

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       __A (							unittest.TestCase       ):






    def 		lowercase__       (		self	:			List[str]			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			Any			):
        self.assertEqual(len(lowerCAmelCase__			)			, len(lowerCAmelCase__			)			)
        for a, b in zip(lowerCAmelCase__			, lowerCAmelCase__			):
            self.assertAlmostEqual(lowerCAmelCase__			, lowerCAmelCase__			, delta=lowerCAmelCase__			)






    def 		lowercase__       (		self	:			int			):
        lowerCAmelCase :	Optional[Any]				=					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(lowerCAmelCase__			):
            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 		lowercase__       (		self	:			str			):
        lowerCAmelCase :	Any				=					None
        ops.enable_eager_execution_internal()
        lowerCAmelCase :	int				=					tf.config.list_physical_devices('CPU'			)
        if len(lowerCAmelCase__			) == 1:
            tf.config.set_logical_device_configuration(
                physical_devices[0]			, [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]			)
        lowerCAmelCase :	Optional[int]				=					tf.config.list_logical_devices(device_type='CPU'			)
        lowerCAmelCase :	List[str]				=					tf.distribute.MirroredStrategy(devices=devices[:2]			)

        with strategy.scope():
            lowerCAmelCase :	int				=					GradientAccumulator()
            lowerCAmelCase :	Optional[Any]				=					tf.Variable([4.0, 3.0]			)
            lowerCAmelCase :	Dict				=					create_optimizer(5E-5			, 10			, 5			)
            lowerCAmelCase :	Optional[int]				=					tf.Variable([0.0, 0.0]			, trainable=lowerCAmelCase__			)

        def accumulate_on_replica(UpperCAmelCase_	:			str			):
            accumulator([gradient]			)

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

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

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

        def _check_local_values(UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			Union[str, Any]			):
            lowerCAmelCase :	Tuple				=					strategy.experimental_local_results(accumulator._gradients[0]			)
            self.assertListAlmostEqual(values[0].value()			, lowerCAmelCase__			, tol=1E-2			)
            self.assertListAlmostEqual(values[1].value()			, lowerCAmelCase__			, 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]			)












import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







@nightly
@require_torch_gpu
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCAmelCase__							)
          a__	:					str										=VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Optional[int]										=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1






