
'''simple docstring'''





from typing import TYPE_CHECKING

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


A							     = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["DebertaV2TokenizerFast"]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = [
         "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDebertaV2ForMaskedLM",
         "TFDebertaV2ForQuestionAnswering",
         "TFDebertaV2ForSequenceClassification",
         "TFDebertaV2ForTokenClassification",
         "TFDebertaV2Model",
         "TFDebertaV2PreTrainedModel",
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = [
         "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DebertaV2ForMaskedLM",
         "DebertaV2ForMultipleChoice",
         "DebertaV2ForQuestionAnswering",
         "DebertaV2ForSequenceClassification",
         "DebertaV2ForTokenClassification",
         "DebertaV2Model",
         "DebertaV2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_deberta_va import (
         DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DebertaVaConfig,
         DebertaVaOnnxConfig,
     )
     from .tokenization_deberta_va import DebertaVaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_deberta_va import (
              TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
              TFDebertaVaModel,
              TFDebertaVaPreTrainedModel,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deberta_va import (
              DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              DebertaVaForMaskedLM,
              DebertaVaForMultipleChoice,
              DebertaVaForQuestionAnswering,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaModel,
              DebertaVaPreTrainedModel,
          )

else:
     import sys

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


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "nystromformer"







       def __init__(					self,				lowerCAmelCase__=3_0000,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=510,				lowerCAmelCase__=2,				lowerCAmelCase__=64,				lowerCAmelCase__=64,				lowerCAmelCase__=65,				lowerCAmelCase__=False,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-5,				lowerCAmelCase__=1,				lowerCAmelCase__=0,				lowerCAmelCase__=2,				**lowerCAmelCase__,				) -> Optional[Any]:
            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   segment_means_seq_len
            snake_case_   			=   num_landmarks
            snake_case_   			=   conv_kernel_size
            snake_case_   			=   inv_coeff_init_option
            snake_case_   			=   layer_norm_eps
            super().__init__(pad_token_id=lowerCAmelCase__,				bos_token_id=lowerCAmelCase__,				eos_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

