

import torch

from transformers import AutoModel







class 	lowercase    (				torch.nn.Module							):


  """simple docstring"""




  def __init__(				self	,       __snake_case="sayef/fsner-bert-base-uncased"):
   super(__snake_case	,       self).__init__()

   _UpperCamelCase   :		Any			=						AutoModel.from_pretrained(__snake_case	,       return_dict=__snake_case)
   _UpperCamelCase   :		List[Any]			=						torch.nn.CosineSimilarity(3	,       1e-08)
   _UpperCamelCase   :		Tuple			=						torch.nn.Softmax(dim=1)


  def 		A__					(				self	,       **__snake_case):
   return self.bert(**__snake_case).last_hidden_state


  def 		A__					(				self	,       __snake_case):
   return token_embeddings.sum(2	,       keepdim=__snake_case)


  def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case=1):
   return self.softmax(T * self.cos(__snake_case	,       __snake_case))






  def 		A__					(				self	,       __snake_case	,       __snake_case):
   _UpperCamelCase   :		List[Any]			=						W_supports['sizes'].tolist()
   _UpperCamelCase   :		Dict			=						W_supports['start_token_id'].item()
   _UpperCamelCase   :		str			=						W_supports['end_token_id'].item()

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

   _UpperCamelCase   :		Optional[Any]			=						self.BERT(**__snake_case)
   _UpperCamelCase   :		List[str]			=						self.BERT(**__snake_case)

   _UpperCamelCase   :		Union[str, Any]			=						None
   _UpperCamelCase   :		Optional[Any]			=						None

   _UpperCamelCase   :		Any			=						W_supports['input_ids'] == start_token_id
   _UpperCamelCase   :		Optional[Any]			=						W_supports['input_ids'] == end_token_id

   for i, size in enumerate(__snake_case):
    if i == 0:
     _UpperCamelCase   :		str			=						0
    else:
     _UpperCamelCase   :		Dict			=						support_sizes[i - 1]

    _UpperCamelCase   :		Optional[Any]			=						S[s : s + size][start_token_masks[s : s + size]]
    _UpperCamelCase   :		Union[str, Any]			=						S[s : s + size][end_token_masks[s : s + size]]

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

    if p_starts is not None:
     _UpperCamelCase   :		List[Any]			=						torch.vstack((p_starts, p_start))
     _UpperCamelCase   :		Any			=						torch.vstack((p_ends, p_end))
    else:
     _UpperCamelCase   :		int			=						p_start
     _UpperCamelCase   :		Tuple			=						p_end

   return p_starts, p_ends







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="absolute"	,       __snake_case=True	,       __snake_case=None	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

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







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




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




