
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


_a      :			Union[str, Any]					=							logging.get_logger(__name__)

_a      :			Dict					=							{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}






class      _UpperCAmelCase				(					lowercase__):
    __lowercase							: Tuple  =					"""ibert"""
    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_=0.02						,    snake_case_=1E-12						,    snake_case_=1						,    snake_case_=0						,    snake_case_=2						,    snake_case_="absolute"						,    snake_case_=False						,    snake_case_="none"						,    **snake_case_						,    ):
      super().__init__(pad_token_id=__lowerCamelCase						,    bos_token_id=__lowerCamelCase						,    eos_token_id=__lowerCamelCase						,    **__lowerCamelCase     )

      _snake_case     :						Any						=   vocab_size
      _snake_case     :						Optional[Any]						=   hidden_size
      _snake_case     :						Optional[int]						=   num_hidden_layers
      _snake_case     :						int						=   num_attention_heads
      _snake_case     :						List[str]						=   hidden_act
      _snake_case     :						List[str]						=   intermediate_size
      _snake_case     :						Optional[int]						=   hidden_dropout_prob
      _snake_case     :						Any						=   attention_probs_dropout_prob
      _snake_case     :						Tuple						=   max_position_embeddings
      _snake_case     :						Any						=   type_vocab_size
      _snake_case     :						Optional[int]						=   initializer_range
      _snake_case     :						Tuple						=   layer_norm_eps
      _snake_case     :						int						=   position_embedding_type
      _snake_case     :						List[str]						=   quant_mode
      _snake_case     :						int						=   force_dequant






class      _UpperCAmelCase				(					lowercase__):
    @property
    def 					lowerCamelCase__	(    self     ):



      if self.task == "multiple-choice":
        _snake_case     :						Any						=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
        _snake_case     :						Any						=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]     )



"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Optional[int]						=   dataset
      _snake_case     :						str						=   process
      _snake_case     :						int						=   params
    def __len__(    self     ):
      return len(self.dataset     )


    def __getitem__(    self						,    snake_case_     ):
      _snake_case     :						Union[str, Any]						=   self.dataset[i]
      _snake_case     :						Optional[Any]						=   self.process(snake_case_						,    **self.params     )
      return processed






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_=None     ):
      _snake_case     :						Union[str, Any]						=   loader
      _snake_case     :						Tuple						=   infer
      _snake_case     :						List[Any]						=   params
      if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        _snake_case     :						int						=   None
      _snake_case     :						int						=   loader_batch_size

      # Internal bookkeeping
      _snake_case     :						Any						=   None
      _snake_case     :						Dict						=   None
    def __len__(    self     ):
      return len(self.loader     )
    def __iter__(    self     ):
      _snake_case     :						int						=   iter(self.loader     )
      return self
    def 					lowerCamelCase__	(    self     ):
      if isinstance(self._loader_batch_data						,    torch.Tensor     ):
        # Batch data is simple tensor, just fetch the slice
        _snake_case     :						List[Any]						=   self._loader_batch_data[self._loader_batch_index]
      else:
        # Batch data is assumed to be BaseModelOutput (or dict)
        _snake_case     :						int						=   {}
        for k, element in self._loader_batch_data.items():
          if isinstance(snake_case_						,    snake_case_     ):
            # Convert ModelOutput to tuple first
            _snake_case     :						Tuple						=   element.to_tuple()
            if isinstance(element[0]						,    torch.Tensor     ):
              _snake_case     :						Union[str, Any]						=   tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
            elif isinstance(element[0]						,    np.ndarray     ):
              _snake_case     :						int						=   tuple(np.expand_dims(el[self._loader_batch_index]						,    0     ) for el in element     )
            continue
          if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_						,    snake_case_     ):
            # Those are stored as lists of tensors so need specific unbatching.
            if isinstance(element[0]						,    torch.Tensor     ):
              _snake_case     :						Tuple						=   tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
            elif isinstance(element[0]						,    np.ndarray     ):
              _snake_case     :						Tuple						=   tuple(np.expand_dims(el[self._loader_batch_index]						,    0     ) for el in element     )
            continue
          if element is None:
            # This can happen for optional data that get passed around
            _snake_case     :						Tuple						=   None
          elif isinstance(element[self._loader_batch_index]						,    torch.Tensor     ):
            # Take correct batch data, but make it looked like batch_size=1
            # For compatibility with other methods within transformers

            _snake_case     :						List[Any]						=   element[self._loader_batch_index].unsqueeze(0     )
          elif isinstance(element[self._loader_batch_index]						,    np.ndarray     ):
            # Take correct batch data, but make it looked like batch_size=1
            # For compatibility with other methods within transformers
            _snake_case     :						Union[str, Any]						=   np.expand_dims(element[self._loader_batch_index]						,    0     )
          else:
            # This is typically a list, so no need to `unsqueeze`.
            _snake_case     :						List[Any]						=   element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
        _snake_case     :						int						=   self._loader_batch_data.__class__(snake_case_     )
      self._loader_batch_index += 1
      return result


    def 					lowerCamelCase__	(    self     ):
      if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
        # We are currently unrolling a batch so we just need to return
        # the current item within a batch
        return self.loader_batch_item()

