





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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       return EnvironmentCommand()


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       return EnvironmentCommand(args.accelerate_config_file					)

class  __A(						__UpperCAmelCase ):

     @staticmethod
     def 	_UpperCamelCase     (      A      ):





            """simple docstring"""

            _UpperCamelCase              =						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"""

            _UpperCamelCase              =						accelerate_config_file

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

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

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

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

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

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

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

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

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

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

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

            _UpperCamelCase              =						{
                '''`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 	_UpperCamelCase     (      A      ):





            """simple docstring"""

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













from __future__ import annotations

lowercase    : int    	=  {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}

class  __A:

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						graph
            # mapping node to its parent in resulting breadth first tree
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						source_vertex

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						{self.source_vertex}
            _UpperCamelCase              =						None
            _UpperCamelCase              =						[self.source_vertex]  # first in first out queue

            while queue:
                   _UpperCamelCase              =						queue.pop(0      )
                   for adjacent_vertex in self.graph[vertex]:
                          if adjacent_vertex not in visited:
                                 visited.add(A      )
                                 _UpperCamelCase              =						vertex
                                 queue.append(A      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if target_vertex == self.source_vertex:
                   return self.source_vertex

            _UpperCamelCase              =						self.parent.get(A      )
            if target_vertex_parent is None:
                   _UpperCamelCase              =						(
                       F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
                   )
                   raise ValueError(A      )

            return self.shortest_path(A      ) + F'''->{target_vertex}'''


if __name__ == "__main__":
       lowercase    : Tuple    	=  Graph(graph, """G""")
       g.breath_first_search()
       print(g.shortest_path("""D"""))
       print(g.shortest_path("""G"""))
       print(g.shortest_path("""Foo"""))






