
'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__ :						List[str]    							=					{
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						Union[str, Any]    							=					["BigBirdTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[Any]    							=					["BigBirdTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BigBirdForCausalLM",
         "BigBirdForMaskedLM",
         "BigBirdForMultipleChoice",
         "BigBirdForPreTraining",
         "BigBirdForQuestionAnswering",
         "BigBirdForSequenceClassification",
         "BigBirdForTokenClassification",
         "BigBirdLayer",
         "BigBirdModel",
         "BigBirdPreTrainedModel",
         "load_tf_weights_in_big_bird",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "FlaxBigBirdForCausalLM",
         "FlaxBigBirdForMaskedLM",
         "FlaxBigBirdForMultipleChoice",
         "FlaxBigBirdForPreTraining",
         "FlaxBigBirdForQuestionAnswering",
         "FlaxBigBirdForSequenceClassification",
         "FlaxBigBirdForTokenClassification",
         "FlaxBigBirdModel",
         "FlaxBigBirdPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird import BigBirdTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird_fast import BigBirdTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_big_bird import (
              BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
              BigBirdForCausalLM,
              BigBirdForMaskedLM,
              BigBirdForMultipleChoice,
              BigBirdForPreTraining,
              BigBirdForQuestionAnswering,
              BigBirdForSequenceClassification,
              BigBirdForTokenClassification,
              BigBirdLayer,
              BigBirdModel,
              BigBirdPreTrainedModel,
              load_tf_weights_in_big_bird,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_big_bird import (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
              FlaxBigBirdModel,
              FlaxBigBirdPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase__ :						List[str]    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ :						int    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Any    							=					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'cpmant'






       def __init__(							self    ,							A__ = 3_07_20    ,							A__ = 40_96    ,							A__ = 32    ,							A__ = 1_28    ,							A__ = 1_02_40    ,							A__ = 48    ,							A__ = 0.0    ,							A__ = 5_12    ,							A__ = 20_48    ,							A__ = 1E-6    ,							A__ = 1.0    ,							A__ = 32    ,							A__ = 32    ,							A__ = 32    ,							A__ = True    ,							**A__    ,							)      ->     List[Any]:
          super().__init__(**A__ )
          _SCREAMING_SNAKE_CASE       	=			prompt_types
          _SCREAMING_SNAKE_CASE       	=			prompt_length
          _SCREAMING_SNAKE_CASE       	=			segment_types
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			dim_head
          _SCREAMING_SNAKE_CASE       	=			dim_ff
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			position_bias_num_buckets
          _SCREAMING_SNAKE_CASE       	=			position_bias_max_distance
          _SCREAMING_SNAKE_CASE       	=			dropout_p
          _SCREAMING_SNAKE_CASE       	=			eps
          _SCREAMING_SNAKE_CASE       	=			use_cache
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			init_std






