



"""simple docstring"""
# 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 			snake_case_						(						TensorFormatter[Mapping, "torch.Tensor", Mapping]				):






							def __init__(   self			,     a_=None			,     **a_      ):
									super().__init__(features=a_      )
									a_     :      Tuple 						=     torch_tensor_kwargs
									import torch  # noqa import torch at initialization






							def snake_case_  (   self			,     a_      ):
									import torch

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






							def snake_case_  (   self			,     a_      ):
									import torch

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

									a_     :      Dict 						=     {}

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

											if isinstance(a_			,     PIL.Image.Image      ):
													a_     :      List[Any] 						=     np.asarray(a_      )
									return torch.tensor(a_			,     **{**default_dtype, **self.torch_tensor_kwargs}      )






							def snake_case_  (   self			,     a_      ):
									import torch

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






							def snake_case_  (   self			,     a_      ):
									return map_nested(self._recursive_tensorize			,     a_			,     map_list=a_      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Optional[Any] 						=     self.numpy_arrow_extractor().extract_row(a_      )
									a_     :      int 						=     self.python_features_decoder.decode_row(a_      )
									return self.recursive_tensorize(a_      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Any 						=     self.numpy_arrow_extractor().extract_column(a_      )
									a_     :      Dict 						=     self.python_features_decoder.decode_column(a_			,     pa_table.column_names[0]      )
									a_     :      Optional[int] 						=     self.recursive_tensorize(a_      )
									a_     :      Union[str, Any] 						=     self._consolidate(a_      )
									return column






							def snake_case_  (   self			,     a_      ):
									a_     :      Optional[int] 						=     self.numpy_arrow_extractor().extract_batch(a_      )
									a_     :      Union[str, Any] 						=     self.python_features_decoder.decode_batch(a_      )
									a_     :      Optional[Any] 						=     self.recursive_tensorize(a_      )
									for column_name in batch:
											a_     :      int 						=     self._consolidate(batch[column_name]      )
									return batch




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"mra"






							def __init__(   self			,     a_=5_0_2_6_5			,     a_=7_6_8			,     a_=1_2			,     a_=1_2			,     a_=3_0_7_2			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=1			,     a_=0.02			,     a_=1e-5			,     a_="absolute"			,     a_=4			,     a_="full"			,     a_=0			,     a_=0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									a_     :      Optional[int] 						=     vocab_size
									a_     :      Dict 						=     max_position_embeddings
									a_     :      str 						=     hidden_size
									a_     :      Optional[int] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      List[str] 						=     intermediate_size
									a_     :      Tuple 						=     hidden_act
									a_     :      List[Any] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      List[Any] 						=     initializer_range
									a_     :      Dict 						=     type_vocab_size
									a_     :      Union[str, Any] 						=     layer_norm_eps
									a_     :      List[str] 						=     position_embedding_type
									a_     :      Union[str, Any] 						=     block_per_row
									a_     :      Tuple 						=     approx_mode
									a_     :      Optional[Any] 						=     initial_prior_first_n_blocks
									a_     :      List[str] 						=     initial_prior_diagonal_n_blocks