






# Copyright 2022 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.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCamelCase__       		=  """Run commands across TPU VMs for initial setup before running `accelerate launch`."""






def 	UpperCamelCase	(							snake_case__  : Optional[int]=None	):




     '''simple docstring'''






     if subparsers is not None:
          __snake_case      :List[str] 							=    subparsers.add_parser("""tpu-config"""						,description=_description	)
     else:
          __snake_case      :Optional[int] 							=    argparse.ArgumentParser("""Accelerate tpu-config command"""						,description=_description	)
     # Core arguments
     __snake_case      :List[Any] 							=    parser.add_argument_group(
         """Config Arguments"""						,"""Arguments that can be configured through `accelerate config`."""	)
     config_args.add_argument(
         """--config_file"""						,type=snake_case__						,default=snake_case__						,help="""Path to the config file to use for accelerate."""						,)
     config_args.add_argument(
         """--tpu_name"""						,default=snake_case__						,help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file."""						,)
     config_args.add_argument(
         """--tpu_zone"""						,default=snake_case__						,help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file."""						,)
     __snake_case      :Tuple 							=    parser.add_argument_group("""TPU Arguments"""						,"""Arguments for options ran inside the TPU."""	)
     pod_args.add_argument(
         """--use_alpha"""						,action="""store_true"""						,help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."""						,)
     pod_args.add_argument(
         """--command_file"""						,default=snake_case__						,help="""The path to the file containing the commands to run on the pod on startup."""						,)
     pod_args.add_argument(
         """--command"""						,action="""append"""						,nargs="""+"""						,help="""A command to run on the pod. Can be passed multiple times."""						,)
     pod_args.add_argument(
         """--install_accelerate"""						,action="""store_true"""						,help="""Whether to install accelerate on the pod. Defaults to False."""						,)
     pod_args.add_argument(
         """--accelerate_version"""						,default="""latest"""						,help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."""						,)
     pod_args.add_argument(
         """--debug"""						,action="""store_true"""						,help="""If set, will print the command that would be run instead of running it."""	)

     if subparsers is not None:
          parser.set_defaults(func=snake_case__	)
     return parser






def 	UpperCamelCase	(							snake_case__  : Tuple	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    None

     # Get the default from the config file if it exists.
     if args.config_file is not None or os.path.isfile(snake_case__	):
          __snake_case      :Optional[Any] 							=    load_config_from_file(args.config_file	)
          if not args.command_file and defaults.command_file is not None and not args.command:
               __snake_case      :Tuple 							=    defaults.command_file
          if not args.command and defaults.commands is not None:
               __snake_case      :Optional[int] 							=    defaults.commands
          if not args.tpu_name:
               __snake_case      :Dict 							=    defaults.tpu_name
          if not args.tpu_zone:
               __snake_case      :Optional[int] 							=    defaults.tpu_zone
     if args.accelerate_version == "dev":
          __snake_case      :List[str] 							=    """git+https://github.com/huggingface/accelerate.git"""
     elif args.accelerate_version == "latest":
          __snake_case      :Optional[Any] 							=    """accelerate -U"""
     elif isinstance(parse(args.accelerate_version	)						,snake_case__	):
          __snake_case      :List[str] 							=    f'''accelerate=={args.accelerate_version}'''

     if not args.command_file and not args.command:
          raise ValueError("""You must specify either a command file or a command to run on the pod."""	)

     if args.command_file:
          with open(args.command_file						,"""r"""	) as f:
               __snake_case      :Dict 							=    [f.read().splitlines()]

    # To turn list of lists into list of strings
     if isinstance(args.command[0]						,snake_case__	):
          __snake_case      :Dict 							=    [line for cmd in args.command for line in cmd]
     # Default to the shared folder and install accelerate
     __snake_case      :str 							=    ["""cd /usr/share"""]
     if args.install_accelerate:
          new_cmd += [f'''pip install {args.accelerate_version}''']
     new_cmd += args.command
     __snake_case      :List[Any] 							=    """; """.join(snake_case__	)

     # Then send it to gcloud
     # Eventually try to use google-api-core to do this instead of subprocess
     __snake_case      :str 							=    ["""gcloud"""]
     if args.use_alpha:
          cmd += ["alpha"]
     cmd += [
         "compute",
         "tpus",
         "tpu-vm",
         "ssh",
         args.tpu_name,
         "--zone",
         args.tpu_zone,
         "--command",
         args.command,
         "--worker",
         "all",
     ]
     if args.debug:
          print(f'''Running {' '.join(snake_case__	)}'''	)
          return
     subprocess.run(snake_case__	)
     print("""Successfully setup pod."""	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    tpu_command_parser()
     __snake_case      :List[str] 							=    parser.parse_args()

     tpu_command_launcher(snake_case__	)










# 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       snake_case__ (				TensorFormatter[Mapping, "torch.Tensor", Mapping]):

       '''simple docstring'''



       def __init__(				self      ,    a__=None      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(features=a__     )
            __snake_case      :int 							=    torch_tensor_kwargs
            import torch  # noqa import torch at initialization



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            import torch

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



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            import torch

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

            __snake_case      :Optional[int] 							=    {}

            if isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.integer     ):
                 __snake_case      :List[Any] 							=    {"""dtype""": torch.intaa}
            elif isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.floating     ):
                 __snake_case      :Tuple 							=    {"""dtype""": torch.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(a__      ,    PIL.Image.Image     ):
                      __snake_case      :Union[str, Any] 							=    np.asarray(a__     )
            return torch.tensor(a__      ,    **{**default_dtype, **self.torch_tensor_kwargs}     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            import torch

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



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





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



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.numpy_arrow_extractor().extract_row(a__     )
            __snake_case      :Any 							=    self.python_features_decoder.decode_row(a__     )
            return self.recursive_tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   "torch.Tensor":






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.numpy_arrow_extractor().extract_column(a__     )
            __snake_case      :List[Any] 							=    self.python_features_decoder.decode_column(a__      ,    pa_table.column_names[0]     )
            __snake_case      :Optional[Any] 							=    self.recursive_tensorize(a__     )
            __snake_case      :Any 							=    self._consolidate(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.numpy_arrow_extractor().extract_batch(a__     )
            __snake_case      :Tuple 							=    self.python_features_decoder.decode_batch(a__     )
            __snake_case      :Tuple 							=    self.recursive_tensorize(a__     )
            for column_name in batch:
                 __snake_case      :str 							=    self._consolidate(batch[column_name]     )
            return batch


