


'''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_      (   A__ :       List[str] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Any      =       checkpoints.load_tax_checkpoint(A__ )
  lowerCAmelCase_      :				Optional[int]      =       flatten_dict(A__ )
  return flax_params







def     UpperCamelCase_      (   A__ :       Optional[int] ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       {}

  lowerCAmelCase_      :				int      =       {
      """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_      :				Optional[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
      lowerCAmelCase_      :				Dict      =       """.""".join(key[1:] )

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

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          lowerCAmelCase_      :				str      =       new_key.replace(A__							,   A__ )

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

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        lowerCAmelCase_      :				Optional[int]      =       re.sub(R"""layers_(\d+)"""							,   R"""layer.\1"""							,   A__ )

      lowerCAmelCase_      :				str      =       flax_dict[key]

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

  return converted_torch_dict







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[Any]							,   A__ :       int=False							,   A__ :       Optional[Any]=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       get_flax_param(A__ )

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

  lowerCAmelCase_      :				List[str]      =       PixaStructForConditionalGeneration(A__ )

  lowerCAmelCase_      :				List[Any]      =       rename_and_convert_flax_params(A__ )
  model.load_state_dict(A__ )

  lowerCAmelCase_      :				Optional[Any]      =       AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
  lowerCAmelCase_      :				Any      =       PixaStructImageProcessor()
  lowerCAmelCase_      :				Any      =       PixaStructProcessor(image_processor=A__							,   tokenizer=A__ )

  if use_large:
    lowerCAmelCase_      :				Optional[int]      =       40_96

  lowerCAmelCase_      :				Dict      =       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 :  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 :  Optional[Any]    							=				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 List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A :  Dict    							=				logging.get_logger(__name__)

__A :  int    							=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'efficientformer'

    def __init__(       self   :  Any   ,						lowerCamelCase   :  List[int] = [3, 2, 6, 4]   ,						lowerCamelCase   :  List[int] = [48, 96, 2_24, 4_48]   ,						lowerCamelCase   :  List[bool] = [True, True, True, True]   ,						lowerCamelCase   :  int = 4_48   ,						lowerCamelCase   :  int = 32   ,						lowerCamelCase   :  int = 4   ,						lowerCamelCase   :  int = 7   ,						lowerCamelCase   :  int = 5   ,						lowerCamelCase   :  int = 8   ,						lowerCamelCase   :  int = 4   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  int = 16   ,						lowerCamelCase   :  int = 3   ,						lowerCamelCase   :  int = 3   ,						lowerCamelCase   :  int = 3   ,						lowerCamelCase   :  int = 2   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  float = 1E-5   ,						lowerCamelCase   :  str = "gelu"   ,						lowerCamelCase   :  float = 0.02   ,						lowerCamelCase   :  float = 1E-12   ,						lowerCamelCase   :  int = 2_24   ,						lowerCamelCase   :  float = 1E-05   ,						**lowerCamelCase   :  int   ,						)       ->  None:
      super().__init__(**lowerCamelCase		)

      lowerCAmelCase_      :				List[str]      =       hidden_act
      lowerCAmelCase_      :				List[Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       hidden_sizes
      lowerCAmelCase_      :				List[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				Union[str, Any]      =       num_attention_heads
      lowerCAmelCase_      :				Tuple      =       initializer_range
      lowerCAmelCase_      :				Union[str, Any]      =       layer_norm_eps
      lowerCAmelCase_      :				int      =       patch_size
      lowerCAmelCase_      :				List[str]      =       num_channels
      lowerCAmelCase_      :				Dict      =       depths
      lowerCAmelCase_      :				int      =       mlp_expansion_ratio
      lowerCAmelCase_      :				Optional[Any]      =       downsamples
      lowerCAmelCase_      :				Union[str, Any]      =       dim
      lowerCAmelCase_      :				Union[str, Any]      =       key_dim
      lowerCAmelCase_      :				str      =       attention_ratio
      lowerCAmelCase_      :				Tuple      =       resolution
      lowerCAmelCase_      :				Optional[Any]      =       pool_size
      lowerCAmelCase_      :				str      =       downsample_patch_size
      lowerCAmelCase_      :				Dict      =       downsample_stride
      lowerCAmelCase_      :				str      =       downsample_pad
      lowerCAmelCase_      :				str      =       drop_path_rate
      lowerCAmelCase_      :				List[Any]      =       num_metaad_blocks
      lowerCAmelCase_      :				Tuple      =       distillation
      lowerCAmelCase_      :				Optional[Any]      =       use_layer_scale
      lowerCAmelCase_      :				Dict      =       layer_scale_init_value
      lowerCAmelCase_      :				Optional[Any]      =       image_size
      lowerCAmelCase_      :				Optional[Any]      =       batch_norm_eps



