




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
class 				__snake_case     (TensorFormatter[Mapping, np.ndarray, Mapping]						):



				def __init__(					self					:						List[str]						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Dict					)     ->			Optional[int]:





								'''simple docstring'''




								super().__init__(features=_UpperCAmelCase					)
								_lowerCAmelCase			:			Any        =					np_array_kwargs



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						Tuple					)     ->			Tuple:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					):
												if column and all(
												    isinstance(_UpperCAmelCase						,		np.ndarray					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
																return np.stack(_UpperCAmelCase					)
												else:
																# don't use np.array(column, dtype=object)
																# since it fails in certain cases
																# see https://stackoverflow.com/q/51005699
																_lowerCAmelCase			:			List[Any]        =					np.empty(len(_UpperCAmelCase					)						,		dtype=_UpperCAmelCase					)
																_lowerCAmelCase			:			List[Any]        =					column
																return out
								return column



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple						,		_UpperCAmelCase					:						Optional[int]					)     ->			Union[str, Any]:





								'''simple docstring'''




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

								_lowerCAmelCase			:			List[str]        =					{}

								if isinstance(_UpperCAmelCase						,		np.ndarray					) and np.issubdtype(value.dtype						,		np.integer					):
												_lowerCAmelCase			:			Union[str, Any]        =					{"""dtype""": np.intaa}
								elif isinstance(_UpperCAmelCase						,		np.ndarray					) and np.issubdtype(value.dtype						,		np.floating					):
												_lowerCAmelCase			:			Tuple        =					{"""dtype""": np.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_UpperCAmelCase						,		PIL.Image.Image					):
																return np.asarray(_UpperCAmelCase						,		**self.np_array_kwargs					)

								return np.asarray(_UpperCAmelCase						,		**{**default_dtype, **self.np_array_kwargs}					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str						,		_UpperCAmelCase					:						Dict					)     ->			Optional[Any]:





								'''simple docstring'''




								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_UpperCAmelCase						,		torch.Tensor					):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]					)
								if hasattr(_UpperCAmelCase						,		"""__array__"""					) and not isinstance(_UpperCAmelCase						,		(np.ndarray, np.character, np.number)					):
												_lowerCAmelCase			:			str        =					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_UpperCAmelCase						,		np.ndarray					):
												if data_struct.dtype == object:
																return self._consolidate([self.recursive_tensorize(_UpperCAmelCase					) for substruct in data_struct]					)
								if isinstance(_UpperCAmelCase						,		(list, tuple)					):
												return self._consolidate([self.recursive_tensorize(_UpperCAmelCase					) for substruct in data_struct]					)
								return self._tensorize(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						dict					)     ->			List[str]:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						pa.Table					)     ->			Mapping:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					self.numpy_arrow_extractor().extract_row(_UpperCAmelCase					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.python_features_decoder.decode_row(_UpperCAmelCase					)
								return self.recursive_tensorize(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						pa.Table					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					self.numpy_arrow_extractor().extract_column(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					self.python_features_decoder.decode_column(_UpperCAmelCase						,		pa_table.column_names[0]					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.recursive_tensorize(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					self._consolidate(_UpperCAmelCase					)
								return column



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						pa.Table					)     ->			Mapping:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					self.python_features_decoder.decode_batch(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					self.recursive_tensorize(_UpperCAmelCase					)
								for column_name in batch:
												_lowerCAmelCase			:			Any        =					self._consolidate(batch[column_name]					)
								return batch




"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





