
from collections import OrderedDict
from typing import Mapping

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


lowercase        =  logging.get_logger(__name__)

lowercase        =  {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class       __A(				_lowerCamelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''big_bird'''

					def __init__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]=5_0_3_5_8   ,					__UpperCamelCase					:     Tuple=7_6_8   ,					__UpperCamelCase					:     Any=1_2   ,					__UpperCamelCase					:     Dict=1_2   ,					__UpperCamelCase					:     Optional[int]=3_0_7_2   ,					__UpperCamelCase					:     Optional[int]="gelu_new"   ,					__UpperCamelCase					:     Union[str, Any]=0.1   ,					__UpperCamelCase					:     Dict=0.1   ,					__UpperCamelCase					:     Optional[int]=4_0_9_6   ,					__UpperCamelCase					:     str=2   ,					__UpperCamelCase					:     Tuple=0.02   ,					__UpperCamelCase					:     Optional[Any]=1E-12   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     int=0   ,					__UpperCamelCase					:     str=1   ,					__UpperCamelCase					:     Dict=2   ,					__UpperCamelCase					:     Union[str, Any]=6_6   ,					__UpperCamelCase					:     Tuple="block_sparse"   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     int=6_4   ,					__UpperCamelCase					:     Optional[int]=3   ,					__UpperCamelCase					:     List[str]=None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(
												    pad_token_id=A__   ,					bos_token_id=A__   ,					eos_token_id=A__   ,					sep_token_id=A__   ,					**A__   ,					)

												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					type_vocab_size
												lowerCamelCase_							  =					layer_norm_eps
												lowerCamelCase_							  =					use_cache

												lowerCamelCase_							  =					rescale_embeddings
												lowerCamelCase_							  =					attention_type
												lowerCamelCase_							  =					use_bias
												lowerCamelCase_							  =					block_size
												lowerCamelCase_							  =					num_random_blocks
												lowerCamelCase_							  =					classifier_dropout





class       __A(				_lowerCamelCase       ):

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







import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
		from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    List[str]:
							if is_torch_version("""<"""			,				"""2.0.0"""       ) or not hasattr(UpperCAmelCase__			,				"""_dynamo"""       ):
														return False
							return isinstance(UpperCAmelCase__			,				torch._dynamo.eval_frame.OptimizedModule       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      bool = True       )  ->    str:
							lowerCamelCase_							  =					(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

							lowerCamelCase_							  =					is_compiled_module(UpperCAmelCase__       )
							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					model._orig_mod

							if is_deepspeed_available():
														options += (DeepSpeedEngine,)

							while isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					model.module

							if not keep_fpaa_wrapper:
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""forward"""       )
														lowerCamelCase_							  =					model.__dict__.pop("""_original_forward"""			,				UpperCAmelCase__       )
														if original_forward is not None:
																					while hasattr(UpperCAmelCase__			,				"""__wrapped__"""       ):
																												lowerCamelCase_							  =					forward.__wrapped__
																												if forward == original_forward:
																																			break
																					lowerCamelCase_							  =					forward
														if getattr(UpperCAmelCase__			,				"""_converted_to_transformer_engine"""			,				UpperCAmelCase__       ):
																					convert_model(UpperCAmelCase__			,				to_transformer_engine=UpperCAmelCase__       )

							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					compiled_model

							return model



def      __lowerCAmelCase						(							)  ->    Any:
							PartialState().wait_for_everyone()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Tuple       )  ->    Dict:
							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(UpperCAmelCase__			,				UpperCAmelCase__       )
							elif PartialState().local_process_index == 0:
														torch.save(UpperCAmelCase__			,				UpperCAmelCase__       )



@contextmanager
def      __lowerCAmelCase						(							**UpperCAmelCase__				:      Any       )  ->    Dict:
							for key, value in kwargs.items():
														lowerCamelCase_							  =					str(UpperCAmelCase__       )

							yield

							for key in kwargs:
														if key.upper() in os.environ:
																					del os.environ[key.upper()]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    str:
							if not hasattr(UpperCAmelCase__			,				"""__qualname__"""       ) and not hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""__class__"""			,				UpperCAmelCase__       )
							if hasattr(UpperCAmelCase__			,				"""__qualname__"""       ):
														return obj.__qualname__
							if hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														return obj.__name__
							return str(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Dict:
							for key, value in source.items():
														if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
																					lowerCamelCase_							  =					destination.setdefault(UpperCAmelCase__			,				{}       )
																					merge_dicts(UpperCAmelCase__			,				UpperCAmelCase__       )
														else:
																					lowerCamelCase_							  =					value

							return destination



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = None       )  ->    bool:
							if port is None:
														lowerCamelCase_							  =					2_9_5_0_0
							with socket.socket(socket.AF_INET			,				socket.SOCK_STREAM       ) as s:
														return s.connect_ex(("""localhost""", port)       ) == 0





