



"""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 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			return EnvironmentCommand()






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			return EnvironmentCommand(args.accelerate_config_file     )

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					@staticmethod
					def 		snake_case	(	lowerCamelCase							:  ArgumentParser					)-> Dict:
								lowerCamelCase__		:		str		     =parser.add_parser('''env'''					)
								download_parser.set_defaults(func=lowerCamelCase					)
								download_parser.add_argument(
								    '''--accelerate-config_file''',  default=lowerCamelCase,  help='''The accelerate config file to use for the default values in the launching script.''',  )
								download_parser.set_defaults(func=lowerCamelCase					)



					def __init__(	self							:  str,  lowerCamelCase							:  Dict,  *lowerCamelCase							:  Tuple					)-> None:
								lowerCamelCase__		:		Dict		     =accelerate_config_file



					def 		snake_case	(	self							:  List[Any]					)-> List[str]:
								lowerCamelCase__		:		List[str]		     ='''not installed'''
								if is_safetensors_available():
											import safetensors

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

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

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

											lowerCamelCase__		:		List[Any]		     =accelerate.__version__
											# Get the default from the config file.
											if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase					):
														lowerCamelCase__		:		List[str]		     =load_config_from_file(self._accelerate_config_file					).to_dict()

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

								lowerCamelCase__		:		List[str]		     ='''not installed'''
								lowerCamelCase__		:		Optional[int]		     ='''NA'''
								if is_torch_available():
											import torch

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

								lowerCamelCase__		:		Dict		     ='''not installed'''
								lowerCamelCase__		:		Any		     ='''NA'''
								if is_tf_available():
											import tensorflow as tf

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

								lowerCamelCase__		:		int		     ='''not installed'''
								lowerCamelCase__		:		Dict		     ='''not installed'''
								lowerCamelCase__		:		Dict		     ='''not installed'''
								lowerCamelCase__		:		Tuple		     ='''NA'''
								if is_flax_available():
											import flax
											import jax
											import jaxlib

											lowerCamelCase__		:		Dict		     =flax.__version__
											lowerCamelCase__		:		Optional[int]		     =jax.__version__
											lowerCamelCase__		:		Dict		     =jaxlib.__version__
											lowerCamelCase__		:		int		     =jax.lib.xla_bridge.get_backend().platform

								lowerCamelCase__		:		str		     ={
								    '''`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(lowerCamelCase					)					)

								return info




					@staticmethod
					def 		snake_case	(	lowerCamelCase							:  str					)-> Optional[int]:
								return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =[0] * len(__lowerCamelCase     )
			lowerCamelCase__		:		List[Any]		     =[]
			lowerCamelCase__		:		List[Any]		     =[1] * len(__lowerCamelCase     )

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(__lowerCamelCase     )     ):
						if indegree[i] == 0:
									queue.append(__lowerCamelCase     )

			while queue:
						lowerCamelCase__		:		Tuple		     =queue.pop(0     )
						for x in graph[vertex]:
									indegree[x] -= 1

									if long_dist[vertex] + 1 > long_dist[x]:
												lowerCamelCase__		:		Optional[Any]		     =long_dist[vertex] + 1

									if indegree[x] == 0:
												queue.append(__lowerCamelCase     )

			print(max(__lowerCamelCase     )     )


# Adjacency list of Graph
_lowercase			:			Optional[Any]										=						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)




