



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

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


A:   str								=		logging.get_logger(__name__)

A:   List[str]								=		{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Dict						=						'ibert'

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=30522      ,     _SCREAMING_SNAKE_CASE=768      ,     _SCREAMING_SNAKE_CASE=12      ,     _SCREAMING_SNAKE_CASE=12      ,     _SCREAMING_SNAKE_CASE=3072      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1E-12      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE="absolute"      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE="none"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      List[str]:





    '''simple docstring'''




    super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      ,     bos_token_id=_SCREAMING_SNAKE_CASE      ,     eos_token_id=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	vocab_size
    UpperCAmelCase	:					Union[str, Any]	=	hidden_size
    UpperCAmelCase	:					Optional[int]	=	num_hidden_layers
    UpperCAmelCase	:					Union[str, Any]	=	num_attention_heads
    UpperCAmelCase	:					Dict	=	hidden_act
    UpperCAmelCase	:					List[str]	=	intermediate_size
    UpperCAmelCase	:					Any	=	hidden_dropout_prob
    UpperCAmelCase	:					Dict	=	attention_probs_dropout_prob
    UpperCAmelCase	:					Union[str, Any]	=	max_position_embeddings
    UpperCAmelCase	:					Union[str, Any]	=	type_vocab_size
    UpperCAmelCase	:					List[str]	=	initializer_range
    UpperCAmelCase	:					int	=	layer_norm_eps
    UpperCAmelCase	:					Any	=	position_embedding_type
    UpperCAmelCase	:					Optional[int]	=	quant_mode
    UpperCAmelCase	:					Tuple	=	force_dequant



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




    if self.task == "multiple-choice":
     UpperCAmelCase	:					Any	=	{0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
     UpperCAmelCase	:					Optional[int]	=	{0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]						)





'''simple docstring'''






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCamelCase__:   Optional[int]								=			"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
						import torch

						print("Torch version:", torch.__version__)
						print("Cuda available:", torch.cuda.is_available())
						print("Cuda version:", torch.version.cuda)
						print("CuDNN version:", torch.backends.cudnn.version())
						print("Number of GPUs available:", torch.cuda.device_count())
						print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
						print("Torch version:", None)

try:
						import deepspeed

						print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
						print("DeepSpeed version:", None)

try:
						import tensorflow as tf

						print("TensorFlow version:", tf.__version__)
						print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
						print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
						print("TensorFlow version:", None)





