




"""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 			__UpperCAmelCase			(      __UpperCamelCase     ):
			__lowercase       :   Optional[int]        =    checkpoints.load_tax_checkpoint(_UpperCAmelCase     )
			__lowercase       :   List[str]        =    flatten_dict(_UpperCAmelCase     )
			return flax_params







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			__lowercase       :   Any        =    {}

			__lowercase       :   str        =    {
			    '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       :   Dict        =    {
			    '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       :   List[str]        =    '.'.join(key[1:]     )

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												__lowercase       :   Dict        =    new_key.replace(_UpperCAmelCase     ,							_UpperCAmelCase     )

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															__lowercase       :   int        =    new_key.replace(_UpperCAmelCase     ,							_UpperCAmelCase     )

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

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

									__lowercase       :   Optional[Any]        =    flax_dict[key]

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

			return converted_torch_dict







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=False     ,							__UpperCamelCase=False     ):
			__lowercase       :   Dict        =    get_flax_param(_UpperCAmelCase     )

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

			__lowercase       :   str        =    PixaStructForConditionalGeneration(_UpperCAmelCase     )

			__lowercase       :   Dict        =    rename_and_convert_flax_params(_UpperCAmelCase     )
			model.load_state_dict(_UpperCAmelCase     )

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

			if use_large:
						__lowercase       :   Tuple        =    40_96

			__lowercase       :   Tuple        =    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
			)










"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  A_  :       Tuple										=  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)