






"""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       =				{
    "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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					str 			=					"""big_bird"""





	def __init__(self  ,  lowerCAmelCase_=50358  ,  lowerCAmelCase_=768  ,  lowerCAmelCase_=12  ,  lowerCAmelCase_=12  ,  lowerCAmelCase_=3072  ,  lowerCAmelCase_="gelu_new"  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=4096  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=1e-1_2  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=1  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=66  ,  lowerCAmelCase_="block_sparse"  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=64  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_  ,  ):
		super().__init__(
		    pad_token_id=lowerCAmelCase_  ,  bos_token_id=lowerCAmelCase_  ,  eos_token_id=lowerCAmelCase_  ,  sep_token_id=lowerCAmelCase_  ,  **lowerCAmelCase_  ,  )

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

		A_		:    Union[str, Any]						     = rescale_embeddings
		A_		:    Tuple						     = attention_type
		A_		:    str						     = use_bias
		A_		:    int						     = block_size
		A_		:    Tuple						     = num_random_blocks
		A_		:    Union[str, Any]						     = classifier_dropout






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




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









"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

