





def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
							_lowerCamelCase							:     list[list[str]]	=		[[] for _ in range(lowercase_      )]
							_lowerCamelCase							:     Any	=		key - 1

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

							for position, character in enumerate(lowercase_      ):
														_lowerCamelCase							:     Optional[int]	=		position % (lowest * 2)  # puts it in bounds
														_lowerCamelCase							:     Union[str, Any]	=		min(lowercase_ ,    lowest * 2 - num      )  # creates zigzag pattern
														temp_grid[num].append(lowercase_      )
							_lowerCamelCase							:     int	=		[''''''.join(lowercase_      ) for row in temp_grid]
							_lowerCamelCase							:     List[Any]	=		''''''.join(lowercase_      )

							return output_string




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
							_lowerCamelCase							:     str	=		[]
							_lowerCamelCase							:     Optional[int]	=		key - 1

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

							_lowerCamelCase							:     list[list[str]]	=		[[] for _ in range(lowercase_      )]  # generates template
							for position in range(len(lowercase_      )      ):
														_lowerCamelCase							:     str	=		position % (lowest * 2)  # puts it in bounds
														_lowerCamelCase							:     Union[str, Any]	=		min(lowercase_ ,    lowest * 2 - num      )  # creates zigzag pattern
														temp_grid[num].append('''*'''      )

							_lowerCamelCase							:     List[str]	=		0
							for row in temp_grid:  # fills in the characters
														_lowerCamelCase							:     Optional[Any]	=		input_string[counter : counter + len(lowercase_      )]
														grid.append(list(lowercase_      )      )
														counter += len(lowercase_      )

							_lowerCamelCase							:     Optional[Any]	=		''''''  # reads as zigzag
							for position in range(len(lowercase_      )      ):
														_lowerCamelCase							:     Tuple	=		position % (lowest * 2)  # puts it in bounds
														_lowerCamelCase							:     List[str]	=		min(lowercase_ ,    lowest * 2 - num      )  # creates zigzag pattern
														output_string += grid[num][0]
														grid[num].pop(0      )
							return output_string




def    __UpperCAmelCase(      lowercase_      ):
							_lowerCamelCase							:     Any	=		{}
							for key_guess in range(1 ,    len(lowercase_      )      ):  # tries every key
														_lowerCamelCase							:     Optional[Any]	=		decrypt(lowercase_ ,    lowercase_      )
							return results


if __name__ == "__main__":
							import doctest

							doctest.testmod()








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(      lowercase_      ):
       return EnvironmentCommand()




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand(args.accelerate_config_file      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		parser.add_parser('''env''')
             download_parser.set_defaults(func=a__)
             download_parser.add_argument(
                 '''--accelerate-config_file'''      ,							default=a__      ,							help='''The accelerate config file to use for the default values in the launching script.'''      ,							)
             download_parser.set_defaults(func=a__)


      def __init__(		self      ,							a__      ,							*a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		accelerate_config_file


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             if is_safetensors_available():
                    import safetensors

                    _lowerCamelCase							:     Optional[Any]	=		safetensors.__version__
             elif importlib.util.find_spec('''safetensors''') is not None:
                    import safetensors

                    _lowerCamelCase							:     Optional[int]	=		F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             _lowerCamelCase							:     Any	=		'''not found'''
             if is_accelerate_available():
                    import accelerate
                    from accelerate.commands.config import default_config_file, load_config_from_file

                    _lowerCamelCase							:     Optional[int]	=		accelerate.__version__
                    # Get the default from the config file.
                    if self._accelerate_config_file is not None or os.path.isfile(a__):
                           _lowerCamelCase							:     Optional[int]	=		load_config_from_file(self._accelerate_config_file).to_dict()

                    _lowerCamelCase							:     str	=		(
                        '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()])
                        if isinstance(a__      ,							a__)
                        else F"""\t{accelerate_config}"""
                    )

             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Tuple	=		'''NA'''
             if is_torch_available():
                    import torch

                    _lowerCamelCase							:     int	=		torch.__version__
                    _lowerCamelCase							:     List[str]	=		torch.cuda.is_available()

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     Union[str, Any]	=		'''NA'''
             if is_tf_available():
                    import tensorflow as tf

                    _lowerCamelCase							:     List[str]	=		tf.__version__
                    try:
                           # deprecated in v2.1
                           _lowerCamelCase							:     Optional[int]	=		tf.test.is_gpu_available()
                    except AttributeError:
                           # returns list of devices, convert to bool
                           _lowerCamelCase							:     Optional[int]	=		bool(tf.config.list_physical_devices('''GPU'''))

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Optional[int]	=		'''NA'''
             if is_flax_available():
                    import flax
                    import jax
                    import jaxlib

                    _lowerCamelCase							:     Any	=		flax.__version__
                    _lowerCamelCase							:     str	=		jax.__version__
                    _lowerCamelCase							:     Any	=		jaxlib.__version__
                    _lowerCamelCase							:     int	=		jax.lib.xla_bridge.get_backend().platform

             _lowerCamelCase							:     int	=		{
                 '''`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(a__))

             return info



      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





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

