



'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor

class     UpperCamelCase_				(							unittest.TestCase    ):





	def __init__(						self	,	A	,	A=7	,	A=3	,	A=18	,	A=30	,	A=400	,	A=True	,	A=None	,	A=True	,	A=None	,	A=True	,	)    ->    Tuple:
								UpperCAmelCase  :	Union[str, Any]							  =	size if size is not None else {"""shortest_edge""": 20}
								UpperCAmelCase  :	Tuple							  =	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								UpperCAmelCase  :	Dict							  =	parent
								UpperCAmelCase  :	List[str]							  =	batch_size
								UpperCAmelCase  :	Optional[int]							  =	num_channels
								UpperCAmelCase  :	Optional[int]							  =	image_size
								UpperCAmelCase  :	Any							  =	min_resolution
								UpperCAmelCase  :	Optional[int]							  =	max_resolution
								UpperCAmelCase  :	str							  =	do_resize
								UpperCAmelCase  :	str							  =	size
								UpperCAmelCase  :	int							  =	do_center_crop
								UpperCAmelCase  :	int							  =	crop_size
								UpperCAmelCase  :	List[str]							  =	do_flip_channel_order






	def 				_lowercase(						self					)    ->    Union[str, Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_flip_channel_order": self.do_flip_channel_order,
								}




@require_torch
@require_vision
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				MobileViTImageProcessor if is_vision_available() else None





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	MobileViTImageProcessingTester(self					)





	@property
	def 				_lowercase(						self					)    ->    Any:
								return self.image_processor_tester.prepare_image_processor_dict()





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(A	,	"""do_resize"""					)					)
								self.assertTrue(hasattr(A	,	"""size"""					)					)
								self.assertTrue(hasattr(A	,	"""do_center_crop"""					)					)
								self.assertTrue(hasattr(A	,	"""center_crop"""					)					)
								self.assertTrue(hasattr(A	,	"""do_flip_channel_order"""					)					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Optional[Any]							  =	self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size	,	{"""shortest_edge""": 20}					)
								self.assertEqual(image_processor.crop_size	,	{"""height""": 18, """width""": 18}					)

								UpperCAmelCase  :	Union[str, Any]							  =	self.image_processing_class.from_dict(self.image_processor_dict	,	size=42	,	crop_size=84					)
								self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}					)
								self.assertEqual(image_processor.crop_size	,	{"""height""": 84, """width""": 84}					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								pass





	def 				_lowercase(						self					)    ->    str:
								# Initialize image_processing
								UpperCAmelCase  :	List[str]							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								UpperCAmelCase  :	str							  =	prepare_image_inputs(self.image_processor_tester	,	equal_resolution=A					)
								for image in image_inputs:
															self.assertIsInstance(A	,	Image.Image					)

								# Test not batched input
								UpperCAmelCase  :	Any							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)

								# Test batched
								UpperCAmelCase  :	int							  =	image_processing(A	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)





	def 				_lowercase(						self					)    ->    int:
								# Initialize image_processing
								UpperCAmelCase  :	Any							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								UpperCAmelCase  :	Dict							  =	prepare_image_inputs(self.image_processor_tester	,	equal_resolution=A	,	numpify=A					)
								for image in image_inputs:
															self.assertIsInstance(A	,	np.ndarray					)

								# Test not batched input
								UpperCAmelCase  :	int							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)

								# Test batched
								UpperCAmelCase  :	List[str]							  =	image_processing(A	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)






	def 				_lowercase(						self					)    ->    List[Any]:
								# Initialize image_processing
								UpperCAmelCase  :	List[str]							  =	self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								UpperCAmelCase  :	int							  =	prepare_image_inputs(self.image_processor_tester	,	equal_resolution=A	,	torchify=A					)
								for image in image_inputs:
															self.assertIsInstance(A	,	torch.Tensor					)

								# Test not batched input
								UpperCAmelCase  :	str							  =	image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)

								# Test batched
								UpperCAmelCase  :	List[str]							  =	image_processing(A	,	return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape	,	(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )	,	)






'''simple docstring'''




# Lint as: python3
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

a     :    Any      						=					get_logger()

a     :    Optional[dict]      						=					None

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





	def __init__(						self	,	A=None	,	A=None	,	**A					)    ->    str:
								super().__init__(features=A					)
								import jax
								from jaxlib.xla_client import Device

								if isinstance(A	,	A					):
															raise ValueError(
															    f'''Expected {device} to be a `str` not {type(A					)}, 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`."""					)
								UpperCAmelCase  :	Optional[int]							  =	device if isinstance(A	,	A					) 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:
															UpperCAmelCase  :	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]					)}.'''					)
															UpperCAmelCase  :	List[Any]							  =	str(jax.devices()[0]					)
								UpperCAmelCase  :	Union[str, Any]							  =	jnp_array_kwargs





	@staticmethod
	def 				_lowercase(						)    ->    Dict[str, "jaxlib.xla_extension.Device"]:
								import jax

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





	def 				_lowercase(						self	,	A					)    ->    str:
								import jax
								import jax.numpy as jnp

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





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax
								import jax.numpy as jnp

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

								UpperCAmelCase  :	List[str]							  =	{}

								if isinstance(A	,	(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:
																						UpperCAmelCase  :	str							  =	{"""dtype""": jnp.intaa}
															else:
																						UpperCAmelCase  :	int							  =	{"""dtype""": jnp.intaa}
								elif isinstance(A	,	(np.number, np.ndarray)					) and np.issubdtype(value.dtype	,	np.floating					):
															UpperCAmelCase  :	Any							  =	{"""dtype""": jnp.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

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

        # 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:
															UpperCAmelCase  :	Dict							  =	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(A	,	**{**default_dtype, **self.jnp_array_kwargs}					)





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax

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

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





	def 				_lowercase(						self	,	A					)    ->    Dict:
								return map_nested(self._recursive_tensorize	,	A	,	map_list=A					)





	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Union[str, Any]							  =	self.numpy_arrow_extractor().extract_row(A					)
								UpperCAmelCase  :	Dict							  =	self.python_features_decoder.decode_row(A					)
								return self.recursive_tensorize(A					)





	def 				_lowercase(						self	,	A					)    ->    "jax.Array":
								UpperCAmelCase  :	int							  =	self.numpy_arrow_extractor().extract_column(A					)
								UpperCAmelCase  :	Optional[Any]							  =	self.python_features_decoder.decode_column(A	,	pa_table.column_names[0]					)
								UpperCAmelCase  :	Optional[int]							  =	self.recursive_tensorize(A					)
								UpperCAmelCase  :	Any							  =	self._consolidate(A					)
								return column






	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Optional[int]							  =	self.numpy_arrow_extractor().extract_batch(A					)
								UpperCAmelCase  :	List[str]							  =	self.python_features_decoder.decode_batch(A					)
								UpperCAmelCase  :	Union[str, Any]							  =	self.recursive_tensorize(A					)
								for column_name in batch:
															UpperCAmelCase  :	Optional[Any]							  =	self._consolidate(batch[column_name]					)
								return batch

