





"""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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						int									=					dataset
     __A   :						Dict									=					process
     __A   :						Any									=					params

 def __len__(   self  ):



     '''simple docstring'''




     return len(self.dataset  )







 def __getitem__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.dataset[i]
     __A   :						List[Any]									=					self.process(__lowerCamelCase ,						**self.params  )
     return processed






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None  ):



     '''simple docstring'''




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

     # Internal bookkeeping
     __A   :						Any									=					None
     __A   :						str									=					None

 def __len__(   self  ):



     '''simple docstring'''




     return len(self.loader  )

 def __iter__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					iter(self.loader  )
     return self

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self  ):



     '''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   :						List[str]									=					next(self.iterator  )
     __A   :						Optional[Any]									=					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   :						str									=					processed
         else:
             __A   :						str									=					list(processed.keys()  )[0]
             __A   :						Dict									=					processed[key]
         if isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
             __A   :						Optional[Any]									=					len(__lowerCamelCase  )
         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   :						Union[str, Any]									=					observed_batch_size
         # Setting internal index to unwrap the batch
         __A   :						Any									=					processed
         __A   :						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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None  ):



     '''simple docstring'''




     super().__init__(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  )

 def __iter__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					iter(self.loader  )
     __A   :						List[Any]									=					None
     return self







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __iter__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					iter(self.loader  )
     return self







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					False
     __A   :						Dict									=					[]
     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   :						Tuple									=					self.loader_batch_item()
             __A   :						List[Any]									=					item.pop('''is_last'''  )
             accumulator.append(__lowerCamelCase  )
             if is_last:
                 return accumulator

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






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Dict									=					dataset
     __A   :						Optional[int]									=					key

 def __len__(   self  ):



     '''simple docstring'''




     return len(self.dataset  )







 def __getitem__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




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






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					dataset
     __A   :						Optional[Any]									=					keya
     __A   :						int									=					keya

 def __len__(   self  ):



     '''simple docstring'''




     return len(self.dataset  )







 def __getitem__(   self ,						__lowerCamelCase  ):



     '''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_tokenizers_available, is_torch_available


a_			=      {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""BloomTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """BloomForCausalLM""",
           """BloomModel""",
           """BloomPreTrainedModel""",
           """BloomForSequenceClassification""",
           """BloomForTokenClassification""",
           """BloomForQuestionAnswering""",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




