

'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			str:
       A_						=      [[] for _ in range(UpperCAmelCase__				)]
       A_						=      key - 1

       if key <= 0:
              raise ValueError("""Height of grid can't be 0 or negative"""				)
       if key == 1 or len(UpperCAmelCase__				) <= key:
              return input_string

       for position, character in enumerate(UpperCAmelCase__				):
              A_						=      position % (lowest * 2)  # puts it in bounds
              A_						=      min(UpperCAmelCase__,       lowest * 2 - num				)  # creates zigzag pattern
              temp_grid[num].append(UpperCAmelCase__				)
       A_						=      ["""""".join(UpperCAmelCase__				) for row in temp_grid]
       A_						=      """""".join(UpperCAmelCase__				)

       return output_string
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			str:
       A_						=      []
       A_						=      key - 1

       if key <= 0:
              raise ValueError("""Height of grid can't be 0 or negative"""				)
       if key == 1:
              return input_string

       A_						=      [[] for _ in range(UpperCAmelCase__				)]  # generates template
       for position in range(len(UpperCAmelCase__				)				):
              A_						=      position % (lowest * 2)  # puts it in bounds
              A_						=      min(UpperCAmelCase__,       lowest * 2 - num				)  # creates zigzag pattern
              temp_grid[num].append("""*"""				)

       A_						=      0
       for row in temp_grid:  # fills in the characters
              A_						=      input_string[counter : counter + len(UpperCAmelCase__				)]
              grid.append(list(UpperCAmelCase__				)				)
              counter += len(UpperCAmelCase__				)

       A_						=      """"""  # reads as zigzag
       for position in range(len(UpperCAmelCase__				)				):
              A_						=      position % (lowest * 2)  # puts it in bounds
              A_						=      min(UpperCAmelCase__,       lowest * 2 - num				)  # creates zigzag pattern
              output_string += grid[num][0]
              grid[num].pop(0				)
       return output_string
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			dict[int, str]:
       A_						=      {}
       for key_guess in range(1,       len(UpperCAmelCase__				)				):  # tries every key
              A_						=      decrypt(UpperCAmelCase__,       UpperCAmelCase__				)
       return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''







import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Tuple:
       return EnvironmentCommand()
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       return EnvironmentCommand(args.accelerate_config_file				)







class 					A__      (      _snake_case			):



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      parser.add_parser("""env"""       )
           download_parser.set_defaults(func=UpperCamelCase__       )
           download_parser.add_argument(
               """--accelerate-config_file"""		,				default=UpperCamelCase__		,				help="""The accelerate config file to use for the default values in the launching script."""		,				)
           download_parser.set_defaults(func=UpperCamelCase__       )



    def __init__(					self		,				UpperCamelCase__		,				*UpperCamelCase__       )						->      None:





           '''simple docstring'''
           A_						=      accelerate_config_file



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      """not installed"""
           if is_safetensors_available():
                  import safetensors

                  A_						=      safetensors.__version__
           elif importlib.util.find_spec("""safetensors"""       ) is not None:
                  import safetensors

                  A_						=      f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

           A_						=      """not installed"""
           A_						=      A_						=      """not found"""
           if is_accelerate_available():
                  import accelerate
                  from accelerate.commands.config import default_config_file, load_config_from_file

                  A_						=      accelerate.__version__
                  # Get the default from the config file.
                  if self._accelerate_config_file is not None or os.path.isfile(UpperCamelCase__       ):
                         A_						=      load_config_from_file(self._accelerate_config_file       ).to_dict()

                  A_						=      (
                      """\n""".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]       )
                      if isinstance(UpperCamelCase__		,				UpperCamelCase__       )
                      else f'''\t{accelerate_config}'''
                  )

           A_						=      """not installed"""
           A_						=      """NA"""
           if is_torch_available():
                  import torch

                  A_						=      torch.__version__
                  A_						=      torch.cuda.is_available()

           A_						=      """not installed"""
           A_						=      """NA"""
           if is_tf_available():
                  import tensorflow as tf

                  A_						=      tf.__version__
                  try:
                         # deprecated in v2.1
                         A_						=      tf.test.is_gpu_available()
                  except AttributeError:
                         # returns list of devices, convert to bool
                         A_						=      bool(tf.config.list_physical_devices("""GPU"""       )       )

           A_						=      """not installed"""
           A_						=      """not installed"""
           A_						=      """not installed"""
           A_						=      """NA"""
           if is_flax_available():
                  import flax
                  import jax
                  import jaxlib

                  A_						=      flax.__version__
                  A_						=      jax.__version__
                  A_						=      jaxlib.__version__
                  A_						=      jax.lib.xla_bridge.get_backend().platform

           A_						=      {
               """`transformers` version""": version,
               """Platform""": platform.platform(),
               """Python version""": platform.python_version(),
               """Huggingface_hub version""": huggingface_hub.__version__,
               """Safetensors version""": f'''{safetensors_version}''',
               """Accelerate version""": f'''{accelerate_version}''',
               """Accelerate config""": f'''{accelerate_config_str}''',
               """PyTorch version (GPU?)""": f'''{pt_version} ({pt_cuda_available})''',
               """Tensorflow version (GPU?)""": f'''{tf_version} ({tf_cuda_available})''',
               """Flax version (CPU?/GPU?/TPU?)""": f'''{flax_version} ({jax_backend})''',
               """Jax version""": f'''{jax_version}''',
               """JaxLib version""": f'''{jaxlib_version}''',
               """Using GPU in script?""": """<fill in>""",
               """Using distributed or parallel set-up in script?""": """<fill in>""",
           }

           print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""       )
           print(self.format_dict(UpperCamelCase__       )       )

           return info







    @staticmethod
    def 					snake_case_			(					UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]       ) + "\n"
