code: 
  
  """simple docstring"""
  import importlib
  import importlib.metadata
  import os
  import warnings
  from distutils.util import strtobool
  from functools import lru_cache
  
  import torch
  from packaging import version
  from packaging.version import parse
  
  from .environment import parse_flag_from_env
  from .versions import compare_versions, is_torch_version
  
  
  try:
      import torch_xla.core.xla_model as xm  # noqa: F401
  
      _A						=     True
  except ImportError:
      _A						=     False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  _A						=     torch.distributed.is_available()
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   str:
        # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
        UpperCAmelCase__ :       Tuple						=						importlib.util.find_spec(lowerCAmelCase						) is not None
        if package_exists:
              try:
                    UpperCAmelCase__ :       Union[str, Any]						=						importlib.metadata.metadata(lowerCAmelCase						)
                    return True
              except importlib.metadata.PackageNotFoundError:
                    return False
  
  
  
  
  def 				a__	(	)  ->   bool:
        return _torch_distributed_available
  
  
  
  
  def 				a__	(	)  ->   Tuple:
        try:
              pass
        except ImportError:
              print(
                  """Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"""
                  """ detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"""
                  """ Bindings for PyTorch*."""						)
        return (
            importlib.util.find_spec("""torch_ccl"""						) is not None
            or importlib.util.find_spec("""oneccl_bindings_for_pytorch"""						) is not None
        )
  
  
  
  
  def 				a__	(	)  ->   Tuple:
        return importlib.metadata.version("""oneccl_bind_pt"""						)
  
  
  
  
  def 				a__	(	)  ->   Any:
        return _is_package_available("""transformer_engine"""						)
  
  
  
  
  @lru_cache
  def 				a__	(	lowerCAmelCase=True						)  ->   Any:
        # Due to bugs on the amp series GPUs, we disable torch-xla on them
        if torch.cuda.is_available():
              return False
        if _tpu_available and check_device:
              try:
                    # Will raise a RuntimeError if no XLA configuration is found
                    UpperCAmelCase__ :       Optional[int]						=						xm.xla_device()
                    return True
              except RuntimeError:
                    return False
        return _tpu_available
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        return _is_package_available("""deepspeed"""						)
  
  
  
  
  def 				a__	(	lowerCAmelCase=False						)  ->   List[Any]:
        if is_tpu_available():
              return not ignore_tpu
        if torch.cuda.is_available():
              return torch.cuda.is_bfaa_supported()
        if is_npu_available():
              return False
        return True
  
  
  
  
  def 				a__	(	)  ->   str:
        UpperCAmelCase__ :       Optional[Any]						=						_is_package_available("""bitsandbytes"""						)
        if package_exists:
              UpperCAmelCase__ :       Optional[int]						=						version.parse(importlib.metadata.version("""bitsandbytes"""						)						)
              return compare_versions(lowerCAmelCase ,  """>=""" ,  """0.39.0"""						)
        return False
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        UpperCAmelCase__ :       Optional[Any]						=						_is_package_available("""bitsandbytes"""						)
        if package_exists:
              UpperCAmelCase__ :       List[Any]						=						version.parse(importlib.metadata.version("""bitsandbytes"""						)						)
              return compare_versions(lowerCAmelCase ,  """>=""" ,  """0.37.2"""						)
        return False
  
  
  
  
  def 				a__	(	)  ->   str:
        return _is_package_available("""bitsandbytes"""						)
  
  
  
  
  def 				a__	(	)  ->   List[str]:
        if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM""" ,  """False"""						)						) == 1:
              UpperCAmelCase__ :       Tuple						=						importlib.util.find_spec("""megatron"""						) is not None
              if package_exists:
                    try:
                          UpperCAmelCase__ :       int						=						parse(importlib.metadata.version("""megatron-lm"""						)						)
                          return compare_versions(lowerCAmelCase ,  """>=""" ,  """2.2.0"""						)
                    except Exception as e:
                          warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""						)
                          return False
  
  
  
  
  def 				a__	(	)  ->   Tuple:
        return _is_package_available("""safetensors"""						)
  
  
  
  
  def 				a__	(	)  ->   List[str]:
        return _is_package_available("""transformers"""						)
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        return _is_package_available("""datasets"""						)
  
  
  
  
  def 				a__	(	)  ->   Optional[Any]:
        return _is_package_available("""aim"""						)
  
  
  
  
  def 				a__	(	)  ->   Dict:
        return _is_package_available("""tensorboard"""						) or _is_package_available("""tensorboardX"""						)
  
  
  
  
  def 				a__	(	)  ->   int:
        return _is_package_available("""wandb"""						)
  
  
  
  
  def 				a__	(	)  ->   Dict:
        return _is_package_available("""comet_ml"""						)
  
  
  
  
  def 				a__	(	)  ->   str:
        return _is_package_available("""boto3"""						)
  
