

"""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        _lowercase    (			__lowerCAmelCase							)				-> Optional[int]:
      SCREAMING_SNAKE_CASE__      :					Tuple											=		checkpoints.load_tax_checkpoint(__a							)
      SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		flatten_dict(__a							)
      return flax_params




def        _lowercase    (			__lowerCAmelCase							)				-> Optional[int]:
      SCREAMING_SNAKE_CASE__      :					Dict											=		{}

      SCREAMING_SNAKE_CASE__      :					Optional[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__      :					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__      :					str											=		'''.'''.join(key[1:]							)

                  # rename the key
                  for old, new in CONVERSION_MAPPING.items():
                        SCREAMING_SNAKE_CASE__      :					str											=		new_key.replace(__a     ,    __a							)

                  if "decoder" in new_key:
                        for old, new in DECODER_CONVERSION_MAPPING.items():
                              SCREAMING_SNAKE_CASE__      :					Any											=		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__      :					int											=		re.sub(r"""layers_(\d+)"""     ,    r"""layer.\1"""     ,    __a							)
                        SCREAMING_SNAKE_CASE__      :					Tuple											=		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__      :					Any											=		re.sub(r"""layers_(\d+)"""     ,    r"""layer.\1"""     ,    __a							)

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

      return converted_torch_dict




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase=False     ,    __lowerCAmelCase=False							)				-> Dict:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		get_flax_param(__a							)

      if not use_large:
            SCREAMING_SNAKE_CASE__      :					Optional[int]											=		PixaStructVisionConfig()
            SCREAMING_SNAKE_CASE__      :					int											=		PixaStructTextConfig()
      else:
            SCREAMING_SNAKE_CASE__      :					Dict											=		PixaStructVisionConfig(
                hidden_size=1536     ,    d_ff=3968     ,    num_attention_heads=24     ,    num_hidden_layers=18							)
            SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		PixaStructTextConfig(hidden_size=1536     ,    d_ff=3968     ,    num_heads=24     ,    num_layers=18							)
      SCREAMING_SNAKE_CASE__      :					Tuple											=		PixaStructConfig(
          vision_config=encoder_config.to_dict()     ,    text_config=decoder_config.to_dict()     ,    is_vqa=__a							)

      SCREAMING_SNAKE_CASE__      :					Dict											=		PixaStructForConditionalGeneration(__a							)

      SCREAMING_SNAKE_CASE__      :					Optional[int]											=		rename_and_convert_flax_params(__a							)
      model.load_state_dict(__a							)

      SCREAMING_SNAKE_CASE__      :					Tuple											=		AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""							)
      SCREAMING_SNAKE_CASE__      :					List[str]											=		PixaStructImageProcessor()
      SCREAMING_SNAKE_CASE__      :					str											=		PixaStructProcessor(image_processor=__a     ,    tokenizer=__a							)

      if use_large:
            SCREAMING_SNAKE_CASE__      :					int											=		4096

      SCREAMING_SNAKE_CASE__      :					int											=		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__":
  a    :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.")
  a    :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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


