





"""simple docstring"""





from __future__ import annotations

from math import pi


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  if (inductance, frequency, reactance).count(0  ) != 1:
    raise ValueError('''One and only one argument must be 0'''  )
  if inductance < 0:
    raise ValueError('''Inductance cannot be negative'''  )
  if frequency < 0:
    raise ValueError('''Frequency cannot be negative'''  )
  if reactance < 0:
    raise ValueError('''Inductive reactance cannot be negative'''  )
  if inductance == 0:
    return {"inductance": reactance / (2 * pi * frequency)}
  elif frequency == 0:
    return {"frequency": reactance / (2 * pi * inductance)}
  elif reactance == 0:
    return {"reactance": 2 * pi * frequency * inductance}
  else:
    raise ValueError('''Exactly one argument must be 0'''  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






"""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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						checkpoints.load_tax_checkpoint(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						flatten_dict(UpperCAmelCase_  )
  return flax_params


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{}

  __UpperCAmelCase    :						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''',
  }

  __UpperCAmelCase    :						Optional[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
      __UpperCAmelCase    :						Optional[Any]       =						'''.'''.join(key[1:]  )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __UpperCAmelCase    :						List[str]       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

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

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

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

      __UpperCAmelCase    :						Dict       =						flax_dict[key]

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

  return converted_torch_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						get_flax_param(UpperCAmelCase_  )

  if not use_large:
    __UpperCAmelCase    :						List[str]       =						PixaStructVisionConfig()
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig()
  else:
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructVisionConfig(
        hidden_size=15_36  ,			d_ff=39_68  ,			num_attention_heads=24  ,			num_hidden_layers=18  )
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig(hidden_size=15_36  ,			d_ff=39_68  ,			num_heads=24  ,			num_layers=18  )
  __UpperCAmelCase    :						Dict       =						PixaStructConfig(
      vision_config=encoder_config.to_dict()  ,			text_config=decoder_config.to_dict()  ,			is_vqa=UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						PixaStructForConditionalGeneration(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						rename_and_convert_flax_params(UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''  )
  __UpperCAmelCase    :						Union[str, Any]       =						PixaStructImageProcessor()
  __UpperCAmelCase    :						Optional[int]       =						PixaStructProcessor(image_processor=UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  )

  if use_large:
    __UpperCAmelCase    :						str       =						40_96

  __UpperCAmelCase    :						str       =						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__":
  lowercase__				:List[str]				   =       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.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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