

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_      =     {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['XLNetTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['XLNetTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'XLNetForMultipleChoice',
			    'XLNetForQuestionAnswering',
			    'XLNetForQuestionAnsweringSimple',
			    'XLNetForSequenceClassification',
			    'XLNetForTokenClassification',
			    'XLNetLMHeadModel',
			    'XLNetModel',
			    'XLNetPreTrainedModel',
			    'load_tf_weights_in_xlnet',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFXLNetForMultipleChoice',
			    'TFXLNetForQuestionAnsweringSimple',
			    'TFXLNetForSequenceClassification',
			    'TFXLNetForTokenClassification',
			    'TFXLNetLMHeadModel',
			    'TFXLNetMainLayer',
			    'TFXLNetModel',
			    'TFXLNetPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


from collections import OrderedDict
from typing import Mapping

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







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

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)