  
  
  
  def 				a__	(	)  ->   List[str]:
        if _is_package_available("""rich"""						):
              if "ACCELERATE_DISABLE_RICH" in os.environ:
                    warnings.warn(
                        """`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."""						)
                    return not parse_flag_from_env("""ACCELERATE_DISABLE_RICH""" ,  lowerCAmelCase						)
              return parse_flag_from_env("""ACCELERATE_ENABLE_RICH""" ,  lowerCAmelCase						)
        return False
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        return _is_package_available("""sagemaker"""						)
  
  
  
  
  def 				a__	(	)  ->   Any:
        return _is_package_available("""tqdm"""						)
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        return _is_package_available("""mlflow"""						)
  
  
  
  
  def 				a__	(	)  ->   Dict:
        return is_torch_version(""">=""" ,  """1.12"""						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        def get_major_and_minor_from_version(lowerCAmelCase						):
              return str(version.parse(lowerCAmelCase						).major						) + "." + str(version.parse(lowerCAmelCase						).minor						)
  
        UpperCAmelCase__ :       Any						=						importlib.metadata.version("""torch"""						)
        if importlib.util.find_spec("""intel_extension_for_pytorch"""						) is None:
              return False
        UpperCAmelCase__ :       Union[str, Any]						=						"""N/A"""
        try:
              UpperCAmelCase__ :       List[Any]						=						importlib.metadata.version("""intel_extension_for_pytorch"""						)
        except importlib.metadata.PackageNotFoundError:
              return False
        UpperCAmelCase__ :       int						=						get_major_and_minor_from_version(_torch_version						)
        UpperCAmelCase__ :       str						=						get_major_and_minor_from_version(_ipex_version						)
        if torch_major_and_minor != ipex_major_and_minor:
              warnings.warn(
                  F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
                  F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""						)
              return False
        return True
  
  
  
  
  @lru_cache
  def 				a__	(	lowerCAmelCase=False						)  ->   str:
        if importlib.util.find_spec("""torch"""						) is None or importlib.util.find_spec("""torch_npu"""						) is None:
              return False
  
        import torch
        import torch_npu  # noqa: F401
  
        if check_device:
              try:
                    # Will raise a RuntimeError if no NPU is found
                    UpperCAmelCase__ :       Union[str, Any]						=						torch.npu.device_count()
                    return torch.npu.is_available()
              except RuntimeError:
                    return False
        return hasattr(lowerCAmelCase ,  """npu"""						) and torch.npu.is_available()
  
  
  
  
  
  @lru_cache
  def 				a__	(	lowerCAmelCase=False						)  ->   Optional[int]:
        if not parse_flag_from_env("""ACCELERATE_USE_XPU""" ,  default=lowerCAmelCase						):
              return False
        if is_ipex_available():
              import torch
  
              if is_torch_version("""<=""" ,  """1.12"""						):
                    return False
        else:
              return False
  
        import intel_extension_for_pytorch  # noqa: F401
  
        if check_device:
              try:
                    # Will raise a RuntimeError if no XPU  is found
                    UpperCAmelCase__ :       Dict						=						torch.xpu.device_count()
                    return torch.xpu.is_available()
              except RuntimeError:
                    return False
        return hasattr(lowerCAmelCase ,  """xpu"""						) and torch.xpu.is_available()
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
        UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
        # Encoder
        for layer_index in range(config.num_layers						):
              UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       str						=						tax_attention_key
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
              UpperCAmelCase__ :       List[Any]						=						tax_attention_value
  
              UpperCAmelCase__ :       Any						=						tax_attention_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi
  
              UpperCAmelCase__ :       int						=						tax_mlp_wo
              UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding
  
        # Assigning
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm
  
        # Decoder
        for layer_index in range(config.num_decoder_layers						):
              UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                  """scale"""
              ]
  
              # Encoder-Decoder-Attention
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                  """kernel"""
              ]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
              # MLP
              if split_mlp_wi:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
              UpperCAmelCase__ :       str						=						tax_attention_out
              UpperCAmelCase__ :       Tuple						=						tax_attention_query
              UpperCAmelCase__ :       int						=						tax_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm
  
              UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
              UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
              UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
              UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       str						=						tax_mlp_wi
  
              UpperCAmelCase__ :       Tuple						=						tax_mlp_wo
  
              UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm
  
        # Decoder Normalization
        UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding
  
        # Token Embeddings
        UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
        UpperCAmelCase__ :       int						=						txa_token_embeddings
  
        # LM Head (only in v1.1 checkpoints)
        if "logits_dense" in tax_model["target"]["decoder"]:
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
        flax_model.save_pretrained(lowerCAmelCase						)
        print("""T5X Model was sucessfully converted!"""						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
      parser.add_argument(
          """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
      )
      _A						=     parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  