
"""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					(		snake_case__ :  Tuple  ):
	return EnvironmentCommand()






def    _snake_case					(		snake_case__ :  List[str]  ):
	return EnvironmentCommand(args.accelerate_config_file  )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   ArgumentParser   )  -> Any:
							A     = 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  :   List[Any]	,A_  :   Optional[int]	,*A_  :   Tuple   )  -> None:
							A     = accelerate_config_file


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Union[str, Any]:
							A     = 'not installed'
							if is_safetensors_available():
								import safetensors

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

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

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

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

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

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

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

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

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

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

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

							A     = {
							    '`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_  :   Union[str, Any]   )  -> Dict:
							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]   ) + "\n"



def 	_UpperCamelCase					(    )    ->list[list[int]]:
   return [list(range(1_0_0_0 - i	,    -1_0_0_0 - i	,    -1 ) ) for i in range(1_0_0_0 )]


__a	 =      generate_large_matrix()
__a	 =      (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->None:
   assert all(row == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for row in grid )
   assert all(list(lowerCAmelCase_ ) == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for col in zip(*lowerCAmelCase_ ) )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) - 1

   # Edge cases such as no values or all numbers are negative.
   if not array or array[0] < 0:
      return 0

   while right + 1 > left:
      UpperCAmelCase      					=  (left + right) // 2
      UpperCAmelCase      					=  array[mid]

      # Num must be negative and the index must be greater than or equal to 0.
      if num < 0 and array[mid - 1] >= 0:
         return mid

      if num >= 0:
         UpperCAmelCase      					=  mid + 1
      else:
         UpperCAmelCase      					=  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
   return len(lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(grid[0] )

   for i in range(len(lowerCAmelCase_ ) ):
      UpperCAmelCase      					=  find_negative_index(grid[i][:bound] )
      total += bound
   return (len(lowerCAmelCase_ ) * len(grid[0] )) - total
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   return len([number for row in grid for number in row if number < 0] )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   for row in grid:
      for i, number in enumerate(lowerCAmelCase_ ):
         if number < 0:
            total += len(lowerCAmelCase_ ) - i
            break
   return total
def 	_UpperCamelCase					(    )    ->None:
   from timeit import timeit

   print("""Running benchmarks""" )
   UpperCAmelCase      					=  (
       """from __main__ import count_negatives_binary_search, """
       """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
   )
   for func in (
       "count_negatives_binary_search",  # took 0.7727 seconds
       "count_negatives_brute_force_with_break",  # took 4.6505 seconds
       "count_negatives_brute_force",  # took 12.8160 seconds
   ):
      UpperCAmelCase      					=  timeit(F"""{func}(grid=grid)"""	,    setup=lowerCAmelCase_	,    number=5_0_0 )
      print(F"""{func}() took {time:0.4f} seconds""" )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()



