




'''simple docstring'''



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

from ..utils.generic import ModelOutput







class 		lowercase_   (			_a       ):


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				dataset
      _SCREAMING_SNAKE_CASE	=				process
      _SCREAMING_SNAKE_CASE	=				params



   def __len__(   self :     List[Any]   ):

      """simple docstring"""
      return len(self.dataset   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.dataset[i]
      _SCREAMING_SNAKE_CASE	=				self.process(_a  ,				**self.params   )
      return processed







class 		lowercase_   (			_a       ):


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[str]=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				loader
      _SCREAMING_SNAKE_CASE	=				infer
      _SCREAMING_SNAKE_CASE	=				params
      if loader_batch_size == 1:
         # Let's spare some time by deactivating altogether
         _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				loader_batch_size

      # Internal bookkeeping
      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None



   def __len__(   self :     Tuple   ):

      """simple docstring"""
      return len(self.loader   )



   def __iter__(   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				iter(self.loader   )
      return self



   def 							lowerCAmelCase_      (   self :     Tuple   ):

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

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """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
      _SCREAMING_SNAKE_CASE	=				next(self.iterator   )
      _SCREAMING_SNAKE_CASE	=				self.infer(_a  ,				**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(_a  ,				torch.Tensor   ):
            _SCREAMING_SNAKE_CASE	=				processed
         else:
            _SCREAMING_SNAKE_CASE	=				list(processed.keys()   )[0]
            _SCREAMING_SNAKE_CASE	=				processed[key]
         if isinstance(_a  ,				_a   ):
            _SCREAMING_SNAKE_CASE	=				len(_a   )
         else:
            _SCREAMING_SNAKE_CASE	=				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.
            _SCREAMING_SNAKE_CASE	=				observed_batch_size
         # Setting internal index to unwrap the batch
         _SCREAMING_SNAKE_CASE	=				processed
         _SCREAMING_SNAKE_CASE	=				0
         return self.loader_batch_item()
      else:
         # We're not unrolling batches
         return processed







class 		lowercase_   (			_a       ):


   """simple docstring"""

   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     int=None   ):

      """simple docstring"""
      super().__init__(_a  ,				_a  ,				_a   )



   def __iter__(   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				iter(self.loader   )
      _SCREAMING_SNAKE_CASE	=				None
      return self



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      if self.subiterator is None:
         _SCREAMING_SNAKE_CASE	=				self.infer(next(self.iterator   )  ,				**self.params   )
      try:
         # Try to return next item
         _SCREAMING_SNAKE_CASE	=				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
         _SCREAMING_SNAKE_CASE	=				self.infer(next(self.iterator   )  ,				**self.params   )
         _SCREAMING_SNAKE_CASE	=				next(self.subiterator   )
      return processed







class 		lowercase_   (			_a       ):


   """simple docstring"""

   def __iter__(   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				iter(self.loader   )
      return self



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      # 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.
      _SCREAMING_SNAKE_CASE	=				False
      _SCREAMING_SNAKE_CASE	=				[]
      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:
            _SCREAMING_SNAKE_CASE	=				self.loader_batch_item()
            _SCREAMING_SNAKE_CASE	=				item.pop("is_last"   )
            accumulator.append(_a   )
            if is_last:
               return accumulator

      while not is_last:
         _SCREAMING_SNAKE_CASE	=				self.infer(next(self.iterator   )  ,				**self.params   )
         if self.loader_batch_size is not None:
            if isinstance(_a  ,				torch.Tensor   ):
               _SCREAMING_SNAKE_CASE	=				processed
            else:
               _SCREAMING_SNAKE_CASE	=				list(processed.keys()   )[0]
               _SCREAMING_SNAKE_CASE	=				processed[key]
            if isinstance(_a  ,				_a   ):
               _SCREAMING_SNAKE_CASE	=				len(_a   )
            else:
               _SCREAMING_SNAKE_CASE	=				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.
               _SCREAMING_SNAKE_CASE	=				observed_batch_size
            _SCREAMING_SNAKE_CASE	=				processed
            _SCREAMING_SNAKE_CASE	=				0
            while self._loader_batch_index < self.loader_batch_size:
               _SCREAMING_SNAKE_CASE	=				self.loader_batch_item()
               _SCREAMING_SNAKE_CASE	=				item.pop("is_last"   )
               accumulator.append(_a   )
               if is_last:
                  return accumulator
         else:
            _SCREAMING_SNAKE_CASE	=				processed
            _SCREAMING_SNAKE_CASE	=				item.pop("is_last"   )
            accumulator.append(_a   )
      return accumulator







class 		lowercase_   (			_a       ):


   """simple docstring"""

   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     Dataset  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				dataset
      _SCREAMING_SNAKE_CASE	=				key



   def __len__(   self :     Optional[int]   ):

      """simple docstring"""
      return len(self.dataset   )



   def __getitem__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      return self.dataset[i][self.key]







class 		lowercase_   (			_a       ):


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Dataset  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				dataset
      _SCREAMING_SNAKE_CASE	=				keya
      _SCREAMING_SNAKE_CASE	=				keya



   def __len__(   self :     Optional[Any]   ):

      """simple docstring"""
      return len(self.dataset   )



   def __getitem__(   self :     List[str]  ,				__lowerCamelCase :     Any   ):

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







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_			=						{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CTRLForSequenceClassification',
     'CTRLLMHeadModel',
     'CTRLModel',
     'CTRLPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCTRLForSequenceClassification',
     'TFCTRLLMHeadModel',
     'TFCTRLModel',
     'TFCTRLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .tokenization_ctrl import CTRLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ctrl import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
      CTRLPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
      TFCTRLPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

