






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					str 					=				'distilbert'
						SCREAMING_SNAKE_CASE_:					int 					=				{
						    'hidden_size': 'dim',
						    'num_attention_heads': 'n_heads',
						    'num_hidden_layers': 'n_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_=3_0_5_2_2   ,				lowerCAmelCase_=5_1_2   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=6   ,				lowerCAmelCase_=1_2   ,				lowerCAmelCase_=7_6_8   ,				lowerCAmelCase_=4 * 7_6_8   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_="gelu"   ,				lowerCAmelCase_=0.02   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.2   ,				lowerCAmelCase_=0   ,				**lowerCAmelCase_   ,				)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    vocab_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		Any       		=    sinusoidal_pos_embds
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    n_layers
										_SCREAMING_SNAKE_CASE   :		str       		=    n_heads
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    dim
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_dim
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    dropout
										_SCREAMING_SNAKE_CASE   :		Dict       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    activation
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    qa_dropout
										_SCREAMING_SNAKE_CASE   :		str       		=    seq_classif_dropout
										super().__init__(**lowerCAmelCase_   ,				pad_token_id=lowerCAmelCase_							)







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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









"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def 				lowercase__		(					lowerCamelCase       ):
				return EnvironmentCommand()






class _lowerCAmelCase						(    __UpperCAmelCase       ):







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    parser.add_parser('env'							)
										download_parser.set_defaults(func=lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		int       		=    huggingface_hub.__version__

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

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

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'not installed'
										if is_transformers_available():
														import transformers

														_SCREAMING_SNAKE_CASE   :		Tuple       		=    transformers.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'not installed'
										if is_accelerate_available():
														import accelerate

														_SCREAMING_SNAKE_CASE   :		str       		=    accelerate.__version__

										_SCREAMING_SNAKE_CASE   :		str       		=    'not installed'
										if is_xformers_available():
														import xformers

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xformers.__version__

										_SCREAMING_SNAKE_CASE   :		Any       		=    {
										    '`diffusers` version': version,
										    'Platform': platform.platform(),
										    'Python version': platform.python_version(),
										    'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
										    'Huggingface_hub version': hub_version,
										    'Transformers version': transformers_version,
										    'Accelerate version': accelerate_version,
										    'xFormers version': xformers_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(lowerCAmelCase_							)							)

										return info







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"

