						=							self.pad(__a		)
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										_snake_case						=							torch.true_divide(__a      , __a		)
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



		"""simple docstring"""






		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes



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



		"""simple docstring"""






		assert torch.isfinite(__snake_case					).all(), "Box tensor contains infinite or NaN!"
		_snake_case,					_snake_case						=							box_size
		tensor[:, 0].clamp_(min=0			,	max=__snake_case					)
		tensor[:, 1].clamp_(min=0			,	max=__snake_case					)
		tensor[:, 2].clamp_(min=0			,	max=__snake_case					)
		tensor[:, 3].clamp_(min=0			,	max=__snake_case					)




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 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							checkpoints.load_tax_checkpoint(_UpperCamelCase					)
  _snake_case						=							flatten_dict(_UpperCamelCase					)
  return flax_params



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							{}

  _snake_case						=							{
      '''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''',
  }

  _snake_case						=							{
      '''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
      _snake_case						=							'''.'''.join(key[1:]					)

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

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

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

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

      _snake_case						=							flax_dict[key]

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

  return converted_torch_dict



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=False			,	_UpperCamelCase=False					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							get_flax_param(_UpperCamelCase					)

  if not use_large:
    _snake_case						=							PixaStructVisionConfig()
    _snake_case						=							PixaStructTextConfig()
  else:
    _snake_case						=							PixaStructVisionConfig(
        hidden_size=1_536			,	d_ff=3_968			,	num_attention_heads=24			,	num_hidden_layers=18					)
    _snake_case						=							PixaStructTextConfig(hidden_size=1_536			,	d_ff=3_968			,	num_heads=24			,	num_layers=18					)
  _snake_case						=							PixaStructConfig(
      vision_config=encoder_config.to_dict()			,	text_config=decoder_config.to_dict()			,	is_vqa=_UpperCamelCase					)

  _snake_case						=							PixaStructForConditionalGeneration(_UpperCamelCase					)

  _snake_case						=							rename_and_convert_flax_params(_UpperCamelCase					)
  model.load_state_dict(_UpperCamelCase					)

  _snake_case						=							AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
  _snake_case						=							PixaStructImageProcessor()
  _snake_case						=							PixaStructProcessor(image_processor=_UpperCamelCase			,	tokenizer=_UpperCamelCase					)

  if use_large:
    _snake_case						=							4_096

  _snake_case						=							True

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

  model.save_pretrained(_UpperCamelCase					)
  processor.save_pretrained(_UpperCamelCase					)

  print('''Model saved in {}'''.format(_UpperCamelCase					)					)


if __name__ == "__main__":
 __A      		=     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.''')
 __A      		=     parser.parse_args()

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

