



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

from ..utils.generic import ModelOutput


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

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

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

 def __getitem__(    self					:					Dict			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Any:
    _A      =       self.dataset[i]
    _A      =       self.process(__lowerCAmelCase			,						**self.params      )
    return processed


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Dict			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Optional[int]=None      )   ->	int:
    _A      =       loader
    _A      =       infer
    _A      =       params
    if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       _A      =       None
    _A      =       loader_batch_size

    # Internal bookkeeping
    _A      =       None
    _A      =       None

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

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[int]:
    if isinstance(self._loader_batch_data			,						torch.Tensor      ):
       # Batch data is simple tensor, just fetch the slice
       _A      =       self._loader_batch_data[self._loader_batch_index]
    else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       _A      =       {}
       for k, element in self._loader_batch_data.items():
          if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
             # Convert ModelOutput to tuple first
             _A      =       element.to_tuple()
             if isinstance(element[0]			,						torch.Tensor      ):
                _A      =       tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
             elif isinstance(element[0]			,						np.ndarray      ):
                _A      =       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      ):
                _A      =       tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
             elif isinstance(element[0]			,						np.ndarray      ):
                _A      =       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      =       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      =       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      =       np.expand_dims(element[self._loader_batch_index]			,						0      )
          else:
             # This is typically a list, so no need to `unsqueeze`.
             _A      =       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
       _A      =       self._loader_batch_data.__class__(__lowerCAmelCase      )
    self._loader_batch_index += 1
    return result

 def     snake_case_      (    self					:					str      )   ->	str:
    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      =       next(self.iterator      )
    _A      =       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      ):
          _A      =       processed
       else:
          _A      =       list(processed.keys()      )[0]
          _A      =       processed[key]
       if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          _A      =       len(__lowerCAmelCase      )
       else:
          _A      =       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      =       observed_batch_size
       # Setting internal index to unwrap the batch
       _A      =       processed
       _A      =       0
       return self.loader_batch_item()
    else:
       # We're not unrolling batches
       return processed


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Dict			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					str=None      )   ->	int:
    super().__init__(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

 def __iter__(    self					:					Optional[Any]      )   ->	str:
    _A      =       iter(self.loader      )
    _A      =       None
    return self

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


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

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

 def     snake_case_      (    self					:					List[Any]      )   ->	Dict:
    # 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.
    _A      =       False
    _A      =       []
    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      =       self.loader_batch_item()
          _A      =       item.pop('''is_last'''      )
          accumulator.append(__lowerCAmelCase      )
          if is_last:
             return accumulator

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


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					Dataset			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    _A      =       dataset
    _A      =       key

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

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


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					Dataset			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    _A      =       dataset
    _A      =       keya
    _A      =       keya

 def __len__(    self					:					Union[str, Any]      )   ->	Optional[int]:
    return len(self.dataset      )

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)