



"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__		:				Any          =	logging.get_logger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ["""pixel_values"""]



      def __init__(      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=PILImageResampling.BILINEAR    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):
        lowerCAmelCase_   :				Tuple								= do_resize
        lowerCAmelCase_   :				Optional[int]								= do_rescale
        lowerCAmelCase_   :				Optional[int]								= size_divisor
        lowerCAmelCase_   :				Any								= resample
        super().__init__(**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      np.ndarray    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[ChannelDimension] = None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				Any								= get_image_size(SCREAMING_SNAKE_CASE_       )
        # Rounds the height and width down to the closest multiple of size_divisor
        lowerCAmelCase_   :				Union[str, Any]								= height // size_divisor * size_divisor
        lowerCAmelCase_   :				Optional[Any]								= width // size_divisor * size_divisor
        lowerCAmelCase_   :				str								= resize(SCREAMING_SNAKE_CASE_    ,			(new_h, new_w)    ,			resample=SCREAMING_SNAKE_CASE_    ,			data_format=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        return image



      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      np.ndarray    ,			SCREAMING_SNAKE_CASE_				:      float    ,			SCREAMING_SNAKE_CASE_				:      Optional[ChannelDimension] = None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        return rescale(image=SCREAMING_SNAKE_CASE_    ,			scale=SCREAMING_SNAKE_CASE_    ,			data_format=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[TensorType, str]] = None    ,			SCREAMING_SNAKE_CASE_				:      ChannelDimension = ChannelDimension.FIRST    ,			**SCREAMING_SNAKE_CASE_				:      Dict    ,			):
        lowerCAmelCase_   :				List[Any]								= do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase_   :				str								= do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase_   :				str								= size_divisor if size_divisor is not None else self.size_divisor
        lowerCAmelCase_   :				List[Any]								= resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
          raise ValueError('size_divisor is required for resizing'       )

        lowerCAmelCase_   :				Union[str, Any]								= make_list_of_images(SCREAMING_SNAKE_CASE_       )

        if not valid_images(SCREAMING_SNAKE_CASE_       ):
          raise ValueError('Invalid image(s)'       )

        # All transformations expect numpy arrays.
        lowerCAmelCase_   :				List[str]								= [to_numpy_array(SCREAMING_SNAKE_CASE_       ) for img in images]

        if do_resize:
          lowerCAmelCase_   :				List[Any]								= [self.resize(SCREAMING_SNAKE_CASE_    ,			size_divisor=SCREAMING_SNAKE_CASE_    ,			resample=SCREAMING_SNAKE_CASE_       ) for image in images]

        if do_rescale:
          lowerCAmelCase_   :				Union[str, Any]								= [self.rescale(SCREAMING_SNAKE_CASE_    ,			scale=1 / 2_5_5       ) for image in images]

        lowerCAmelCase_   :				Tuple								= [to_channel_dimension_format(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ) for image in images]

        lowerCAmelCase_   :				List[Any]								= {'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_    ,			tensor_type=SCREAMING_SNAKE_CASE_       )










"""simple docstring"""







import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.eager import context
     from tensorflow.python.framework import ops

     from transformers import GradientAccumulator, create_optimizer







@require_tf
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			len(SCREAMING_SNAKE_CASE_       )       )
        for a, b in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
          self.assertAlmostEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			delta=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				int								= GradientAccumulator()
        accumulator([tf.constant([1.0, 2.0]       )]       )
        accumulator([tf.constant([-2.0, 1.0]       )]       )
        accumulator([tf.constant([-1.0, 2.0]       )]       )
        with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
          accumulator([tf.constant([1.0, 1.0]       ), tf.constant([2.0, 2.0]       )]       )
        self.assertEqual(accumulator.step    ,			3       )
        self.assertEqual(len(accumulator.gradients       )    ,			1       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[-2.0, 5.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[0.0, 0.0]    ,			tol=1E-2       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= None
        ops.enable_eager_execution_internal()
        lowerCAmelCase_   :				str								= tf.config.list_physical_devices('CPU'       )
        if len(SCREAMING_SNAKE_CASE_       ) == 1:
          tf.config.set_logical_device_configuration(
              physical_devices[0]    ,			[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]       )
        lowerCAmelCase_   :				Dict								= tf.config.list_logical_devices(device_type='CPU'       )
        lowerCAmelCase_   :				Optional[int]								= tf.distribute.MirroredStrategy(devices=devices[:2]       )

        with strategy.scope():
          lowerCAmelCase_   :				Union[str, Any]								= GradientAccumulator()
          lowerCAmelCase_   :				int								= tf.Variable([4.0, 3.0]       )
          lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= create_optimizer(5E-5    ,			1_0    ,			5       )
          lowerCAmelCase_   :				Union[str, Any]								= tf.Variable([0.0, 0.0]    ,			trainable=SCREAMING_SNAKE_CASE_       )

        def accumulate_on_replica(SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          accumulator([gradient]       )

        def apply_on_replica():
          optimizer.apply_gradients(list(zip(accumulator.gradients    ,			[variable]       )       )       )

        @tf.function
        def accumulate(SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
          with strategy.scope():
            lowerCAmelCase_   :				Tuple								= strategy.experimental_local_results(SCREAMING_SNAKE_CASE_       )
            local_variables[0].assign(SCREAMING_SNAKE_CASE_       )
            local_variables[1].assign(SCREAMING_SNAKE_CASE_       )
            strategy.run(SCREAMING_SNAKE_CASE_    ,			args=(gradient_placeholder,)       )

        @tf.function
        def apply_grad():
          with strategy.scope():
            strategy.run(SCREAMING_SNAKE_CASE_       )

        def _check_local_values(SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          lowerCAmelCase_   :				List[Any]								= strategy.experimental_local_results(accumulator._gradients[0]       )
          self.assertListAlmostEqual(values[0].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )
          self.assertListAlmostEqual(values[1].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )

        accumulate([1.0, 2.0]    ,			[-1.0, 1.0]       )
        accumulate([3.0, -1.0]    ,			[-1.0, -1.0]       )
        accumulate([-2.0, 2.0]    ,			[3.0, -2.0]       )
        self.assertEqual(accumulator.step    ,			3       )
        _check_local_values([2.0, 3.0]    ,			[1.0, -2.0]       )
        apply_grad()
        self.assertListAlmostEqual(variable.value()    ,			[4.0, 3.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        _check_local_values([0.0, 0.0]    ,			[0.0, 0.0]       )





