





"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import torch






class 					_SCREAMING_SNAKE_CASE(   TensorFormatter[Mapping, '''torch.Tensor''', Mapping]  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             super().__init__(features=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  torch_tensor_kwargs
             import torch  # noqa import torch at initialization




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             import torch

             if isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) and column:
                   if all(
                       isinstance(SCREAMING_SNAKE_CASE__		,torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
                       for x in column							):
                         return torch.stack(SCREAMING_SNAKE_CASE__							)
             return column




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Union[str, Any]:





             """simple docstring"""






             import torch

             if isinstance(SCREAMING_SNAKE_CASE__		,(str, bytes, type(SCREAMING_SNAKE_CASE__							))							):
                   return value
             elif isinstance(SCREAMING_SNAKE_CASE__		,(np.character, np.ndarray)							) and np.issubdtype(value.dtype		,np.character							):
                   return value.tolist()

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  {}

             if isinstance(SCREAMING_SNAKE_CASE__		,(np.number, np.ndarray)							) and np.issubdtype(value.dtype		,np.integer							):
                   __SCREAMING_SNAKE_CASE   :str						=  {'''dtype''': torch.intaa}
             elif isinstance(SCREAMING_SNAKE_CASE__		,(np.number, np.ndarray)							) and np.issubdtype(value.dtype		,np.floating							):
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {'''dtype''': torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                   import PIL.Image

                   if isinstance(SCREAMING_SNAKE_CASE__		,PIL.Image.Image							):
                         __SCREAMING_SNAKE_CASE   :int						=  np.asarray(SCREAMING_SNAKE_CASE__							)
             return torch.tensor(SCREAMING_SNAKE_CASE__		,**{**default_dtype, **self.torch_tensor_kwargs}							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[Any]:





             """simple docstring"""






             import torch

             # support for torch, tf, jax etc.
             if hasattr(SCREAMING_SNAKE_CASE__		,'''__array__'''							) and not isinstance(SCREAMING_SNAKE_CASE__		,torch.Tensor							):
                   __SCREAMING_SNAKE_CASE   :List[str]						=  data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(SCREAMING_SNAKE_CASE__		,np.ndarray							):
                   if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                         return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__							) for substruct in data_struct]							)
             elif isinstance(SCREAMING_SNAKE_CASE__		,(list, tuple)							):
                   return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__							) for substruct in data_struct]							)
             return self._tensorize(SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      int:





             """simple docstring"""






             return map_nested(self._recursive_tensorize		,SCREAMING_SNAKE_CASE__		,map_list=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Mapping:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE__							)
             return self.recursive_tensorize(SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      "torch.Tensor":





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE__		,pa_table.column_names[0]							)
             __SCREAMING_SNAKE_CASE   :Dict						=  self.recursive_tensorize(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self._consolidate(SCREAMING_SNAKE_CASE__							)
             return column




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Mapping:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :int						=  self.recursive_tensorize(SCREAMING_SNAKE_CASE__							)
             for column_name in batch:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  self._consolidate(batch[column_name]							)
             return batch






"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def      __lowerCamelCase   (							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  ArgumentParser('''Accelerate CLI tool''' ,   usage='''accelerate <command> [<args>]''' ,   allow_abbrev=a_							)
      __SCREAMING_SNAKE_CASE   :str						=  parser.add_subparsers(help='''accelerate command helpers'''							)

      # Register commands
      get_config_parser(subparsers=a_							)
      env_command_parser(subparsers=a_							)
      launch_command_parser(subparsers=a_							)
      tpu_command_parser(subparsers=a_							)
      test_command_parser(subparsers=a_							)

      # Let's go
      __SCREAMING_SNAKE_CASE   :int						=  parser.parse_args()

      if not hasattr(a_ ,   '''func'''							):
            parser.print_help()
            exit(1							)

      # Run
      args.func(a_							)


if __name__ == "__main__":
  main()