      # We're out of items within a batch
      _snake_case     :						Tuple						=   next(self.iterator     )
      _snake_case     :						Any						=   self.infer(snake_case_						,    **self.params     )
      # We now have a batch of "inferred things".
      if self.loader_batch_size is not None:
        # Try to infer the size of the batch
        if isinstance(snake_case_						,    torch.Tensor     ):
          _snake_case     :						Union[str, Any]						=   processed
        else:
          _snake_case     :						Optional[int]						=   list(processed.keys()     )[0]
          _snake_case     :						List[str]						=   processed[key]
        if isinstance(snake_case_						,    snake_case_     ):
          _snake_case     :						Dict						=   len(snake_case_     )
        else:
          _snake_case     :						Optional[int]						=   first_tensor.shape[0]
        if 0 < observed_batch_size < self.loader_batch_size:
          # could be last batch so we can't unroll as many
          # elements.
          _snake_case     :						Union[str, Any]						=   observed_batch_size
        # Setting internal index to unwrap the batch
        _snake_case     :						str						=   processed
        _snake_case     :						List[Any]						=   0
        return self.loader_batch_item()
      else:
        # We're not unrolling batches
        return processed






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_=None     ):
      super().__init__(snake_case_						,    snake_case_						,    snake_case_     )
    def __iter__(    self     ):
      _snake_case     :						Tuple						=   iter(self.loader     )
      _snake_case     :						List[Any]						=   None
      return self


    def 					lowerCamelCase__	(    self     ):
      if self.subiterator is None:
        _snake_case     :						Optional[Any]						=   self.infer(next(self.iterator     )						,    **self.params     )
      try:
        # Try to return next item
        _snake_case     :						Union[str, Any]						=   next(self.subiterator     )
      except StopIteration:
        # When a preprocess iterator ends, we can start lookig at the next item
        # ChunkIterator will keep feeding until ALL elements of iterator
        # all have created their subiterator and have been iterating against.
        #
        # Another way to look at it, is we're basically flattening lists of lists
        # into a single list, but with generators
        _snake_case     :						str						=   self.infer(next(self.iterator     )						,    **self.params     )
        _snake_case     :						Tuple						=   next(self.subiterator     )
      return processed






class      _UpperCAmelCase				(					_snake_case):
    def __iter__(    self     ):
      _snake_case     :						Optional[Any]						=   iter(self.loader     )
      return self


    def 					lowerCamelCase__	(    self     ):
      # Extremely similar to PipelineIterator in its unpacking mechanism
      # BUT, we have an extra required item which is the presence of `is_last`
      # That is because everything is flattened by `PipelineChunkIterator` we
      # need to keep track of how to regroup here in the original `process`
      # boundaries so that `process` and `postprocess` see the same data.

      # This iterator accumulates items (possibly while unbatching) until it
      # its a `is_last` and then just passes it on to the caller.
      _snake_case     :						Optional[Any]						=   False
      _snake_case     :						Tuple						=   []
      if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
        while self._loader_batch_index < self.loader_batch_size:
          _snake_case     :						Union[str, Any]						=   self.loader_batch_item()
          _snake_case     :						str						=   item.pop("is_last"     )
          accumulator.append(snake_case_     )
          if is_last:
            return accumulator

      while not is_last:
        _snake_case     :						List[str]						=   self.infer(next(self.iterator     )						,    **self.params     )
        if self.loader_batch_size is not None:
          if isinstance(snake_case_						,    torch.Tensor     ):
            _snake_case     :						Union[str, Any]						=   processed
          else:
            _snake_case     :						Tuple						=   list(processed.keys()     )[0]
            _snake_case     :						Tuple						=   processed[key]
          if isinstance(snake_case_						,    snake_case_     ):
            _snake_case     :						Any						=   len(snake_case_     )
          else:
            _snake_case     :						List[Any]						=   first_tensor.shape[0]
          if 0 < observed_batch_size < self.loader_batch_size:
            # could be last batch so we can't unroll as many
            # elements.
            _snake_case     :						Dict						=   observed_batch_size
          _snake_case     :						List[Any]						=   processed
          _snake_case     :						List[str]						=   0
          while self._loader_batch_index < self.loader_batch_size:
            _snake_case     :						Union[str, Any]						=   self.loader_batch_item()
            _snake_case     :						int						=   item.pop("is_last"     )
            accumulator.append(snake_case_     )
            if is_last:
              return accumulator
        else:
          _snake_case     :						Dict						=   processed
          _snake_case     :						Dict						=   item.pop("is_last"     )
          accumulator.append(snake_case_     )
      return accumulator






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      _snake_case     :						str						=   dataset
      _snake_case     :						Any						=   key
    def __len__(    self     ):
      return len(self.dataset     )


    def __getitem__(    self						,    snake_case_     ):
      return self.dataset[i][self.key]






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						int						=   dataset
      _snake_case     :						Any						=   keya
      _snake_case     :						int						=   keya
    def __len__(    self     ):
      return len(self.dataset     )


    def __getitem__(    self						,    snake_case_     ):
      return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

