





# 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 				_UpperCamelCase       (						TensorFormatter[Mapping, '''tf.Tensor''', Mapping]  ):

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			str=None       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			int:




              """simple docstring"""


              super().__init__(features=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tf_tensor_kwargs
              import tensorflow as tf  # noqa: import tf at initialization

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			int		)							->			str:




              """simple docstring"""


              import tensorflow as tf

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

              return column

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			Any:




              """simple docstring"""


              import tensorflow as tf

              if value is None:
                     return value

              UpperCamelCase_ 							=	{}

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

                     if isinstance(_SCREAMING_SNAKE_CASE       , PIL.Image.Image		):
                            UpperCamelCase_ 							=	np.asarray(_SCREAMING_SNAKE_CASE		)

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

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any		)							->			Optional[int]:




              """simple docstring"""


              import tensorflow as tf

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

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

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			dict		)							->			Tuple:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			pa.Table		)							->			Mapping:




              """simple docstring"""


              UpperCamelCase_ 							=	self.numpy_arrow_extractor().extract_row(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.python_features_decoder.decode_row(_SCREAMING_SNAKE_CASE		)
              return self.recursive_tensorize(_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			pa.Table		)							->			"tf.Tensor":




              """simple docstring"""


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



       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			pa.Table		)							->			Mapping:




              """simple docstring"""


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





'''simple docstring'''


import torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



