

from collections import OrderedDict
from typing import Mapping

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


A_	:			Any  	=      logging.get_logger(__name__)

A_	:			int  	=      {
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	'''xlm-roberta'''






					def __init__(			self   ,	A__=3_0522   ,	A__=768   ,	A__=12   ,	A__=12   ,	A__=3072   ,	A__="gelu"   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=2   ,	A__=0.0_2   ,	A__=1e-12   ,	A__=1   ,	A__=0   ,	A__=2   ,	A__="absolute"   ,	A__=True   ,	A__=None   ,	**A__   ,	):
						super().__init__(pad_token_id=A__   ,	bos_token_id=A__   ,	eos_token_id=A__   ,	**A__      )

						A__							:   Optional[Any]					       =		vocab_size
						A__							:   Optional[int]					       =		hidden_size
						A__							:   str					       =		num_hidden_layers
						A__							:   Any					       =		num_attention_heads
						A__							:   Union[str, Any]					       =		hidden_act
						A__							:   Any					       =		intermediate_size
						A__							:   List[str]					       =		hidden_dropout_prob
						A__							:   List[str]					       =		attention_probs_dropout_prob
						A__							:   int					       =		max_position_embeddings
						A__							:   Any					       =		type_vocab_size
						A__							:   Dict					       =		initializer_range
						A__							:   Dict					       =		layer_norm_eps
						A__							:   Tuple					       =		position_embedding_type
						A__							:   List[str]					       =		use_cache
						A__							:   Optional[int]					       =		classifier_dropout



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@property
					def   __A      (			self      ):
						if self.task == "multiple-choice":
							A__							:   str					       =		{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
							A__							:   Dict					       =		{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ]      )






from typing import TYPE_CHECKING

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


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



