



from collections import OrderedDict
from typing import Mapping

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """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__     (				_lowercase	):
	__magic_name__		:    Optional[int]	    =     "big_bird"


	def __init__(self : Optional[Any],					__UpperCAmelCase : Optional[int]=50358,					__UpperCAmelCase : Optional[int]=768,					__UpperCAmelCase : Dict=12,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : List[Any]=3072,					__UpperCAmelCase : Union[str, Any]="gelu_new",					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Union[str, Any]=4096,					__UpperCAmelCase : Optional[Any]=2,					__UpperCAmelCase : Dict=0.02,					__UpperCAmelCase : str=1e-12,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : List[str]=0,					__UpperCAmelCase : List[str]=1,					__UpperCAmelCase : str=2,					__UpperCAmelCase : Any=66,					__UpperCAmelCase : Optional[int]="block_sparse",					__UpperCAmelCase : str=True,					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : Optional[Any]=64,					__UpperCAmelCase : Tuple=3,					__UpperCAmelCase : Optional[Any]=None,					**__UpperCAmelCase : str,					)     ->			List[Any]:

					"""simple docstring"""

					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					sep_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)

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

					SCREAMING_SNAKE_CASE  :						Dict      					= rescale_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= attention_type
					SCREAMING_SNAKE_CASE  :						int      					= use_bias
					SCREAMING_SNAKE_CASE  :						List[Any]      					= block_size
					SCREAMING_SNAKE_CASE  :						str      					= num_random_blocks
					SCREAMING_SNAKE_CASE  :						List[Any]      					= classifier_dropout



class 	a__     (				_lowercase	):


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

					"""simple docstring"""

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	snake_case_			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




