






import torch

from transformers import AutoModel



class 				_A			(     torch.nn.Module				):
    """simple docstring"""

    def __init__(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     str="sayef/fsner-bert-base-uncased"      )      ->							Optional[int]:
           super(__SCREAMING_SNAKE_CASE ,     self      ).__init__()

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



    def _a							(				self      :     str ,     **__SCREAMING_SNAKE_CASE      :     Tuple      )      ->							List[Any]:
           return self.bert(**__SCREAMING_SNAKE_CASE      ).last_hidden_state



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							str:
           return token_embeddings.sum(2 ,     keepdim=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     str=1      )      ->							Optional[int]:
           return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )      )



    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     str      )      ->							Any:
           __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(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=self.BERT(**__SCREAMING_SNAKE_CASE      )

           __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(__SCREAMING_SNAKE_CASE      ):
                  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







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



