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      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  checkpoints.load_tax_checkpoint(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  flatten_dict(__UpperCamelCase				)
      return flax_params


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  {}

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

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

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

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

                  SCREAMING_SNAKE_CASE_          =  flax_dict[key]

      SCREAMING_SNAKE_CASE_          =  {}
      # 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_          =  torch.from_numpy(converted_dict[key].T				)
            else:
                  SCREAMING_SNAKE_CASE_          =  torch.from_numpy(converted_dict[key]				)

      return converted_torch_dict


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=False     ,      __UpperCamelCase=False				):
      SCREAMING_SNAKE_CASE_          =  get_flax_param(__UpperCamelCase				)

      if not use_large:
            SCREAMING_SNAKE_CASE_          =  PixaStructVisionConfig()
            SCREAMING_SNAKE_CASE_          =  PixaStructTextConfig()
      else:
            SCREAMING_SNAKE_CASE_          =  PixaStructVisionConfig(
                hidden_size=1_5_3_6     ,      d_ff=3_9_6_8     ,      num_attention_heads=2_4     ,      num_hidden_layers=1_8				)
            SCREAMING_SNAKE_CASE_          =  PixaStructTextConfig(hidden_size=1_5_3_6     ,      d_ff=3_9_6_8     ,      num_heads=2_4     ,      num_layers=1_8				)
      SCREAMING_SNAKE_CASE_          =  PixaStructConfig(
          vision_config=encoder_config.to_dict()     ,      text_config=decoder_config.to_dict()     ,      is_vqa=__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  PixaStructForConditionalGeneration(__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  rename_and_convert_flax_params(__UpperCamelCase				)
      model.load_state_dict(__UpperCamelCase				)

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

      if use_large:
            SCREAMING_SNAKE_CASE_          =  4_0_9_6

      SCREAMING_SNAKE_CASE_          =  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     :  List[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     :  Union[str, Any]					     =       parser.parse_args()

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



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


