





'''simple docstring'''





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

from ..utils.generic import ModelOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[str]       ,      __a   :   List[str]       ,      __a   :   Tuple       ,      __a   :   Optional[Any]			):
         UpperCAmelCase_	   =       dataset
         UpperCAmelCase_	   =       process
         UpperCAmelCase_	   =       params



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






    def __getitem__(self   :   Union[str, Any]       ,      __a   :   List[str]			):
         UpperCAmelCase_	   =       self.dataset[i]
         UpperCAmelCase_	   =       self.process(__a       ,      **self.params			)
         return processed




class 	__A				(			UpperCamelCase__     ):



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

         # Internal bookkeeping
         UpperCAmelCase_	   =       None
         UpperCAmelCase_	   =       None



    def __len__(self   :   Any			):
         return len(self.loader			)



    def __iter__(self   :   Dict			):
         UpperCAmelCase_	   =       iter(self.loader			)
         return self



    def  _lowercase						(self   :   Tuple			):
         if isinstance(self._loader_batch_data       ,      torch.Tensor			):
              # Batch data is simple tensor, just fetch the slice
              UpperCAmelCase_	   =       self._loader_batch_data[self._loader_batch_index]
         else:
              # Batch data is assumed to be BaseModelOutput (or dict)
              UpperCAmelCase_	   =       {}
              for k, element in self._loader_batch_data.items():
                   if isinstance(__a       ,      __a			):
                        # Convert ModelOutput to tuple first
                        UpperCAmelCase_	   =       element.to_tuple()
                        if isinstance(element[0]       ,      torch.Tensor			):
                             UpperCAmelCase_	   =       tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                        elif isinstance(element[0]       ,      np.ndarray			):
                             UpperCAmelCase_	   =       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			):
                             UpperCAmelCase_	   =       tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                        elif isinstance(element[0]       ,      np.ndarray			):
                             UpperCAmelCase_	   =       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
                        UpperCAmelCase_	   =       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

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






    def  _lowercase						(self   :   Optional[Any]			):
         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
         UpperCAmelCase_	   =       next(self.iterator			)
         UpperCAmelCase_	   =       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			):
                   UpperCAmelCase_	   =       processed
              else:
                   UpperCAmelCase_	   =       list(processed.keys()			)[0]
                   UpperCAmelCase_	   =       processed[key]
              if isinstance(__a       ,      __a			):
                   UpperCAmelCase_	   =       len(__a			)
              else:
                   UpperCAmelCase_	   =       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.
                   UpperCAmelCase_	   =       observed_batch_size
              # Setting internal index to unwrap the batch
              UpperCAmelCase_	   =       processed
              UpperCAmelCase_	   =       0
              return self.loader_batch_item()
         else:
              # We're not unrolling batches
              return processed




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   List[Any]       ,      __a   :   List[Any]       ,      __a   :   List[str]       ,      __a   :   Optional[Any]       ,      __a   :   Dict=None			):
         super().__init__(__a       ,      __a       ,      __a			)



    def __iter__(self   :   Tuple			):
         UpperCAmelCase_	   =       iter(self.loader			)
         UpperCAmelCase_	   =       None
         return self






    def  _lowercase						(self   :   str			):
         if self.subiterator is None:
              UpperCAmelCase_	   =       self.infer(next(self.iterator			)       ,      **self.params			)
         try:
              # Try to return next item
              UpperCAmelCase_	   =       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
              UpperCAmelCase_	   =       self.infer(next(self.iterator			)       ,      **self.params			)
              UpperCAmelCase_	   =       next(self.subiterator			)
         return processed




class 	__A				(			UpperCamelCase__     ):



    def __iter__(self   :   str			):
         UpperCAmelCase_	   =       iter(self.loader			)
         return self






    def  _lowercase						(self   :   Optional[int]			):
         # 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.
         UpperCAmelCase_	   =       False
         UpperCAmelCase_	   =       []
         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:
                   UpperCAmelCase_	   =       self.loader_batch_item()
                   UpperCAmelCase_	   =       item.pop("is_last"			)
                   accumulator.append(__a			)
                   if is_last:
                        return accumulator

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




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   str       ,      __a   :   Dataset       ,      __a   :   str			):
         UpperCAmelCase_	   =       dataset
         UpperCAmelCase_	   =       key



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






    def __getitem__(self   :   Union[str, Any]       ,      __a   :   int			):
         return self.dataset[i][self.key]




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   int       ,      __a   :   Dataset       ,      __a   :   str       ,      __a   :   str			):
         UpperCAmelCase_	   =       dataset
         UpperCAmelCase_	   =       keya
         UpperCAmelCase_	   =       keya



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






    def __getitem__(self   :   Optional[int]       ,      __a   :   Optional[int]			):
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





'''simple docstring'''





from typing import Mapping

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


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class 	__A				(			UpperCamelCase__     ):
    a__	:						Dict	       =  """t5"""
    a__	:						List[str]	       =  ["""past_key_values"""]
    a__	:						Union[str, Any]	       =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



    def __init__(self   :   List[str]       ,      __a   :   Any=32128       ,      __a   :   Optional[int]=512       ,      __a   :   Union[str, Any]=64       ,      __a   :   List[str]=2048       ,      __a   :   Optional[int]=6       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=8       ,      __a   :   Dict=32       ,      __a   :   List[Any]=128       ,      __a   :   Optional[Any]=0.1       ,      __a   :   List[Any]=1E-6       ,      __a   :   str=1.0       ,      __a   :   Dict="relu"       ,      __a   :   int=True       ,      __a   :   int=True       ,      __a   :   Optional[Any]=0       ,      __a   :   Dict=1       ,      **__a   :   List[Any]       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       d_model
         UpperCAmelCase_	   =       d_kv
         UpperCAmelCase_	   =       d_ff
         UpperCAmelCase_	   =       num_layers
         UpperCAmelCase_	   =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_	   =       num_heads
         UpperCAmelCase_	   =       relative_attention_num_buckets
         UpperCAmelCase_	   =       relative_attention_max_distance
         UpperCAmelCase_	   =       dropout_rate
         UpperCAmelCase_	   =       layer_norm_epsilon
         UpperCAmelCase_	   =       initializer_factor
         UpperCAmelCase_	   =       feed_forward_proj
         UpperCAmelCase_	   =       use_cache

         UpperCAmelCase_	   =       self.feed_forward_proj.split("-"			)
         UpperCAmelCase_	   =       act_info[-1]
         UpperCAmelCase_	   =       act_info[0] == "gated"

         if len(__a			) > 1 and act_info[0] != "gated" or len(__a			) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"			)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_	   =       "gelu_new"

         super().__init__(
             pad_token_id=__a       ,      eos_token_id=__a       ,      is_encoder_decoder=__a       ,      **__a       ,      )




class 	__A				(			UpperCamelCase__     ):



    @property
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       {
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
              UpperCAmelCase_	   =       "past_encoder_sequence + sequence"
              UpperCAmelCase_	   =       {0: "batch"}
              UpperCAmelCase_	   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
              self.fill_with_past_key_values_(__a       ,      direction="inputs"			)

         return common_inputs






    @property
    def  _lowercase						(self   :   Optional[int]			):
         return 13




