'''simple docstring'''

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
_A			:      Tuple					      =				logging.getLogger(__name__)







def      UpperCamelCase_						(					snake_case_       :       str      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						git.Repo(search_parent_directories=snake_case_      )
 __lowerCAmelCase											=						{
     'repo_id': str(snake_case_      ),
     'repo_sha': str(repo.head.object.hexsha      ),
     'repo_branch': str(repo.active_branch      ),
 }

 with open(os.path.join(snake_case_						,		"""git_log.json"""      )						,		"""w"""      ) as f:
  json.dump(snake_case_						,		snake_case_						,		indent=4      )







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Tuple:






 '''simple docstring'''

 if params.n_gpu <= 0:
  __lowerCAmelCase											=						0
  __lowerCAmelCase											=						-1
  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						False
  return

 assert torch.cuda.is_available()

 logger.info("""Initializing GPUs"""      )
 if params.n_gpu > 1:
  assert params.local_rank != -1

  __lowerCAmelCase											=						int(os.environ["""WORLD_SIZE"""]      )
  __lowerCAmelCase											=						int(os.environ["""N_GPU_NODE"""]      )
  __lowerCAmelCase											=						int(os.environ["""RANK"""]      )

  # number of nodes / node ID
  __lowerCAmelCase											=						params.world_size // params.n_gpu_per_node
  __lowerCAmelCase											=						params.global_rank // params.n_gpu_per_node
  __lowerCAmelCase											=						True

  assert params.n_nodes == int(os.environ["""N_NODES"""]      )
  assert params.node_id == int(os.environ["""NODE_RANK"""]      )

 # local job (single GPU)
 else:
  assert params.local_rank == -1

  __lowerCAmelCase											=						1
  __lowerCAmelCase											=						0
  __lowerCAmelCase											=						0
  __lowerCAmelCase											=						0
  __lowerCAmelCase											=						1
  __lowerCAmelCase											=						1
  __lowerCAmelCase											=						False

 # sanity checks
 assert params.n_nodes >= 1
 assert 0 <= params.node_id < params.n_nodes
 assert 0 <= params.local_rank <= params.global_rank < params.world_size
 assert params.world_size == params.n_nodes * params.n_gpu_per_node

 # define whether this is the master process / if we are in multi-node distributed mode
 __lowerCAmelCase											=						params.node_id == 0 and params.local_rank == 0
 __lowerCAmelCase											=						params.n_nodes > 1

 # summary
 __lowerCAmelCase											=						f"""--- Global rank: {params.global_rank} - """
 logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes      )
 logger.info(PREFIX + """Node ID        : %i""" % params.node_id      )
 logger.info(PREFIX + """Local rank     : %i""" % params.local_rank      )
 logger.info(PREFIX + """World size     : %i""" % params.world_size      )
 logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node      )
 logger.info(PREFIX + """Master         : %s""" % str(params.is_master      )      )
 logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node      )      )
 logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu      )      )
 logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()      )

 # set GPU device
 torch.cuda.set_device(params.local_rank      )

 # initialize multi-GPU
 if params.multi_gpu:
  logger.info("""Initializing PyTorch distributed"""      )
  torch.distributed.init_process_group(
      init_method="""env://"""						,		backend="""nccl"""						,		)







def      UpperCamelCase_						(					snake_case_       :       List[Any]      )					->     Tuple:






 '''simple docstring'''

 np.random.seed(args.seed      )
 torch.manual_seed(args.seed      )
 if args.n_gpu > 0:
  torch.cuda.manual_seed_all(args.seed      )











from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


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






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






