
import torch
from torch import autograd







class     __A(				autograd.Function    ):




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					inputs.numel()
								__a    							=					int(0.005 * nb_elems       ) + 1
								if sigmoid:
												__a    							=					(torch.sigmoid(SCREAMING_SNAKE_CASE_       ) > threshold).type(inputs.type()       )
								else:
												__a    							=					(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
												__a    							=					inputs.flatten().kthvalue(max(nb_elems - nb_min	,  1       )       ).values
												__a    							=					(inputs > k_threshold).type(inputs.type()       )
								return mask




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								return gradOutput, None, None







class     __A(				autograd.Function    ):




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case	,  _snake_case	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					inputs.clone()
								__a							,  __a    							=					inputs.flatten().sort(descending=SCREAMING_SNAKE_CASE_       )
								__a    							=					int(threshold * inputs.numel()       )

								# flat_out and mask access the same memory.
								__a    							=					mask.flatten()
								__a    							=					0
								__a    							=					1
								return mask




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case	,  _snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								return gradOutput, None







class     __A(				lowerCamelCase__    ):




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case	,  _snake_case       )					->   int:


								'''simple docstring'''




								__a    							=					inputs.clone()
								__a							,  __a    							=					inputs.abs().flatten().sort(descending=SCREAMING_SNAKE_CASE_       )
								__a    							=					int(threshold * inputs.numel()       )

								# flat_out and mask access the same memory.
								__a    							=					mask.flatten()
								__a    							=					0
								__a    							=					1
								return mask








# 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]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

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

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

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

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''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(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_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(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




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