


from typing import TYPE_CHECKING

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


__snake_case            =   {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case            =   ["MvpTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case            =   [
							    "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MvpForCausalLM",
							    "MvpForConditionalGeneration",
							    "MvpForQuestionAnswering",
							    "MvpForSequenceClassification",
							    "MvpModel",
							    "MvpPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
							from .tokenization_mvp import MvpTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mvp_fast import MvpTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mvp import (
														    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MvpForCausalLM,
														    MvpForConditionalGeneration,
														    MvpForQuestionAnswering,
														    MvpForSequenceClassification,
														    MvpModel,
														    MvpPreTrainedModel,
														)

else:
							import sys

							__snake_case            =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







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





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







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

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

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




