
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import jax
					import jaxlib

UpperCamelCase_						=						get_logger()

UpperCamelCase_						=						None

class      _SCREAMING_SNAKE_CASE				(		TensorFormatter[Mapping, 'jax.Array', Mapping]				):





				def __init__(		self			:		Dict       ,   snake_case_			:		Dict=None       ,   snake_case_			:		Dict=None       ,   **snake_case_			:		Any				):


								"""simple docstring"""

								super().__init__(features=__lowerCAmelCase				)
								import jax
								from jaxlib.xla_client import Device

								if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase				):
												raise ValueError(
												    f"""Expected {device} to be a `str` not {type(__lowerCAmelCase				)}, as `jaxlib.xla_extension.Device` """
												    '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
												    '''the device with `str()` to get its string identifier that will be internally mapped '''
												    '''to the actual `jaxlib.xla_extension.Device`.'''				)
								A  :	List[Any]				=					device if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase				) else str(jax.devices()[0]				)
								# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
								# with `pickle` nor with `dill`, so we need to use a global variable instead
								global DEVICE_MAPPING
								if DEVICE_MAPPING is None:
												A  :	Optional[Any]				=					self._map_devices_to_str()
								if self.device not in list(DEVICE_MAPPING.keys()				):
												logger.warning(
												    f"""Device with string identifier {self.device} not listed among the available """
												    f"""devices: {list(DEVICE_MAPPING.keys()				)}, so falling back to the default """
												    f"""device: {str(jax.devices()[0]				)}."""				)
												A  :	Union[str, Any]				=					str(jax.devices()[0]				)
								A  :	Any				=					jnp_array_kwargs





				@staticmethod
				def 							_UpperCAmelCase    (		):


								"""simple docstring"""

								import jax

								return {str(__lowerCAmelCase				): device for device in jax.devices()}





				def 							_UpperCAmelCase    (		self			:		Dict       ,   snake_case_			:		int				):


								"""simple docstring"""

								import jax
								import jax.numpy as jnp

								if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase				) and column:
												if all(
												    isinstance(__lowerCAmelCase       ,   jax.Array				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
																return jnp.stack(__lowerCAmelCase       ,   axis=0				)
								return column





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]       ,   snake_case_			:		Optional[int]				):


								"""simple docstring"""

								import jax
								import jax.numpy as jnp

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

								A  :	Union[str, Any]				=					{}

								if isinstance(__lowerCAmelCase       ,   (np.number, np.ndarray)				) and np.issubdtype(value.dtype       ,   np.integer				):
												# the default int precision depends on the jax config
												# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
												if jax.config.jax_enable_xaa:
																A  :	List[Any]				=					{'''dtype''': jnp.intaa}
												else:
																A  :	Optional[Any]				=					{'''dtype''': jnp.intaa}
								elif isinstance(__lowerCAmelCase       ,   (np.number, np.ndarray)				) and np.issubdtype(value.dtype       ,   np.floating				):
												A  :	Union[str, Any]				=					{'''dtype''': jnp.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(__lowerCAmelCase       ,   PIL.Image.Image				):
																A  :	List[str]				=					np.asarray(__lowerCAmelCase				)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
								global DEVICE_MAPPING
								if DEVICE_MAPPING is None:
												A  :	Optional[int]				=					self._map_devices_to_str()

								with jax.default_device(DEVICE_MAPPING[self.device]				):
												# calling jnp.array on a np.ndarray does copy the data
												# see https://github.com/google/jax/issues/4486
												return jnp.array(__lowerCAmelCase       ,   **{**default_dtype, **self.jnp_array_kwargs}				)





				def 							_UpperCAmelCase    (		self			:		Dict       ,   snake_case_			:		Optional[int]				):


								"""simple docstring"""

								import jax

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

												if isinstance(__lowerCAmelCase       ,   torch.Tensor				):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
								if hasattr(__lowerCAmelCase       ,   '''__array__'''				) and not isinstance(__lowerCAmelCase       ,   jax.Array				):
												A  :	Optional[Any]				=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(__lowerCAmelCase       ,   np.ndarray				):
												if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(__lowerCAmelCase				) for substruct in data_struct]				)
								elif isinstance(__lowerCAmelCase       ,   (list, tuple)				):
												return self._consolidate([self.recursive_tensorize(__lowerCAmelCase				) for substruct in data_struct]				)
								return self._tensorize(__lowerCAmelCase				)





				def 							_UpperCAmelCase    (		self			:		Dict       ,   snake_case_			:		dict				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		pa.Table				):


								"""simple docstring"""

								A  :	Optional[Any]				=					self.numpy_arrow_extractor().extract_row(__lowerCAmelCase				)
								A  :	Optional[Any]				=					self.python_features_decoder.decode_row(__lowerCAmelCase				)
								return self.recursive_tensorize(__lowerCAmelCase				)





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]       ,   snake_case_			:		pa.Table				):


								"""simple docstring"""

								A  :	List[str]				=					self.numpy_arrow_extractor().extract_column(__lowerCAmelCase				)
								A  :	Optional[int]				=					self.python_features_decoder.decode_column(__lowerCAmelCase       ,   pa_table.column_names[0]				)
								A  :	List[Any]				=					self.recursive_tensorize(__lowerCAmelCase				)
								A  :	List[str]				=					self._consolidate(__lowerCAmelCase				)
								return column






				def 							_UpperCAmelCase    (		self			:		Optional[Any]       ,   snake_case_			:		pa.Table				):


								"""simple docstring"""

								A  :	str				=					self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase				)
								A  :	List[str]				=					self.python_features_decoder.decode_batch(__lowerCAmelCase				)
								A  :	Union[str, Any]				=					self.recursive_tensorize(__lowerCAmelCase				)
								for column_name in batch:
												A  :	str				=					self._consolidate(batch[column_name]				)
								return batch





'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Tuple       ,		*__lowerCAmelCase     :  List[str]       ,		**__lowerCAmelCase     :  List[str]      ):
							warnings.warn(
							    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use VideoMAEImageProcessor instead.'       ,		__lowerCAmelCase       ,		)
							super().__init__(*__lowerCAmelCase       ,		**__lowerCAmelCase      )

