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

from ..utils.generic import ModelOutput




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[int]										=	dataset
        a__:     Union[str, Any]										=	process
        a__:     Any										=	params



      def __len__(						self)	->			List[str]:





        '''simple docstring'''





        return len(self.dataset)



      def __getitem__(						self    ,			lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     int										=	self.dataset[i]
        a__:     Any										=	self.process(lowercase    ,			**self.params)
        return processed







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	loader
        a__:     Any										=	infer
        a__:     List[str]										=	params
        if loader_batch_size == 1:
          # Let's spare some time by deactivating altogether
          a__:     Optional[Any]										=	None
        a__:     List[str]										=	loader_batch_size

        # Internal bookkeeping
        a__:     int										=	None
        a__:     List[str]										=	None



      def __len__(						self)	->			Union[str, Any]:





        '''simple docstring'''





        return len(self.loader)



      def __iter__(						self)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	iter(self.loader)
        return self



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        if isinstance(self._loader_batch_data    ,			torch.Tensor):
          # Batch data is simple tensor, just fetch the slice
          a__:     List[str]										=	self._loader_batch_data[self._loader_batch_index]
        else:
          # Batch data is assumed to be BaseModelOutput (or dict)
          a__:     List[str]										=	{}
          for k, element in self._loader_batch_data.items():
            if isinstance(lowercase    ,			lowercase):
              # Convert ModelOutput to tuple first
              a__:     Optional[Any]										=	element.to_tuple()
              if isinstance(element[0]    ,			torch.Tensor):
                a__:     Union[str, Any]										=	tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
              elif isinstance(element[0]    ,			np.ndarray):
                a__:     List[str]										=	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(lowercase    ,			lowercase):
              # Those are stored as lists of tensors so need specific unbatching.
              if isinstance(element[0]    ,			torch.Tensor):
                a__:     Any										=	tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
              elif isinstance(element[0]    ,			np.ndarray):
                a__:     Union[str, Any]										=	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
              a__:     List[Any]										=	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

              a__:     List[str]										=	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
              a__:     Optional[int]										=	np.expand_dims(element[self._loader_batch_index]    ,			0)
            else:
              # This is typically a list, so no need to `unsqueeze`.
              a__:     int										=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
          a__:     List[Any]										=	self._loader_batch_data.__class__(lowercase)
        self._loader_batch_index += 1
        return result



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        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
        a__:     Union[str, Any]										=	next(self.iterator)
        a__:     Any										=	self.infer(lowercase    ,			**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(lowercase    ,			torch.Tensor):
            a__:     int										=	processed
          else:
            a__:     List[str]										=	list(processed.keys())[0]
            a__:     str										=	processed[key]
          if isinstance(lowercase    ,			lowercase):
            a__:     Tuple										=	len(lowercase)
          else:
            a__:     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.
            a__:     Any										=	observed_batch_size
          # Setting internal index to unwrap the batch
          a__:     Dict										=	processed
          a__:     int										=	0
          return self.loader_batch_item()
        else:
          # We're not unrolling batches
          return processed







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        super().__init__(lowercase    ,			lowercase    ,			lowercase)



      def __iter__(						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	iter(self.loader)
        a__:     List[str]										=	None
        return self



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        if self.subiterator is None:
          a__:     List[str]										=	self.infer(next(self.iterator)    ,			**self.params)
        try:
          # Try to return next item
          a__:     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
          a__:     Union[str, Any]										=	self.infer(next(self.iterator)    ,			**self.params)
          a__:     List[Any]										=	next(self.subiterator)
        return processed







class    __snake_case       (     __lowerCAmelCase				):



      def __iter__(						self)	->			Any:





        '''simple docstring'''





        a__:     Optional[Any]										=	iter(self.loader)
        return self



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Any										=	False
        a__:     Optional[Any]										=	[]
        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:
            a__:     int										=	self.loader_batch_item()
            a__:     Tuple										=	item.pop('is_last')
            accumulator.append(lowercase)
            if is_last:
              return accumulator

        while not is_last:
          a__:     Any										=	self.infer(next(self.iterator)    ,			**self.params)
          if self.loader_batch_size is not None:
            if isinstance(lowercase    ,			torch.Tensor):
              a__:     Optional[Any]										=	processed
            else:
              a__:     List[Any]										=	list(processed.keys())[0]
              a__:     Union[str, Any]										=	processed[key]
            if isinstance(lowercase    ,			lowercase):
              a__:     Dict										=	len(lowercase)
            else:
              a__:     Optional[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.
              a__:     str										=	observed_batch_size
            a__:     List[str]										=	processed
            a__:     Optional[Any]										=	0
            while self._loader_batch_index < self.loader_batch_size:
              a__:     int										=	self.loader_batch_item()
              a__:     Tuple										=	item.pop('is_last')
              accumulator.append(lowercase)
              if is_last:
                return accumulator
          else:
            a__:     List[str]										=	processed
            a__:     List[Any]										=	item.pop('is_last')
            accumulator.append(lowercase)
        return accumulator







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	dataset
        a__:     List[Any]										=	key



      def __len__(						self)	->			str:





        '''simple docstring'''





        return len(self.dataset)



      def __getitem__(						self    ,			lowercase)	->			Optional[Any]:





        '''simple docstring'''





        return self.dataset[i][self.key]







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     int										=	dataset
        a__:     str										=	keya
        a__:     Any										=	keya



      def __len__(						self)	->			List[str]:





        '''simple docstring'''





        return len(self.dataset)



      def __getitem__(						self    ,			lowercase)	->			Optional[Any]:





        '''simple docstring'''





        return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



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

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


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""roberta-prelayernorm"""



      def __init__(						self    ,			lowercase=5_02_65    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=1    ,			lowercase=0    ,			lowercase=2    ,			lowercase="absolute"    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			Any:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase    ,			bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)

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







class    __snake_case       (     __lowerCAmelCase				):



      @property
      def   lowerCamelCase_   (						self)	->			Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





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


