



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




				def __init__(							self		,       _snake_case=None		,       **_snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       _snake_case	):



									"""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 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





									import tensorflow as tf

									if value is None:
														return value

									_lowerCAmelCase      			=					{}

									if isinstance(_snake_case		,       (np.number, np.ndarray)	) and np.issubdtype(value.dtype		,       np.integer	):
														_lowerCAmelCase      			=					{"""dtype""": tf.intaa}
									elif isinstance(_snake_case		,       (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(_snake_case		,       PIL.Image.Image	):
																			_lowerCAmelCase      			=					np.asarray(_snake_case	)

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




				def 							snake_case     (							self		,       _snake_case	):



									"""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	):
														_lowerCAmelCase      			=					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 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





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





# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__			   = abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(snake_case    )


def 	_UpperCAmelCase       (   snake_case    ):





					"""simple docstring"""

					from transformers.testing_utils import pytest_terminal_summary_main

					_lowerCAmelCase      			=					terminalreporter.config.getoption("""--make-reports"""    )
					if make_reports:
										pytest_terminal_summary_main(snake_case			,		id=snake_case    )



