


'''simple docstring'''






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

from ..utils.generic import ModelOutput






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[int]		)   ->  Optional[int]:
      lowerCAmelCase_			:      int			=   dataset
      lowerCAmelCase_			:      int			=   process
      lowerCAmelCase_			:      List[Any]			=   params

    def __len__(   self    :  str		)   ->  List[Any]:
      return len(self.dataset		)





    def __getitem__(   self    :  List[str]    ,						lowerCamelCase    :  Optional[Any]		)   ->  Any:
      lowerCAmelCase_			:      List[Any]			=   self.dataset[i]
      lowerCAmelCase_			:      Optional[Any]			=   self.process(lowerCamelCase    ,						**self.params		)
      return processed







class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Any    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Optional[Any]=None		)   ->  Optional[int]:
      lowerCAmelCase_			:      List[str]			=   loader
      lowerCAmelCase_			:      Optional[Any]			=   infer
      lowerCAmelCase_			:      str			=   params
      if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        lowerCAmelCase_			:      str			=   None
      lowerCAmelCase_			:      int			=   loader_batch_size

      # Internal bookkeeping
      lowerCAmelCase_			:      Any			=   None
      lowerCAmelCase_			:      Dict			=   None

    def __len__(   self    :  Optional[int]		)   ->  Dict:
      return len(self.loader		)

    def __iter__(   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      Optional[int]			=   iter(self.loader		)
      return self

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

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





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







class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Dict    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  str=None		)   ->  str:
      super().__init__(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		)

    def __iter__(   self    :  Union[str, Any]		)   ->  Tuple:
      lowerCAmelCase_			:      Union[str, Any]			=   iter(self.loader		)
      lowerCAmelCase_			:      int			=   None
      return self





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







class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __iter__(   self    :  Dict		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   iter(self.loader		)
      return self





    def __lowercase   (   self    :  Tuple		)   ->  List[Any]:
      # 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.
      lowerCAmelCase_			:      int			=   False
      lowerCAmelCase_			:      Optional[int]			=   []
      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:
          lowerCAmelCase_			:      Any			=   self.loader_batch_item()
          lowerCAmelCase_			:      Dict			=   item.pop("""is_last"""		)
          accumulator.append(lowerCamelCase		)
          if is_last:
            return accumulator

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







class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Tuple    ,						lowerCamelCase    :  Dataset    ,						lowerCamelCase    :  str		)   ->  Any:
      lowerCAmelCase_			:      Dict			=   dataset
      lowerCAmelCase_			:      Optional[Any]			=   key

    def __len__(   self    :  List[Any]		)   ->  List[str]:
      return len(self.dataset		)





    def __getitem__(   self    :  Optional[int]    ,						lowerCamelCase    :  Any		)   ->  Tuple:
      return self.dataset[i][self.key]







class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Any    ,						lowerCamelCase    :  Dataset    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  str		)   ->  str:
      lowerCAmelCase_			:      str			=   dataset
      lowerCAmelCase_			:      List[Any]			=   keya
      lowerCAmelCase_			:      int			=   keya

    def __len__(   self    :  int		)   ->  List[Any]:
      return len(self.dataset		)





    def __getitem__(   self    :  str    ,						lowerCamelCase    :  Tuple		)   ->  Optional[int]:
      return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A					:  Dict											=      {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A					:  str											=      ["TimmBackbone"]


if TYPE_CHECKING:
  from .configuration_timm_backbone import TimmBackboneConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timm_backbone import TimmBackbone

else:
  import sys

  __A					:  Dict											=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



