


from collections import OrderedDict
from typing import Mapping

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


a_ :Dict  							=			logging.get_logger(__name__)

a_ :List[str]  							=			{
    '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 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			Optional[int]      = '''big_bird'''







     def __init__(						self :						Any   ,   _lowercase :						str=5_03_58   ,   _lowercase :						Optional[int]=7_68   ,   _lowercase :						List[Any]=12   ,   _lowercase :						Any=12   ,   _lowercase :						Tuple=30_72   ,   _lowercase :						int="gelu_new"   ,   _lowercase :						Any=0.1   ,   _lowercase :						Optional[int]=0.1   ,   _lowercase :						str=40_96   ,   _lowercase :						Dict=2   ,   _lowercase :						Union[str, Any]=0.02   ,   _lowercase :						Tuple=1E-12   ,   _lowercase :						Any=True   ,   _lowercase :						List[Any]=0   ,   _lowercase :						Optional[int]=1   ,   _lowercase :						List[Any]=2   ,   _lowercase :						Union[str, Any]=66   ,   _lowercase :						int="block_sparse"   ,   _lowercase :						str=True   ,   _lowercase :						Any=False   ,   _lowercase :						Tuple=64   ,   _lowercase :						List[str]=3   ,   _lowercase :						int=None   ,   **_lowercase :						List[str]   ,   ):
           super().__init__(
               pad_token_id=_lowercase   ,   bos_token_id=_lowercase   ,   eos_token_id=_lowercase   ,   sep_token_id=_lowercase   ,   **_lowercase   ,   )

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

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		rescale_embeddings
           SCREAMING_SNAKE_CASE__       :  str						     =		attention_type
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		use_bias
           SCREAMING_SNAKE_CASE__       :  Dict						     =		block_size
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		num_random_blocks
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		classifier_dropout




class 							lowercase  (						_UpperCAmelCase			):







     @property
     def 							lowercase__       (						self :						Tuple      ):
           if self.task == "multiple-choice":
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                 SCREAMING_SNAKE_CASE__       :  str						     =		{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,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_							:	Any	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






