






# 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   :     Dict 		= logging.get_logger(__name__)

__lowerCamelCase   :     Dict[Optional[str], Type[Formatter]] 		= {}
__lowerCamelCase   :     Dict[Optional[str], str] 		= {}
__lowerCamelCase   :     Dict[Optional[str], Exception] 		= {}







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				type	,    snake_case_   :				Optional[str]	,    snake_case_   :				Optional[List[str]] = None	,    ):
	snake_case__					: Tuple  =    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__})'''  )
	snake_case__					: 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})'''  )
		snake_case__					: Union[str, Any]  =    format_type







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Exception	,    snake_case_   :				Optional[str]	,    snake_case_   :				Optional[List[str]] = None  ):
	snake_case__					: Optional[Any]  =    aliases if aliases is not None else []
	for alias in set(aliases + [format_type]  ):
		snake_case__					: Optional[int]  =    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   :     Optional[Any] 		= 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   :     Optional[Any] 		= 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   :     Optional[int] 		= ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
	_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[str]  ):





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







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[str]	,    **snake_case_   :				Optional[Any]  ):
	snake_case__					: List[Any]  =    get_format_type_from_alias(a__  )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**a__  )
	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


lowerCAmelCase__    :Any       =				logging.get_logger(__name__)

lowerCAmelCase__    :Union[str, Any]       =				{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      'xmod'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=30522     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=3072     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE="absolute"     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=("en_XX",)     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





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

									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     type_vocab_size
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     position_embedding_type
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     classifier_dropout
									_UpperCAmelCase											=     pre_norm
									_UpperCAmelCase											=     adapter_reduction_factor
									_UpperCAmelCase											=     adapter_layer_norm
									_UpperCAmelCase											=     adapter_reuse_layer_norm
									_UpperCAmelCase											=     ln_before_adapter
									_UpperCAmelCase											=     list(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     default_language



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





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