


"""simple docstring"""




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        lowercase     (SCREAMING_SNAKE_CASE_						:							type					,     SCREAMING_SNAKE_CASE_						:							Optional[str]					,     SCREAMING_SNAKE_CASE_						:							Optional[List[str]] = None					,     )  ->		Any:
						SCREAMING_SNAKE_CASE						=   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__})'    )
						SCREAMING_SNAKE_CASE						=   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})'    )
												SCREAMING_SNAKE_CASE						=   format_type


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Exception					,     SCREAMING_SNAKE_CASE_						:							Optional[str]					,     SCREAMING_SNAKE_CASE_						:							Optional[List[str]] = None    )  ->		Any:
						SCREAMING_SNAKE_CASE						=   aliases if aliases is not None else []
						for alias in set(aliases + [format_type]    ):
												SCREAMING_SNAKE_CASE						=   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        lowercase     (SCREAMING_SNAKE_CASE_						:							Optional[str]    )  ->		Optional[str]:
						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Optional[str]					,     **SCREAMING_SNAKE_CASE_						:							List[Any]    )  ->		Formatter:
						SCREAMING_SNAKE_CASE						=   get_format_type_from_alias(lowerCAmelCase__    )
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**lowerCAmelCase__    )
						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}\''    )





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


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

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

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






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





