




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE      :				int				      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      :				int				      ={
    """google/reformer-crime-and-punishment""": (
        """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"""
    ),
    """google/reformer-enwik8""": """https://huggingface.co/google/reformer-enwik8/resolve/main/config.json""",
}






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[Any]       ="reformer"
     _snake_case:		List[str]       =["past_buckets_states"]
     _snake_case:		List[Any]       ={}




     def __init__(      self					:				List[str]		,					A__					:				List[Any]=6_4		,					A__					:				Union[str, Any]=["local", "lsh", "local", "lsh", "local", "lsh"]		,					A__					:				str=1.0		,					A__					:				Union[str, Any]=True		,					A__					:				Union[str, Any]=[6_4, 6_4]		,					A__					:				Tuple=[6_4, 1_9_2]		,					A__					:				List[Any]=0		,					A__					:				int=2		,					A__					:				List[Any]=5_1_2		,					A__					:				Optional[Any]=None		,					A__					:				Any="relu"		,					A__					:				Union[str, Any]=0.05		,					A__					:				Optional[int]=2_5_6		,					A__					:				int=0.02		,					A__					:				Union[str, Any]=False		,					A__					:				Optional[Any]=1E-12		,					A__					:				Tuple=1		,					A__					:				Any=0		,					A__					:				Optional[Any]=0.05		,					A__					:				Optional[int]=6_4		,					A__					:				List[Any]=6_4		,					A__					:				List[Any]=0.0		,					A__					:				Dict=1		,					A__					:				Union[str, Any]=0		,					A__					:				Tuple=4_0_9_6		,					A__					:				Dict=1_2		,					A__					:				Optional[int]=None		,					A__					:				Union[str, Any]=1		,					A__					:				str=0		,					A__					:				List[str]=3_2_0		,					A__					:				List[str]=False		,					A__					:				List[Any]=True		,					A__					:				List[str]=None		,					**A__					:				int		,					)			->    Union[str, Any]:
           lowerCamelCase__ 					=			hash_seed
           lowerCamelCase__ 					=			vocab_size
           lowerCamelCase__ 					=			attention_head_size
           lowerCamelCase__ 					=			hidden_size
           lowerCamelCase__ 					=			num_attention_heads
           lowerCamelCase__ 					=			num_hashes
           lowerCamelCase__ 					=			len(A__     )
           lowerCamelCase__ 					=			tuple(A__     ) if isinstance(A__		,					A__     ) else num_buckets
           lowerCamelCase__ 					=			lsh_attn_chunk_length
           lowerCamelCase__ 					=			local_attn_chunk_length
           lowerCamelCase__ 					=			lsh_num_chunks_after
           lowerCamelCase__ 					=			lsh_num_chunks_before
           lowerCamelCase__ 					=			local_num_chunks_after
           lowerCamelCase__ 					=			local_num_chunks_before
           lowerCamelCase__ 					=			hidden_act
           lowerCamelCase__ 					=			feed_forward_size
           lowerCamelCase__ 					=			hidden_dropout_prob
           lowerCamelCase__ 					=			lsh_attention_probs_dropout_prob
           lowerCamelCase__ 					=			local_attention_probs_dropout_prob
           lowerCamelCase__ 					=			max_position_embeddings
           lowerCamelCase__ 					=			initializer_range
           lowerCamelCase__ 					=			layer_norm_eps
           lowerCamelCase__ 					=			axial_pos_embds
           lowerCamelCase__ 					=			tuple(A__     )
           lowerCamelCase__ 					=			tuple(A__     )
           lowerCamelCase__ 					=			axial_norm_std
           lowerCamelCase__ 					=			chunk_size_lm_head
           lowerCamelCase__ 					=			attn_layers
           lowerCamelCase__ 					=			use_cache
           lowerCamelCase__ 					=			classifier_dropout
           super().__init__(
               pad_token_id=A__		,					eos_token_id=A__		,					is_decoder=A__		,					tie_word_embeddings=A__		,					**A__		,					)





# 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 tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

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

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf 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					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch