





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 lowerCamelCase__    (							snake_case_	:  List[str]       )      ->				List[str]:
   return EnvironmentCommand()






def lowerCamelCase__    (							snake_case_	:  List[str]       )      ->				Union[str, Any]:
   return EnvironmentCommand(args.accelerate_config_file       )







class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      @staticmethod
      def    a (a__       :      ArgumentParser      ):





         """simple docstring"""



         __snake_case							       =   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       :      Union[str, Any]				,			a__       :      str				,			*a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   accelerate_config_file


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   '''not installed'''
         if is_safetensors_available():
            import safetensors

            __snake_case							       =   safetensors.__version__
         elif importlib.util.find_spec('''safetensors'''      ) is not None:
            import safetensors

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

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

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

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

         __snake_case							       =   '''not installed'''
         __snake_case							       =   '''NA'''
         if is_torch_available():
            import torch

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

         __snake_case							       =   '''not installed'''
         __snake_case							       =   '''NA'''
         if is_tf_available():
            import tensorflow as tf

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

         __snake_case							       =   '''not installed'''
         __snake_case							       =   '''not installed'''
         __snake_case							       =   '''not installed'''
         __snake_case							       =   '''NA'''
         if is_flax_available():
            import flax
            import jax
            import jaxlib

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

         __snake_case							       =   {
             '''`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    a (a__       :      Dict      ):





         """simple docstring"""



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








"""simple docstring"""




from heapq import heappop, heappush

import numpy as np





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					):






  """simple docstring"""





  A_						,       A_  :       List[str]										=  grid.shape
  A_  :       Optional[int]										=  [-1, 1, 0, 0]
  A_  :       str										=  [0, 0, -1, 1]
  if allow_diagonal:
    dx += [-1, -1, 1, 1]
    dy += [-1, 1, -1, 1]

  A_						,       A_  :       List[Any]										=  [(0, source)], set()
  A_  :       Optional[Any]										=  np.full((rows, cols) ,					np.inf					)
  A_  :       int										=  0
  A_  :       Optional[int]										=  np.empty((rows, cols) ,					dtype=_UpperCAmelCase					)
  A_  :       Optional[int]										=  None

  while queue:
    ((A_)						,       (A_))  :       str										=  heappop(_UpperCAmelCase					)
    if (x, y) in visited:
      continue
    visited.add((x, y)					)

    if (x, y) == destination:
      A_  :       int										=  []
      while (x, y) != source:
        path.append((x, y)					)
        A_						,       A_  :       List[Any]										=  predecessors[x, y]
      path.append(_UpperCAmelCase					)  # add the source manually
      path.reverse()
      return matrix[destination], path

    for i in range(len(_UpperCAmelCase					)					):
      A_						,       A_  :       Tuple										=  x + dx[i], y + dy[i]
      if 0 <= nx < rows and 0 <= ny < cols:
        A_  :       Union[str, Any]										=  grid[nx][ny]
        if next_node == 1 and matrix[nx, ny] > dist + 1:
          heappush(_UpperCAmelCase ,					(dist + 1, (nx, ny))					)
          A_  :       Optional[Any]										=  dist + 1
          A_  :       Optional[Any]										=  (x, y)

  return np.inf, []


if __name__ == "__main__":
   import doctest

   doctest.testmod()