'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCAmelCase     							=logging.get_logger(__name__)




class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		List[Any]  ,				a		:		int  ,				a		:		int  ,				a		:		float  ,				**a		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  feature_size
         __lowerCamelCase		=  sampling_rate
         __lowerCamelCase		=  padding_value

         __lowerCamelCase		=  kwargs.pop('''padding_side'''  ,				'''right'''    )
         __lowerCamelCase		=  kwargs.pop('''return_attention_mask'''  ,				a    )

         super().__init__(**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ]  ,				a		:		Union[bool, str, PaddingStrategy] = True  ,				a		:		Optional[int] = None  ,				a		:		bool = False  ,				a		:		Optional[int] = None  ,				a		:		Optional[bool] = None  ,				a		:		Optional[Union[str, TensorType]] = None  ,				):





         """simple docstring"""



         if isinstance(a  ,				(list, tuple)    ) and isinstance(processed_features[0]  ,				(dict, BatchFeature)    ):
             __lowerCamelCase		=  {
                 key: [example[key] for example in processed_features] for key in processed_features[0].keys()
             }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
             raise ValueError(
                 '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
                 f""" to this method that includes {self.model_input_names[0]}, but you provided"""
                 f""" {list(processed_features.keys()    )}"""    )

         __lowerCamelCase		=  processed_features[self.model_input_names[0]]
         __lowerCamelCase		=  (
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(a    ) == 0:
             if return_attention_mask:
                 __lowerCamelCase		=  []
             return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         __lowerCamelCase		=  required_input[0]
         if isinstance(a  ,				(list, tuple)    ):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
             __lowerCamelCase		=  0
             while len(required_input[index]    ) == 0:
                 index += 1
             if index < len(a    ):
                 __lowerCamelCase		=  required_input[index][0]

         if return_tensors is None:
             if is_tf_tensor(a    ):
                 __lowerCamelCase		=  '''tf'''
             elif is_torch_tensor(a    ):
                 __lowerCamelCase		=  '''pt'''
             elif isinstance(a  ,				(int, float, list, tuple, np.ndarray)    ):
                 __lowerCamelCase		=  '''np'''
             else:
                 raise ValueError(
                     f"""type of {first_element} unknown: {type(a    )}. """
                     '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

         for key, value in processed_features.items():
             if isinstance(value[0]  ,				(int, float)    ):
                 __lowerCamelCase		=  to_numpy(a    )
             else:
                 __lowerCamelCase		=  [to_numpy(a    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         __lowerCamelCase		=  self._get_padding_strategies(padding=a  ,				max_length=a    )

         __lowerCamelCase		=  processed_features[self.model_input_names[0]]

         __lowerCamelCase		=  len(a    )
         if not all(len(a    ) == batch_size for v in processed_features.values()    ):
             raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

         __lowerCamelCase		=  []
         for i in range(a    ):
             __lowerCamelCase		=  {k: v[i] for k, v in processed_features.items()}
             # truncation
             __lowerCamelCase		=  self._truncate(
                 a  ,				max_length=a  ,				pad_to_multiple_of=a  ,				truncation=a  ,				)
             truncated_inputs.append(a    )

         if padding_strategy == PaddingStrategy.LONGEST:
             # make sure that `max_length` cannot be longer than the longest truncated length
             __lowerCamelCase		=  max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
             __lowerCamelCase		=  PaddingStrategy.MAX_LENGTH

         __lowerCamelCase		=  {}
         for i in range(a    ):
             # padding
             __lowerCamelCase		=  self._pad(
                 truncated_inputs[i]  ,				max_length=a  ,				padding_strategy=a  ,				pad_to_multiple_of=a  ,				return_attention_mask=a  ,				)

             for key, value in outputs.items():
                 if key not in batch_outputs:
                     __lowerCamelCase		=  []
                 if value.dtype is np.dtype(np.floataa    ):
                     __lowerCamelCase		=  value.astype(np.floataa    )
                 batch_outputs[key].append(a    )

         return BatchFeature(a  ,				tensor_type=a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Union[Dict[str, np.ndarray], BatchFeature]  ,				a		:		Optional[int] = None  ,				a		:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD  ,				a		:		Optional[int] = None  ,				a		:		Optional[bool] = None  ,				):





         """simple docstring"""



         __lowerCamelCase		=  processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
             __lowerCamelCase		=  len(a    )

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             __lowerCamelCase		=  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __lowerCamelCase		=  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a    ) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
             __lowerCamelCase		=  np.ones(len(a    )  ,				dtype=np.intaa    )

         if needs_to_be_padded:
             __lowerCamelCase		=  max_length - len(a    )
             if self.padding_side == "right":
                 if return_attention_mask:
                     __lowerCamelCase		=  np.pad(
                         processed_features['''attention_mask''']  ,				(0, difference)    )
                 __lowerCamelCase		=  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                 __lowerCamelCase		=  np.pad(
                     a  ,				a  ,				'''constant'''  ,				constant_values=self.padding_value    )
             elif self.padding_side == "left":
                 if return_attention_mask:
                     __lowerCamelCase		=  np.pad(
                         processed_features['''attention_mask''']  ,				(difference, 0)    )
                 __lowerCamelCase		=  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                 __lowerCamelCase		=  np.pad(
                     a  ,				a  ,				'''constant'''  ,				constant_values=self.padding_value    )
             else:
                 raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

         return processed_features






     def    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		Union[Dict[str, np.ndarray], BatchFeature]  ,				a		:		Optional[int] = None  ,				a		:		Optional[int] = None  ,				a		:		Optional[bool] = None  ,				):





         """simple docstring"""



         if not truncation:
             return processed_features
         elif truncation and max_length is None:
             raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

         __lowerCamelCase		=  processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             __lowerCamelCase		=  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __lowerCamelCase		=  len(a    ) > max_length

         if needs_to_be_truncated:
             __lowerCamelCase		=  processed_features[self.model_input_names[0]][:max_length]
             if "attention_mask" in processed_features:
                 __lowerCamelCase		=  processed_features['''attention_mask'''][:max_length]

         return processed_features






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		Any=False  ,				a		:		Any=None    ):





         """simple docstring"""



         if padding is not False:
             if padding is True:
                 __lowerCamelCase		=  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(a  ,				a    ):
                 __lowerCamelCase		=  PaddingStrategy(a    )
             elif isinstance(a  ,				a    ):
                 __lowerCamelCase		=  padding
         else:
             __lowerCamelCase		=  PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
             if padding_strategy == PaddingStrategy.MAX_LENGTH:
                 raise ValueError(
                     f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
             raise ValueError(
                 '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
                 ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

         return padding_strategy

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )	)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""" ,     type=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ) ,     )

  # rest from the training program
  parser.add_argument("""training_script_args""" ,     nargs=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

  xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores	)


if __name__ == "__main__":
   main()
