




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_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     checkpoints.load_tax_checkpoint(a__  )
      SCREAMING_SNAKE_CASE_				:  Any           =     flatten_dict(a__  )
      return flax_params

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[str]           =     {}

      SCREAMING_SNAKE_CASE_				:  List[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',
      }

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {
          '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
                  SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     '.'.join(key[1:]  )

                  # rename the key
                  for old, new in CONVERSION_MAPPING.items():
                        SCREAMING_SNAKE_CASE_				:  List[Any]           =     new_key.replace(a__					,   a__  )

                  if "decoder" in new_key:
                        for old, new in DECODER_CONVERSION_MAPPING.items():
                              SCREAMING_SNAKE_CASE_				:  Dict           =     new_key.replace(a__					,   a__  )

                  if "layers" in new_key and "decoder" not in new_key:
                        # use regex to replace the layer number
                        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     re.sub(R'layers_(\d+)'					,   R'layer.\1'					,   a__  )
                        SCREAMING_SNAKE_CASE_				:  str           =     new_key.replace('encoder'					,   'encoder.encoder'  )

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

                  SCREAMING_SNAKE_CASE_				:  Any           =     flax_dict[key]

      SCREAMING_SNAKE_CASE_				:  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):
                  SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.from_numpy(converted_dict[key].T  )
            else:
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.from_numpy(converted_dict[key]  )

      return converted_torch_dict

def    A_  (							a					,   a					,   a=False					,   a=False  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     get_flax_param(a__  )

      if not use_large:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     PixaStructVisionConfig()
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     PixaStructTextConfig()
      else:
            SCREAMING_SNAKE_CASE_				:  int           =     PixaStructVisionConfig(
                hidden_size=1_5_3_6					,   d_ff=3_9_6_8					,   num_attention_heads=2_4					,   num_hidden_layers=1_8  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     PixaStructTextConfig(hidden_size=1_5_3_6					,   d_ff=3_9_6_8					,   num_heads=2_4					,   num_layers=1_8  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     PixaStructConfig(
          vision_config=encoder_config.to_dict()					,   text_config=decoder_config.to_dict()					,   is_vqa=a__  )

      SCREAMING_SNAKE_CASE_				:  Any           =     PixaStructForConditionalGeneration(a__  )

      SCREAMING_SNAKE_CASE_				:  Any           =     rename_and_convert_flax_params(a__  )
      model.load_state_dict(a__  )

      SCREAMING_SNAKE_CASE_				:  Any           =     AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     PixaStructImageProcessor()
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     PixaStructProcessor(image_processor=a__					,   tokenizer=a__  )

      if use_large:
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     4_0_9_6

      SCREAMING_SNAKE_CASE_				:  Tuple           =     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__":
    lowerCAmelCase     :   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.')
    lowerCAmelCase     :   Optional[int]						     =  parser.parse_args()

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









from math import pi

def    A_  (							a					,   a  ):

      """simple docstring"""



      return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
    print(arc_length(90, 10))






