




'''simple docstring'''


# 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


UpperCamelCase       			= logging.get_logger(__name__)

UpperCamelCase       			= {}
UpperCamelCase       			= {}
UpperCamelCase       			= {}
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase = None						,							)       ->      Dict:
     A:						Optional[int]						=						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:						Union[str, Any]						=						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:						str						=						format_type
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase = None     )       ->      Union[str, Any]:
     A:						Any						=						aliases if aliases is not None else []
     for alias in set(aliases + [format_type]     ):
          A:						Tuple						=						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:
     UpperCamelCase       			= 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:
     UpperCamelCase       			= 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:
     UpperCamelCase       			= ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
     _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])
def  SCREAMING_SNAKE_CASE(					__lowercase     )       ->      List[Any]:



     if format_type in _FORMAT_TYPES_ALIASES:
          return _FORMAT_TYPES_ALIASES[format_type]
     else:
          return format_type


def  SCREAMING_SNAKE_CASE(					__lowercase						,							**__lowercase     )       ->      List[Any]:
     A:						Optional[int]						=						get_format_type_from_alias(__lowercase     )
     if format_type in _FORMAT_TYPES:
          return _FORMAT_TYPES[format_type](**__lowercase     )
     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}'"""     )







from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


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

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase 			=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase 			=							{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]						)