





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__							(       UpperCamelCase__:							Tuple    ):
		return EnvironmentCommand()






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):
		return EnvironmentCommand(args.accelerate_config_file    )




class   UpperCamelCase_       (     _UpperCAmelCase    ):


							@staticmethod
							def      _snake_case  (							__A    :ArgumentParser		)   ->   int:



									"""simple docstring"""



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


							def __init__(							self    :Any							,				__A    :List[Any]							,				*__A    :Any		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      accelerate_config_file


							def      _snake_case  (							self    :Union[str, Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """not installed"""
									if is_safetensors_available():
											import safetensors

											SCREAMING_SNAKE_CASE__			=      safetensors.__version__
									elif importlib.util.find_spec("""safetensors"""		) is not None:
											import safetensors

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

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

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

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

									SCREAMING_SNAKE_CASE__			=      """not installed"""
									SCREAMING_SNAKE_CASE__			=      """NA"""
									if is_torch_available():
											import torch

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

									SCREAMING_SNAKE_CASE__			=      """not installed"""
									SCREAMING_SNAKE_CASE__			=      """NA"""
									if is_tf_available():
											import tensorflow as tf

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

									SCREAMING_SNAKE_CASE__			=      """not installed"""
									SCREAMING_SNAKE_CASE__			=      """not installed"""
									SCREAMING_SNAKE_CASE__			=      """not installed"""
									SCREAMING_SNAKE_CASE__			=      """NA"""
									if is_flax_available():
											import flax
											import jax
											import jaxlib

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

									SCREAMING_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(__UpperCamelCase		)		)

									return info


							@staticmethod
							def      _snake_case  (							__A    :Union[str, Any]		)   ->   Dict:



									"""simple docstring"""



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






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__    )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					field(default="image-classification"				,  metadata={"include_in_asdict_even_if_is_default": True}    )
							lowerCamelCase_           =					Features({"image": Image()}    )
							lowerCamelCase_           =					Features({"labels": ClassLabel}    )
							lowerCamelCase_           =					"image"
							lowerCamelCase_           =					"labels"


							def      _snake_case  (							self    :List[str]							,				__A    :Tuple		)   ->   Tuple:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]							,				__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__			=      self.label_schema.copy()
									SCREAMING_SNAKE_CASE__			=      features[self.label_column]
									SCREAMING_SNAKE_CASE__			=      label_schema
									return task_template


							@property
							def      _snake_case  (							self    :Dict		)   ->   Dict[str, str]:



									"""simple docstring"""



									return {
									    self.image_column: "image",
									    self.label_column: "labels",
									}