




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			(					UpperCamelCase      )     ->	List[str]:
  lowerCAmelCase__				:  Optional[Any]		       =     checkpoints.load_tax_checkpoint(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     flatten_dict(UpperCamelCase      )
  return flax_params



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  Dict		       =     {}

  lowerCAmelCase__				:  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''',
  }

  lowerCAmelCase__				:  List[str]		       =     {
      '''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__				:  Optional[Any]		       =     '''.'''.join(key[1:]      )

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

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          lowerCAmelCase__				:  List[Any]		       =     new_key.replace(UpperCamelCase	,      UpperCamelCase      )

      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'''	,      UpperCamelCase      )
        lowerCAmelCase__				:  Any		       =     new_key.replace('''encoder'''	,      '''encoder.encoder'''      )

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

      lowerCAmelCase__				:  str		       =     flax_dict[key]

  lowerCAmelCase__				:  str		       =     {}
  # 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[int]		       =     torch.from_numpy(converted_dict[key].T      )
    else:
      lowerCAmelCase__				:  str		       =     torch.from_numpy(converted_dict[key]      )

  return converted_torch_dict



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=False	,      UpperCamelCase=False      )     ->	List[Any]:
  lowerCAmelCase__				:  Union[str, Any]		       =     get_flax_param(UpperCamelCase      )

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

  lowerCAmelCase__				:  Tuple		       =     PixaStructForConditionalGeneration(UpperCamelCase      )

  lowerCAmelCase__				:  List[str]		       =     rename_and_convert_flax_params(UpperCamelCase      )
  model.load_state_dict(UpperCamelCase      )

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

  if use_large:
    lowerCAmelCase__				:  Tuple		       =     4096

  lowerCAmelCase__				:  Union[str, Any]		       =     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_					    = 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_					    = parser.parse_args()

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







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
          # modified it, it will not be accepted here again, since `auto` values would have been overridden
          lowerCAmelCase__				:  Union[str, Any]		       =     deepcopy(__UpperCAmelCase		)
        elif os.path.exists(__UpperCAmelCase		):
          with io.open(__UpperCAmelCase      ,				'''r'''      ,				encoding='''utf-8'''		) as f:
            lowerCAmelCase__				:  int		       =     json.load(__UpperCAmelCase		)
        else:
          try:
            lowerCAmelCase__				:  Union[str, Any]		       =     baseaa.urlsafe_baadecode(__UpperCAmelCase		).decode('''utf-8'''		)
            lowerCAmelCase__				:  Optional[int]		       =     json.loads(__UpperCAmelCase		)
          except (UnicodeDecodeError, AttributeError, ValueError):
            raise ValueError(
                f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""		)

        lowerCAmelCase__				:  Optional[Any]		       =     config

        self.set_stage_and_offload()

      def 	__magic_name__(   self		):
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value('''zero_optimization.stage'''      ,				-1		)

        # offload
        lowerCAmelCase__				:  Dict		       =     False
        if self.is_zeroa() or self.is_zeroa():
          lowerCAmelCase__				:  Dict		       =     set(['''cpu''', '''nvme''']		)
          lowerCAmelCase__				:  Optional[Any]		       =     set(
              [
                  self.get_value('''zero_optimization.offload_optimizer.device'''		),
                  self.get_value('''zero_optimization.offload_param.device'''		),
              ]		)
          if len(offload_devices & offload_devices_valid		) > 0:
            lowerCAmelCase__				:  List[Any]		       =     True

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  Tuple		       =     ds_key_long.split('''.'''		)
        lowerCAmelCase__				:  int		       =     nodes.pop()
        for node in nodes:
          lowerCAmelCase__				:  int		       =     config.get(__UpperCAmelCase		)
          if config is None:
            return None, ds_key

        return config, ds_key

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.find_config_node(__UpperCAmelCase		)
        if config is None:
          return default
        return config.get(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False		):
        lowerCAmelCase__				:  Dict		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  str		       =     ds_key_long.split('''.'''		)
        for node in nodes:
          lowerCAmelCase__				:  Dict		       =     config
          lowerCAmelCase__				:  List[str]		       =     config.get(__UpperCAmelCase		)
          if config is None:
            if must_exist:
              raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""		)
            else:
              return

        # if found remove it
        if parent_config is not None:
          parent_config.pop(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else bool(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else not bool(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        return self._stage == 2

      def 	__magic_name__(   self		):
        return self._stage == 3







      def 	__magic_name__(   self		):
        return self._offload






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     engine







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        # runs backpropagation and handles mixed precision
        self.engine.backward(__UpperCAmelCase      ,				**__UpperCAmelCase		)

        # Deepspeed's `engine.step` performs the following operations:
        # - gradient accumulation check
        # - gradient clipping
        # - optimizer step
        # - zero grad
        # - checking overflow
        # - lr_scheduler step (only if engine.lr_scheduler is not None)
        self.engine.step()
        # and this plugin overrides the above calls with no-ops when Accelerate runs under
        # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
        # training loop that works transparently under many training regimes.






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				device_placement=__UpperCAmelCase      ,				scaler=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     hasattr(self.optimizer      ,				'''overflow'''		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







      @property
      def 	__magic_name__(   self		):
        if self.__has_overflow__:
          return self.optimizer.overflow
        return False






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=0.001      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     params
        lowerCAmelCase__				:  str		       =     lr
        lowerCAmelCase__				:  List[str]		       =     weight_decay
        lowerCAmelCase__				:  Dict		       =     kwargs






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     optimizer
        lowerCAmelCase__				:  Optional[Any]		       =     total_num_steps
        lowerCAmelCase__				:  str		       =     warmup_num_steps
        lowerCAmelCase__				:  Dict		       =     kwargs

