
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







def        A__					(						*lowerCamelCase						)  ->	List[Any]:
				if not isinstance(lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							Dict	=						list(lowerCamelCase						)
				for i in range(len(lowerCamelCase						)						):
								UpperCamelCase_:							Tuple	=						None
				gc.collect()
				if is_xpu_available():
								torch.xpu.empty_cache()
				elif is_npu_available():
								torch.npu.empty_cache()
				else:
								torch.cuda.empty_cache()
				return objects







def        A__					(						lowerCamelCase						)  ->	bool:
				UpperCamelCase_:							List[Any]	=						[
				    """CUDA out of memory.""",  # CUDA OOM
				    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
				    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
				]
				if isinstance(lowerCamelCase     ,    lowerCamelCase						) and len(exception.args						) == 1:
								return any(err in exception.args[0] for err in _statements						)
				return False







def        A__					(						lowerCamelCase = None     ,    lowerCamelCase = 1_28						)  ->	Any:
				if function is None:
								return functools.partial(lowerCamelCase     ,    starting_batch_size=lowerCamelCase						)

				UpperCamelCase_:							Dict	=						starting_batch_size

				def decorator(*lowerCamelCase     ,    **lowerCamelCase						):
								nonlocal batch_size
								gc.collect()
								if is_xpu_available():
												torch.xpu.empty_cache()
								elif is_npu_available():
												torch.npu.empty_cache()
								else:
												torch.cuda.empty_cache()
								UpperCamelCase_:							int	=						list(inspect.signature(lowerCamelCase						).parameters.keys()						)
								# Guard against user error
								if len(lowerCamelCase						) < (len(lowerCamelCase						) + 1):
												UpperCamelCase_:							Optional[Any]	=						""", """.join([F'''{arg}={value}''' for arg, value in zip(params[1:]     ,    args[1:]						)]						)
												raise TypeError(
												    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
												    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''						)
								while True:
												if batch_size == 0:
																raise RuntimeError("""No executable batch size found, reached zero."""						)
												try:
																return function(lowerCamelCase     ,    *lowerCamelCase     ,    **lowerCamelCase						)
												except Exception as e:
																if should_reduce_batch_size(lowerCamelCase						):
																				gc.collect()
																				if is_xpu_available():
																								torch.xpu.empty_cache()
																				elif is_npu_available():
																								torch.npu.empty_cache()
																				else:
																								torch.cuda.empty_cache()
																				batch_size //= 2
																else:
																				raise

				return decorator








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






