





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_		:				int				=						{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				str				=						[
		    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RobertaPreLayerNormForCausalLM''',
		    '''RobertaPreLayerNormForMaskedLM''',
		    '''RobertaPreLayerNormForMultipleChoice''',
		    '''RobertaPreLayerNormForQuestionAnswering''',
		    '''RobertaPreLayerNormForSequenceClassification''',
		    '''RobertaPreLayerNormForTokenClassification''',
		    '''RobertaPreLayerNormModel''',
		    '''RobertaPreLayerNormPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Dict				=						[
		    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRobertaPreLayerNormForCausalLM''',
		    '''TFRobertaPreLayerNormForMaskedLM''',
		    '''TFRobertaPreLayerNormForMultipleChoice''',
		    '''TFRobertaPreLayerNormForQuestionAnswering''',
		    '''TFRobertaPreLayerNormForSequenceClassification''',
		    '''TFRobertaPreLayerNormForTokenClassification''',
		    '''TFRobertaPreLayerNormMainLayer''',
		    '''TFRobertaPreLayerNormModel''',
		    '''TFRobertaPreLayerNormPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				str				=						[
		    '''FlaxRobertaPreLayerNormForCausalLM''',
		    '''FlaxRobertaPreLayerNormForMaskedLM''',
		    '''FlaxRobertaPreLayerNormForMultipleChoice''',
		    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
		    '''FlaxRobertaPreLayerNormForSequenceClassification''',
		    '''FlaxRobertaPreLayerNormForTokenClassification''',
		    '''FlaxRobertaPreLayerNormModel''',
		    '''FlaxRobertaPreLayerNormPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    RobertaPreLayerNormConfig,
		    RobertaPreLayerNormOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaPreLayerNormForCausalLM,
				    RobertaPreLayerNormForMaskedLM,
				    RobertaPreLayerNormForMultipleChoice,
				    RobertaPreLayerNormForQuestionAnswering,
				    RobertaPreLayerNormForSequenceClassification,
				    RobertaPreLayerNormForTokenClassification,
				    RobertaPreLayerNormModel,
				    RobertaPreLayerNormPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta_prelayernorm import (
				    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaPreLayerNormForCausalLM,
				    TFRobertaPreLayerNormForMaskedLM,
				    TFRobertaPreLayerNormForMultipleChoice,
				    TFRobertaPreLayerNormForQuestionAnswering,
				    TFRobertaPreLayerNormForSequenceClassification,
				    TFRobertaPreLayerNormForTokenClassification,
				    TFRobertaPreLayerNormMainLayer,
				    TFRobertaPreLayerNormModel,
				    TFRobertaPreLayerNormPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				    FlaxRobertaPreLayerNormPreTrainedModel,
				)

else:
		import sys

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













import torch

from transformers import AutoModel


class 			lowercase				(							torch.nn.Module     ):



						"""simple docstring"""

						def __init__(    self			:		Tuple ,				lowerCamelCase_			:		Dict="sayef/fsner-bert-base-uncased"					):





										'''simple docstring'''







										super(lowerCamelCase_ ,				self					).__init__()

										_snake_case :			Optional[Any]  						=   AutoModel.from_pretrained(lowerCamelCase_ ,				return_dict=lowerCamelCase_					)
										_snake_case :			str  						=   torch.nn.CosineSimilarity(3 ,				1e-08					)
										_snake_case :			str  						=   torch.nn.Softmax(dim=1					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										return self.bert(**lowerCamelCase_					).last_hidden_state







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										return token_embeddings.sum(2 ,				keepdim=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Optional[Any]=1					):





										'''simple docstring'''







										return self.softmax(T * self.cos(lowerCamelCase_ ,				lowerCamelCase_					)					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   W_supports['sizes'].tolist()
										_snake_case :			int  						=   W_supports['start_token_id'].item()
										_snake_case :			List[str]  						=   W_supports['end_token_id'].item()

										del W_supports["sizes"]
										del W_supports["start_token_id"]
										del W_supports["end_token_id"]

										_snake_case :			Optional[int]  						=   self.BERT(**lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   self.BERT(**lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   None
										_snake_case :			Optional[int]  						=   None

										_snake_case :			List[str]  						=   W_supports['input_ids'] == start_token_id
										_snake_case :			Union[str, Any]  						=   W_supports['input_ids'] == end_token_id

										for i, size in enumerate(lowerCamelCase_					):
														if i == 0:
																		_snake_case :			str  						=   0
														else:
																		_snake_case :			Union[str, Any]  						=   support_sizes[i - 1]

														_snake_case :			Tuple  						=   S[s : s + size][start_token_masks[s : s + size]]
														_snake_case :			Optional[int]  						=   S[s : s + size][end_token_masks[s : s + size]]

														_snake_case :			Tuple  						=   torch.matmul(q[i] ,				s_start.T					).sum(1					).softmax(0					)
														_snake_case :			Union[str, Any]  						=   torch.matmul(q[i] ,				s_end.T					).sum(1					).softmax(0					)

														if p_starts is not None:
																		_snake_case :			Optional[Any]  						=   torch.vstack((p_starts, p_start)					)
																		_snake_case :			List[str]  						=   torch.vstack((p_ends, p_end)					)
														else:
																		_snake_case :			Union[str, Any]  						=   p_start
																		_snake_case :			Any  						=   p_end

										return p_starts, p_ends






