





"""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					):






  """simple docstring"""





  A_  :       Optional[int]										=  checkpoints.load_tax_checkpoint(_UpperCAmelCase					)
  A_  :       List[str]										=  flatten_dict(_UpperCAmelCase					)
  return flax_params





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  {}

  A_  :       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',
  }

  A_  :       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
      A_  :       List[str]										=  '.'.join(key[1:]					)

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

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

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

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

      A_  :       Optional[Any]										=  flax_dict[key]

  A_  :       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):
      A_  :       Optional[Any]										=  torch.from_numpy(converted_dict[key].T					)
    else:
      A_  :       Union[str, Any]										=  torch.from_numpy(converted_dict[key]					)

  return converted_torch_dict





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






  """simple docstring"""





  A_  :       Dict										=  get_flax_param(_UpperCAmelCase					)

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

  A_  :       str										=  PixaStructForConditionalGeneration(_UpperCAmelCase					)

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

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

  if use_large:
    A_  :       Tuple										=  4096

  A_  :       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__":
   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_     :						List[str]						=					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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)