


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				)    ->				Optional[int]:
  return EnvironmentCommand()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[Any]:
  return EnvironmentCommand(args.accelerate_config_file				)


class 		__magic_name__						(lowercase_				):





     '''simple docstring'''



     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       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:Dict     ,		_a:Union[str, Any]     ,		*_a:List[str]   ):
       snake_case__						= accelerate_config_file

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       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 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]   ) + "\n"




from collections.abc import Callable







class 					lowercase							:




				def __init__(   self ,  snake_case = None			):
											# Stores actual heap items.
											snake_case_     							=       []
											# Stores indexes of each item for supporting updates and deletion.
											snake_case_     							=       {}
											# Stores current size of heap.
											snake_case_     							=       0
											# Stores function used to evaluate the score of an item on which basis ordering
											# will be done.
											snake_case_     							=       key or (lambda snake_case			: x)




				def        a				(   self ,  snake_case			):
											return int((i - 1) / 2			) if i > 0 else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 1			)
											return left if 0 < left < self.size else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 2			)
											return right if 0 < right < self.size else None




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_							,       snake_case_     							=       (
											    self.pos_map[self.arr[j][0]],
											    self.pos_map[self.arr[i][0]],
											)
											# Then swap the items in the list.
											snake_case_							,       snake_case_     							=       self.arr[j], self.arr[i]




				def        a				(   self ,  snake_case ,  snake_case			):
											return self.arr[i][1] < self.arr[j][1]




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._left(snake_case			)
											snake_case_     							=       self._right(snake_case			)
											snake_case_     							=       i

											if left is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       left
											if right is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       right

											return valid_parent




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._parent(snake_case			)
											while parent is not None and not self._cmp(snake_case ,  snake_case			):
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       parent, self._parent(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._get_valid_parent(snake_case			)
											while valid_parent != index:
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       valid_parent, self._get_valid_parent(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											snake_case_     							=       [item, self.key(snake_case			)]
											# Make sure heap is right in both up and down direction.
											# Ideally only one of them will make any change.
											self._heapify_up(snake_case			)
											self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											del self.pos_map[item]
											snake_case_     							=       self.arr[self.size - 1]
											snake_case_     							=       index
											self.size -= 1
											# Make sure heap is right in both up and down direction. Ideally only one
											# of them will make any change- so no performance loss in calling both.
											if self.size > index:
																		self._heapify_up(snake_case			)
																		self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       len(self.arr			)
											if arr_len == self.size:
																		self.arr.append([item, self.key(snake_case			)]			)
											else:
																		snake_case_     							=       [item, self.key(snake_case			)]
											snake_case_     							=       self.size
											self.size += 1
											self._heapify_up(self.size - 1			)




				def        a				(   self			):
											return self.arr[0] if self.size else None




				def        a				(   self			):
											snake_case_     							=       self.get_top()
											if top_item_tuple:
																		self.delete_item(top_item_tuple[0]			)
											return top_item_tuple





def    __lowerCamelCase      (					):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




