






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        UpperCAmelCase__		(						lowerCamelCase_       :       int				):
			return EnvironmentCommand()

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]				):
			return EnvironmentCommand(args.accelerate_config_file				)






class 			_UpperCamelCase(       __lowerCamelCase		):

		@staticmethod
		def    __lowerCAmelCase  (					SCREAMING_SNAKE_CASE__		:    ArgumentParser    ):



					'''simple docstring'''



					__a						:  Any									=     parser.add_parser('env'    )
					download_parser.set_defaults(func=SCREAMING_SNAKE_CASE__    )
					download_parser.add_argument(
					    '--accelerate-config_file'    ,       default=SCREAMING_SNAKE_CASE__    ,       help='The accelerate config file to use for the default values in the launching script.'    ,       )
					download_parser.set_defaults(func=SCREAMING_SNAKE_CASE__    )

		def __init__(					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       *SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     accelerate_config_file

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  int									=     'not installed'
					if is_safetensors_available():
								import safetensors

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

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

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

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

								__a						:  List[Any]									=     (
								    '\n'.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]    )
								    if isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
								    else f'''\t{accelerate_config}'''
								)

					__a						:  str									=     'not installed'
					__a						:  Union[str, Any]									=     'NA'
					if is_torch_available():
								import torch

								__a						:  Union[str, Any]									=     torch.__version__
								__a						:  Union[str, Any]									=     torch.cuda.is_available()

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

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

					__a						:  str									=     'not installed'
					__a						:  Union[str, Any]									=     'not installed'
					__a						:  Optional[int]									=     'not installed'
					__a						:  int									=     'NA'
					if is_flax_available():
								import flax
								import jax
								import jaxlib

								__a						:  Optional[Any]									=     flax.__version__
								__a						:  Tuple									=     jax.__version__
								__a						:  Optional[Any]									=     jaxlib.__version__
								__a						:  Optional[int]									=     jax.lib.xla_bridge.get_backend().platform

					__a						:  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(SCREAMING_SNAKE_CASE__    )    )

					return info



		@staticmethod
		def    __lowerCAmelCase  (					SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



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







'''simple docstring'''




lowercase     :							dict[str, float]       						=     {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_02_17_66_34e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_58_18,
}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
        A			:  int   =		(
            F'Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'
            F'Valid values are: {", ".join(snake_case__		)}'
        )
        raise ValueError(snake_case__		)
    return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




