






'''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_     (    lowercase__):
 lowerCamelCase__  =     checkpoints.load_tax_checkpoint(lowercase__)
 lowerCamelCase__  =     flatten_dict(lowercase__)
 return flax_params






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     {}

 lowerCamelCase__  =     {
     "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",
 }

 lowerCamelCase__  =     {
     "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
   lowerCamelCase__  =     ".".join(key[1:])

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

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

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

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

   lowerCamelCase__  =     flax_dict[key]

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

 return converted_torch_dict






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__=False				,     lowercase__=False):
 lowerCamelCase__  =     get_flax_param(lowercase__)

 if not use_large:
  lowerCamelCase__  =     PixaStructVisionConfig()
  lowerCamelCase__  =     PixaStructTextConfig()
 else:
  lowerCamelCase__  =     PixaStructVisionConfig(
      hidden_size=1536				,     d_ff=3968				,     num_attention_heads=24				,     num_hidden_layers=18)
  lowerCamelCase__  =     PixaStructTextConfig(hidden_size=1536				,     d_ff=3968				,     num_heads=24				,     num_layers=18)
 lowerCamelCase__  =     PixaStructConfig(
     vision_config=encoder_config.to_dict()				,     text_config=decoder_config.to_dict()				,     is_vqa=lowercase__)

 lowerCamelCase__  =     PixaStructForConditionalGeneration(lowercase__)

 lowerCamelCase__  =     rename_and_convert_flax_params(lowercase__)
 model.load_state_dict(lowercase__)

 lowerCamelCase__  =     AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer")
 lowerCamelCase__  =     PixaStructImageProcessor()
 lowerCamelCase__  =     PixaStructProcessor(image_processor=lowercase__				,     tokenizer=lowercase__)

 if use_large:
  lowerCamelCase__  =     4096

 lowerCamelCase__  =     True

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

 model.save_pretrained(lowercase__)
 processor.save_pretrained(lowercase__)

 print("Model saved in {}".format(lowercase__))


if __name__ == "__main__":
 __A							:							Optional[int]  							=		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							:							Dict  							=		parser.parse_args()

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









'''simple docstring'''





from ..utils import DummyObject, requires_backends
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     List[str]				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     str				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     Any     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     str				,     *__lowerCamelCase     :     List[Any]				,     **__lowerCamelCase     :     int     )							->							List[str]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Dict				,     *__lowerCamelCase     :     Any				,     **__lowerCamelCase     :     str     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Dict     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     Dict				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     List[str]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     int				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Tuple				,     **__lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     List[Any]				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     List[Any]     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )

