






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       def __init__(				self	:List[str],      snake_case	:NestedDataStructureLike[PathLike],      snake_case	:Optional[NamedSplit] = None,      snake_case	:Optional[Features] = None,      snake_case	:str = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:Optional[str] = None,      snake_case	:Optional[int] = None,      **snake_case	:Union[str, Any],      ):



          """simple docstring"""

          super().__init__(
              snake_case,      split=snake_case,      features=snake_case,      cache_dir=snake_case,      keep_in_memory=snake_case,      streaming=snake_case,      num_proc=snake_case,      **snake_case,      )
          _lowercase              =field
          _lowercase              =path_or_paths if isinstance(snake_case,      snake_case) else {self.split: path_or_paths}
          _lowercase              =Json(
              cache_dir=snake_case,      data_files=snake_case,      features=snake_case,      field=snake_case,      **snake_case,      )





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



          """simple docstring"""

          if self.streaming:
             _lowercase              =self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
             _lowercase              =None
             _lowercase              =None
             _lowercase              =None
             _lowercase              =None

             self.builder.download_and_prepare(
                 download_config=snake_case,      download_mode=snake_case,      verification_mode=snake_case,      base_path=snake_case,      num_proc=self.num_proc,      )
             _lowercase              =self.builder.as_dataset(
                 split=self.split,      verification_mode=snake_case,      in_memory=self.keep_in_memory)
          return dataset






class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:Any,      snake_case	:Dataset,      snake_case	:Union[PathLike, BinaryIO],      snake_case	:Optional[int] = None,      snake_case	:Optional[int] = None,      **snake_case	:List[str],      ):



          """simple docstring"""

          if num_proc is not None and num_proc <= 0:
             raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''')

          _lowercase              =dataset
          _lowercase              =path_or_buf
          _lowercase              =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          _lowercase              =num_proc
          _lowercase              ='utf-8'
          _lowercase              =to_json_kwargs





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.to_json_kwargs.pop('path_or_buf',      snake_case)
          _lowercase              =self.to_json_kwargs.pop('orient',      'records')
          _lowercase              =self.to_json_kwargs.pop('lines',      True if orient == 'records' else False)
          _lowercase              =self.to_json_kwargs.pop('index',      False if orient in ['split', 'table'] else True)
          _lowercase              =self.to_json_kwargs.pop('compression',      snake_case)

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
             raise NotImplementedError(f'''`datasets` currently does not support {compression} compression''')

          if isinstance(self.path_or_buf,      (str, bytes, os.PathLike)):
             with fsspec.open(self.path_or_buf,      'wb',      compression=snake_case) as buffer:
                _lowercase              =self._write(file_obj=snake_case,      orient=snake_case,      lines=snake_case,      index=snake_case,      **self.to_json_kwargs)
          else:
             if compression:
                raise NotImplementedError(
                    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
                    ' was passed. Please provide a local path instead.')
             _lowercase              =self._write(
                 file_obj=self.path_or_buf,      orient=snake_case,      lines=snake_case,      index=snake_case,      **self.to_json_kwargs)
          return written





       def 				UpperCamelCase__						(				self	:int,      snake_case	:Tuple):



          """simple docstring"""

          _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase              =args

          _lowercase              =query_table(
              table=self.dataset.data,      key=slice(snake_case,      offset + self.batch_size),      indices=self.dataset._indices,      )
          _lowercase              =batch.to_pandas().to_json(
              path_or_buf=snake_case,      orient=snake_case,      lines=snake_case,      index=snake_case,      **snake_case)
          if not json_str.endswith('\n'):
             json_str += "\n"
          return json_str.encode(self.encoding)





       def 				UpperCamelCase__						(				self	:str,      snake_case	:BinaryIO,      snake_case	:List[Any],      snake_case	:Optional[int],      snake_case	:Optional[int],      **snake_case	:Union[str, Any],      ):



          """simple docstring"""

          _lowercase              =0

          if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,      len(self.dataset),      self.batch_size),      unit='ba',      disable=not logging.is_progress_bar_enabled(),      desc='Creating json from Arrow format',      ):
                _lowercase              =self._batch_json((offset, orient, lines, index, to_json_kwargs))
                written += file_obj.write(snake_case)
          else:
             _lowercase       ,       _lowercase              =len(self.dataset), self.batch_size
             with multiprocessing.Pool(self.num_proc) as pool:
                for json_str in logging.tqdm(
                    pool.imap(
                        self._batch_json,      [(offset, orient, lines, index, to_json_kwargs) for offset in range(0,      snake_case,      snake_case)],      ),      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,      unit='ba',      disable=not logging.is_progress_bar_enabled(),      desc='Creating json from Arrow format',      ):
                   written += file_obj.write(snake_case)

          return written










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


