"""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   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      Tuple:

	'''simple docstring'''





	return EnvironmentCommand()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]  )							->      Optional[int]:

	'''simple docstring'''





	return EnvironmentCommand(args.accelerate_config_file  )

class 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Union[str, Any]:
					__snake_case      :							Any						=			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    :				str     ,  _A    :				Optional[int]     ,  *_A    :				Union[str, Any])   ->     None:
					__snake_case      :							Union[str, Any]						=			accelerate_config_file



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					__snake_case      :							int						=			'not installed'
					if is_safetensors_available():
						import safetensors

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

						__snake_case      :							List[str]						=			f"{safetensors.__version__} but is ignored because of PyTorch version too old."

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

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

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

					__snake_case      :							Optional[Any]						=			'not installed'
					__snake_case      :							Optional[int]						=			'NA'
					if is_torch_available():
						import torch

						__snake_case      :							Any						=			torch.__version__
						__snake_case      :							Optional[int]						=			torch.cuda.is_available()

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

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

					__snake_case      :							Optional[int]						=			'not installed'
					__snake_case      :							Any						=			'not installed'
					__snake_case      :							Optional[int]						=			'not installed'
					__snake_case      :							Any						=			'NA'
					if is_flax_available():
						import flax
						import jax
						import jaxlib

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

					__snake_case      :							int						=			{
					    '`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  _lowercase					(_A    :				List[Any])   ->     Optional[int]:
					return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a					:					Tuple=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		"""albert"""



				def __init__(self    :				Union[str, Any]     ,  _A    :				int=3_00_00     ,  _A    :				Any=1_28     ,  _A    :				Tuple=40_96     ,  _A    :				int=12     ,  _A    :				Tuple=1     ,  _A    :				int=64     ,  _A    :				Optional[Any]=1_63_84     ,  _A    :				Optional[Any]=1     ,  _A    :				List[str]="gelu_new"     ,  _A    :				Any=0     ,  _A    :				Optional[Any]=0     ,  _A    :				List[Any]=5_12     ,  _A    :				List[Any]=2     ,  _A    :				Dict=0.02     ,  _A    :				Union[str, Any]=1E-12     ,  _A    :				Tuple=0.1     ,  _A    :				int="absolute"     ,  _A    :				List[Any]=0     ,  _A    :				str=2     ,  _A    :				int=3     ,  **_A    :				Tuple     ,  )   ->     Union[str, Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							str						=			vocab_size
					__snake_case      :							List[Any]						=			embedding_size
					__snake_case      :							Tuple						=			hidden_size
					__snake_case      :							Optional[Any]						=			num_hidden_layers
					__snake_case      :							List[str]						=			num_hidden_groups
					__snake_case      :							Dict						=			num_attention_heads
					__snake_case      :							Any						=			inner_group_num
					__snake_case      :							Union[str, Any]						=			hidden_act
					__snake_case      :							List[Any]						=			intermediate_size
					__snake_case      :							List[str]						=			hidden_dropout_prob
					__snake_case      :							int						=			attention_probs_dropout_prob
					__snake_case      :							Any						=			max_position_embeddings
					__snake_case      :							Optional[int]						=			type_vocab_size
					__snake_case      :							Optional[Any]						=			initializer_range
					__snake_case      :							List[Any]						=			layer_norm_eps
					__snake_case      :							Optional[Any]						=			classifier_dropout_prob
					__snake_case      :							List[str]						=			position_embedding_type







class 		UpperCamelCase      (   lowercase						):



				@property
				def  _lowercase					(self    :				Tuple)   ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case      :							int						=			{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case      :							Optional[Any]						=			{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					        ('token_type_ids', dynamic_axis),
					    ])





