






import operator as op


_SCREAMING_SNAKE_CASE   =  "scaler.pt"
_SCREAMING_SNAKE_CASE   =  "pytorch_model"
_SCREAMING_SNAKE_CASE   =  "random_states"
_SCREAMING_SNAKE_CASE   =  "optimizer"
_SCREAMING_SNAKE_CASE   =  "scheduler"
_SCREAMING_SNAKE_CASE   =  "pytorch_model.bin"
_SCREAMING_SNAKE_CASE   =  "pytorch_model.bin.index.json"
_SCREAMING_SNAKE_CASE   =  "model.safetensors"
_SCREAMING_SNAKE_CASE   =  "model.safetensors.index.json"
_SCREAMING_SNAKE_CASE   =  "1.10.2"
_SCREAMING_SNAKE_CASE   =  "py38"
_SCREAMING_SNAKE_CASE   =  "4.17.0"
_SCREAMING_SNAKE_CASE   =  ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
_SCREAMING_SNAKE_CASE   =  ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
_SCREAMING_SNAKE_CASE   =  ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
_SCREAMING_SNAKE_CASE   =  ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
_SCREAMING_SNAKE_CASE   =  ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
_SCREAMING_SNAKE_CASE   =  "2.0.1"
_SCREAMING_SNAKE_CASE   =  ["pdsh", "standard", "openmpi", "mvapich"]
_SCREAMING_SNAKE_CASE   =  ["default", "reduce-overhead", "max-autotune"]

_SCREAMING_SNAKE_CASE   =  {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
_SCREAMING_SNAKE_CASE   =  [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

_SCREAMING_SNAKE_CASE   =  ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
_SCREAMING_SNAKE_CASE   =  ["DEEPSPEED", "MULTI_XPU", "FSDP"]










import torch

from transformers import AutoModel






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


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



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

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

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

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

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

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

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

          return p_starts, p_ends


