

from typing import Mapping

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


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {
    '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  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  """t5"""
  _a									=  ["""past_key_values"""]
  _a									=  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




  def __init__(				self							,   lowerCAmelCase=32_128							,   lowerCAmelCase=512							,   lowerCAmelCase=64							,   lowerCAmelCase=2_048							,   lowerCAmelCase=6							,   lowerCAmelCase=None							,   lowerCAmelCase=8							,   lowerCAmelCase=32							,   lowerCAmelCase=128							,   lowerCAmelCase=0.1							,   lowerCAmelCase=1e-6							,   lowerCAmelCase=1.0							,   lowerCAmelCase="relu"							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   lowerCAmelCase=0							,   lowerCAmelCase=1							,   **lowerCAmelCase							,   )  -> Dict:
        '''simple docstring'''






        _lowercase	       =vocab_size
        _lowercase	       =d_model
        _lowercase	       =d_kv
        _lowercase	       =d_ff
        _lowercase	       =num_layers
        _lowercase	       =(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        _lowercase	       =num_heads
        _lowercase	       =relative_attention_num_buckets
        _lowercase	       =relative_attention_max_distance
        _lowercase	       =dropout_rate
        _lowercase	       =layer_norm_epsilon
        _lowercase	       =initializer_factor
        _lowercase	       =feed_forward_proj
        _lowercase	       =use_cache

        _lowercase	       =self.feed_forward_proj.split('-'					)
        _lowercase	       =act_info[-1]
        _lowercase	       =act_info[0] == 'gated'

        if len(lowerCAmelCase					) > 1 and act_info[0] != "gated" or len(lowerCAmelCase					) > 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":
              _lowercase	       ='gelu_new'

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





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  @property
  def   A__     (				self					)  -> Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        _lowercase	       ={
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
              _lowercase	       ='past_encoder_sequence + sequence'
              _lowercase	       ={0: 'batch'}
              _lowercase	       ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
              _lowercase	       ={0: 'batch', 1: 'decoder_sequence'}
              _lowercase	       ={0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
              self.fill_with_past_key_values_(lowerCAmelCase							,   direction='inputs'					)

        return common_inputs




  @property
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        return 13





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import torch





class  __lowerCAmelCase     (					TensorFormatter[Mapping, """torch.Tensor""", Mapping]							):




  def __init__(				self							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        super().__init__(features=lowerCAmelCase					)
        _lowercase	       =torch_tensor_kwargs
        import torch  # noqa import torch at initialization




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






        import torch

        if isinstance(lowerCAmelCase							,   lowerCAmelCase					) and column:
              if all(
                  isinstance(lowerCAmelCase							,   torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                  for x in column					):
                    return torch.stack(lowerCAmelCase					)
        return column




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        import torch

        if isinstance(lowerCAmelCase							,   (str, bytes, type(lowerCAmelCase					))					):
              return value
        elif isinstance(lowerCAmelCase							,   (np.character, np.ndarray)					) and np.issubdtype(value.dtype							,   np.character					):
              return value.tolist()

        _lowercase	       ={}

        if isinstance(lowerCAmelCase							,   (np.number, np.ndarray)					) and np.issubdtype(value.dtype							,   np.integer					):
              _lowercase	       ={'dtype': torch.intaa}
        elif isinstance(lowerCAmelCase							,   (np.number, np.ndarray)					) and np.issubdtype(value.dtype							,   np.floating					):
              _lowercase	       ={'dtype': torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(lowerCAmelCase							,   PIL.Image.Image					):
                    _lowercase	       =np.asarray(lowerCAmelCase					)
        return torch.tensor(lowerCAmelCase							,   **{**default_dtype, **self.torch_tensor_kwargs}					)




  def   A__     (				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        import torch

        # support for torch, tf, jax etc.
        if hasattr(lowerCAmelCase							,   '__array__'					) and not isinstance(lowerCAmelCase							,   torch.Tensor					):
              _lowercase	       =data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(lowerCAmelCase							,   np.ndarray					):
              if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                    return self._consolidate([self.recursive_tensorize(lowerCAmelCase					) for substruct in data_struct]					)
        elif isinstance(lowerCAmelCase							,   (list, tuple)					):
              return self._consolidate([self.recursive_tensorize(lowerCAmelCase					) for substruct in data_struct]					)
        return self._tensorize(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        return map_nested(self._recursive_tensorize							,   lowerCAmelCase							,   map_list=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Mapping:
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_row(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_row(lowerCAmelCase					)
        return self.recursive_tensorize(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> "torch.Tensor":
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_column(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_column(lowerCAmelCase							,   pa_table.column_names[0]					)
        _lowercase	       =self.recursive_tensorize(lowerCAmelCase					)
        _lowercase	       =self._consolidate(lowerCAmelCase					)
        return column




  def   A__     (				self							,   lowerCAmelCase					)  -> Mapping:
        '''simple docstring'''






        _lowercase	       =self.numpy_arrow_extractor().extract_batch(lowerCAmelCase					)
        _lowercase	       =self.python_features_decoder.decode_batch(lowerCAmelCase					)
        _lowercase	       =self.recursive_tensorize(lowerCAmelCase					)
        for column_name in batch:
              _lowercase	       =self._consolidate(batch[column_name]					)
        return batch


