





"""simple docstring"""



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


a_			=      logging.get_logger(__name__)

a_			=      {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""fnet"""

 def __init__(   self ,						__lowerCamelCase=3_2000 ,						__lowerCamelCase=768 ,						__lowerCamelCase=12 ,						__lowerCamelCase=3072 ,						__lowerCamelCase="gelu_new" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=512 ,						__lowerCamelCase=4 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-1_2 ,						__lowerCamelCase=False ,						__lowerCamelCase=512 ,						__lowerCamelCase=3 ,						__lowerCamelCase=1 ,						__lowerCamelCase=2 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase ,						bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase ,						**__lowerCamelCase  )

     __A   :						List[str]									=					vocab_size
     __A   :						Optional[Any]									=					max_position_embeddings
     __A   :						List[str]									=					hidden_size
     __A   :						Optional[int]									=					num_hidden_layers
     __A   :						Optional[Any]									=					intermediate_size
     __A   :						Optional[Any]									=					hidden_act
     __A   :						Tuple									=					hidden_dropout_prob
     __A   :						Optional[int]									=					initializer_range
     __A   :						Dict									=					type_vocab_size
     __A   :						Dict									=					layer_norm_eps
     __A   :						str									=					use_tpu_fourier_optimizations
     __A   :						Tuple									=					tpu_short_seq_length











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_			=      {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""BloomTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """BloomForCausalLM""",
           """BloomModel""",
           """BloomPreTrainedModel""",
           """BloomForSequenceClassification""",
           """BloomForTokenClassification""",
           """BloomForQuestionAnswering""",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

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




