


'''simple docstring'''
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 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Any       =			checkpoints.load_tax_checkpoint(A_			)
 __A  :		int       =			flatten_dict(A_			)
 return flax_params


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		Any       =			{}

 __A  :		Union[str, Any]       =			{
     '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',
 }

 __A  :		int       =			{
     '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
   __A  :		str       =			'.'.join(key[1:]			)

   # rename the key
   for old, new in CONVERSION_MAPPING.items():
    __A  :		Optional[int]       =			new_key.replace(A_,				A_			)

   if "decoder" in new_key:
    for old, new in DECODER_CONVERSION_MAPPING.items():
     __A  :		List[str]       =			new_key.replace(A_,				A_			)

   if "layers" in new_key and "decoder" not in new_key:
    # use regex to replace the layer number
    __A  :		Union[str, Any]       =			re.sub(r'layers_(\d+)',				r'layer.\1',				A_			)
    __A  :		Tuple       =			new_key.replace('encoder',				'encoder.encoder'			)

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

   __A  :		Dict       =			flax_dict[key]

 __A  :		Union[str, Any]       =			{}
 # convert converted_dict into torch format
 for key in converted_dict.keys():
  if ("embed_tokens" not in key) and ("embedder" not in key):
   __A  :		List[str]       =			torch.from_numpy(converted_dict[key].T			)
  else:
   __A  :		Optional[Any]       =			torch.from_numpy(converted_dict[key]			)

 return converted_torch_dict


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Dict=False,				UpperCamelCase__       :					List[Any]=False			):
 __A  :		Dict       =			get_flax_param(A_			)

 if not use_large:
  __A  :		Optional[int]       =			PixaStructVisionConfig()
  __A  :		Optional[int]       =			PixaStructTextConfig()
 else:
  __A  :		str       =			PixaStructVisionConfig(
      hidden_size=1536,				d_ff=3968,				num_attention_heads=24,				num_hidden_layers=18			)
  __A  :		str       =			PixaStructTextConfig(hidden_size=1536,				d_ff=3968,				num_heads=24,				num_layers=18			)
 __A  :		Tuple       =			PixaStructConfig(
     vision_config=encoder_config.to_dict(),				text_config=decoder_config.to_dict(),				is_vqa=A_			)

 __A  :		Any       =			PixaStructForConditionalGeneration(A_			)

 __A  :		Union[str, Any]       =			rename_and_convert_flax_params(A_			)
 model.load_state_dict(A_			)

 __A  :		Optional[int]       =			AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'			)
 __A  :		Optional[int]       =			PixaStructImageProcessor()
 __A  :		Tuple       =			PixaStructProcessor(image_processor=A_,				tokenizer=A_			)

 if use_large:
  __A  :		Optional[int]       =			4096

 __A  :		Dict       =			True

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

 model.save_pretrained(A_			)
 processor.save_pretrained(A_			)

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Union[str, Any]								=      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.')
 UpperCAmelCase_ :     Optional[int]								=      parser.parse_args()

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










'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCAmelCase_ :     int								=      ''
UpperCAmelCase_ :     Union[str, Any]								=      ''
UpperCAmelCase_ :     Any								=      ''
UpperCAmelCase_ :     int								=      1  # (0 is vertical, 1 is horizontal)


def 			_lowercase       (				):
 __A     ,__A  :		Optional[int]       =			get_dataset(UpperCamelCase__,				UpperCamelCase__			)
 print('Processing...'			)
 __A     ,__A     ,__A  :		Any       =			update_image_and_anno(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 for index, image in enumerate(UpperCamelCase__			):
  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __A  :		Dict       =			random_chars(32			)
  __A  :		List[Any]       =			paths[index].split(os.sep			)[-1].rsplit('.',				1			)[0]
  __A  :		Dict       =			f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
  cva.imwrite(f"""/{file_root}.jpg""",				UpperCamelCase__,				[cva.IMWRITE_JPEG_QUALITY, 85]			)
  print(f"""Success {index+1}/{len(UpperCamelCase__			)} with {file_name}"""			)
  __A  :		Tuple       =			[]
  for anno in new_annos[index]:
   __A  :		Any       =			f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
   annos_list.append(UpperCamelCase__			)
  with open(f"""/{file_root}.txt""",				'w'			) as outfile:
   outfile.write('\n'.join(line for line in annos_list			)			)


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str			):
 __A  :		Tuple       =			[]
 __A  :		int       =			[]
 for label_file in glob.glob(os.path.join(UpperCamelCase__,				'*.txt'			)			):
  __A  :		Optional[int]       =			label_file.split(os.sep			)[-1].rsplit('.',				1			)[0]
  with open(UpperCamelCase__			) as in_file:
   __A  :		Optional[Any]       =			in_file.readlines()
  __A  :		int       =			os.path.join(UpperCamelCase__,				f"""{label_name}.jpg"""			)

  __A  :		Optional[int]       =			[]
  for obj_list in obj_lists:
   __A  :		str       =			obj_list.rstrip('\n'			).split(' '			)
   boxes.append(
       [
           int(obj[0]			),
           float(obj[1]			),
           float(obj[2]			),
           float(obj[3]			),
           float(obj[4]			),
       ]			)
  if not boxes:
   continue
  img_paths.append(UpperCamelCase__			)
  labels.append(UpperCamelCase__			)
 return img_paths, labels


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					list,				UpperCamelCase__       :					int = 1			):
 __A  :		int       =			[]
 __A  :		Optional[Any]       =			[]
 __A  :		str       =			[]
 for idx in range(len(UpperCamelCase__			)			):
  __A  :		List[Any]       =			[]
  __A  :		List[str]       =			img_list[idx]
  path_list.append(UpperCamelCase__			)
  __A  :		Optional[Any]       =			anno_list[idx]
  __A  :		Union[str, Any]       =			cva.imread(UpperCamelCase__			)
  if flip_type == 1:
   __A  :		int       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Union[str, Any]       =			1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
  elif flip_type == 0:
   __A  :		Tuple       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Dict       =			1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
  new_annos_lists.append(UpperCamelCase__			)
  new_imgs_list.append(UpperCamelCase__			)
 return new_imgs_list, new_annos_lists, path_list


def 			_lowercase       (				UpperCamelCase__       :					int = 32			):
 assert number_char > 1, "The number of character should greater than 1"
 __A  :		int       =			ascii_lowercase + digits
 return "".join(random.choice(UpperCamelCase__			) for _ in range(UpperCamelCase__			)			)


if __name__ == "__main__":
 main()
 print('DONE ✅')






