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


__lowerCAmelCase    :	List[Any]						=						logging.get_logger(__name__)

__lowerCAmelCase    :	Dict						=						{
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}


class   __lowerCAmelCase		(		lowerCAmelCase_					):



      """simple docstring"""







      A__		:	str         =			'''funnel'''
      A__		:	int         =			{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''n_head''',
      }




      def __init__(					self						:				str ,				_snake_case						:				Union[str, Any]=3_0522 ,				_snake_case						:				List[str]=[4, 4, 4] ,				_snake_case						:				int=None ,				_snake_case						:				int=2 ,				_snake_case						:				Tuple=768 ,				_snake_case						:				Any=12 ,				_snake_case						:				Tuple=64 ,				_snake_case						:				Union[str, Any]=3072 ,				_snake_case						:				Any="gelu_new" ,				_snake_case						:				Optional[Any]=0.1 ,				_snake_case						:				Any=0.1 ,				_snake_case						:				Optional[Any]=0.0 ,				_snake_case						:				Tuple=0.1 ,				_snake_case						:				Optional[Any]=None ,				_snake_case						:				Any=1E-9 ,				_snake_case						:				List[Any]="mean" ,				_snake_case						:				Optional[int]="relative_shift" ,				_snake_case						:				List[Any]=True ,				_snake_case						:				List[str]=True ,				_snake_case						:				Any=True ,				**_snake_case						:				Optional[int] ,				):
          __lowercase						:	int					=				vocab_size
          __lowercase						:	Optional[Any]					=				block_sizes
          __lowercase						:	Tuple					=				[1] * len(__lowerCamelCase ) if block_repeats is None else block_repeats
          assert len(__lowerCamelCase ) == len(
              self.block_repeats ), "`block_sizes` and `block_repeats` should have the same length."
          __lowercase						:	Optional[Any]					=				num_decoder_layers
          __lowercase						:	List[str]					=				d_model
          __lowercase						:	Dict					=				n_head
          __lowercase						:	Optional[int]					=				d_head
          __lowercase						:	List[Any]					=				d_inner
          __lowercase						:	Any					=				hidden_act
          __lowercase						:	List[Any]					=				hidden_dropout
          __lowercase						:	Dict					=				attention_dropout
          __lowercase						:	List[Any]					=				activation_dropout
          __lowercase						:	List[Any]					=				initializer_range
          __lowercase						:	str					=				initializer_std
          __lowercase						:	Any					=				layer_norm_eps
          assert pooling_type in [
              "mean",
              "max",
          ], F'Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'
          __lowercase						:	Union[str, Any]					=				pooling_type
          assert attention_type in [
              "relative_shift",
              "factorized",
          ], F'Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'
          __lowercase						:	List[Any]					=				attention_type
          __lowercase						:	Tuple					=				separate_cls
          __lowercase						:	Tuple					=				truncate_seq
          __lowercase						:	int					=				pool_q_only

          super().__init__(**__lowerCamelCase )




      @property
      def 						snake_case_	(					self						:				List[Any] ):
          return sum(self.block_sizes )




      @num_hidden_layers.setter
      def 						snake_case_	(					self						:				Optional[int] ,				_snake_case						:				Union[str, Any] ):
          raise NotImplementedError(
              '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.''' )




      @property
      def 						snake_case_	(					self						:				str ):
          return len(self.block_sizes )




      @num_blocks.setter
      def 						snake_case_	(					self						:				Optional[Any] ,				_snake_case						:				Tuple ):
          raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.''' )






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"						)
       _A      :       Dict				    = formatter_cls
       for alias in set(aliases + [format_type]						):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
    from .torch_formatter import TorchFormatter

    _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
    lowerCAmelCase__      		= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
    _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
    from .tf_formatter import TFFormatter

    _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
    lowerCAmelCase__      		= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
    _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
    from .jax_formatter import JaxFormatter

    _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = get_format_type_from_alias(UpperCamelCase__						)
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**UpperCamelCase__						)
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"						)
