


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class _lowerCAmelCase    (				_a   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Union[str, Any]							  =  KandinskyImgaImgPipeline
   __UpperCAmelCase			:	List[Any]							  =  ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
   __UpperCAmelCase			:	str							  =  [
       """prompt""",
       """negative_prompt""",
       """image_embeds""",
       """negative_image_embeds""",
       """image""",
   ]
   __UpperCAmelCase			:	Optional[int]							  =  [
       """generator""",
       """height""",
       """width""",
       """strength""",
       """guidance_scale""",
       """negative_prompt""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   __UpperCAmelCase			:	List[str]							  =  False







   @property
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       return 32







   @property
   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       return 32







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       return self.time_input_dim







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       return self.time_input_dim * 4







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       return 100







   @property
   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
       return tokenizer







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				Any				 =   MCLIPConfig(
           numDims=self.cross_attention_dim						,     transformerDimensions=self.text_embedder_hidden_size						,     hidden_size=self.text_embedder_hidden_size						,     intermediate_size=37						,     num_attention_heads=4						,     num_hidden_layers=5						,     vocab_size=1005						,     )

       snake_case   :				List[Any]				 =   MultilingualCLIP(__lowerCamelCase						)
       snake_case   :				Union[str, Any]				 =   text_encoder.eval()

       return text_encoder







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       torch.manual_seed(0						)

       snake_case   :				Optional[int]				 =   {
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       snake_case   :				Union[str, Any]				 =   UNetaDConditionModel(**__lowerCamelCase						)
       return model







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   VQModel(**self.dummy_movq_kwargs						)
       return model







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self.dummy_text_encoder
       snake_case   :				List[str]				 =   self.dummy_tokenizer
       snake_case   :				str				 =   self.dummy_unet
       snake_case   :				Optional[int]				 =   self.dummy_movq

       snake_case   :				Tuple				 =   {
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.00085,
           """beta_end""": 0.012,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       snake_case   :				List[Any]				 =   DDIMScheduler(**__lowerCamelCase						)

       snake_case   :				int				 =   {
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__=0						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   floats_tensor((1, self.cross_attention_dim)						,     rng=random.Random(__lowerCamelCase						)						).to(__lowerCamelCase						)
       snake_case   :				Tuple				 =   floats_tensor((1, self.cross_attention_dim)						,     rng=random.Random(seed + 1						)						).to(__lowerCamelCase						)
       # create init_image
       snake_case   :				List[Any]				 =   floats_tensor((1, 3, 64, 64)						,     rng=random.Random(__lowerCamelCase						)						).to(__lowerCamelCase						)
       snake_case   :				Optional[int]				 =   image.cpu().permute(0						,     2						,     3						,     1						)[0]
       snake_case   :				str				 =   Image.fromarray(np.uinta(__lowerCamelCase						)						).convert("RGB"						).resize((256, 256)						)

       if str(__lowerCamelCase						).startswith("mps"						):
           snake_case   :				int				 =   torch.manual_seed(__lowerCamelCase						)
       else:
           snake_case   :				str				 =   torch.Generator(device=__lowerCamelCase						).manual_seed(__lowerCamelCase						)
       snake_case   :				int				 =   {
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs







   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   """cpu"""

       snake_case   :				Tuple				 =   self.get_dummy_components()

       snake_case   :				Union[str, Any]				 =   self.pipeline_class(**__lowerCamelCase						)
       snake_case   :				Any				 =   pipe.to(__lowerCamelCase						)

       pipe.set_progress_bar_config(disable=__lowerCamelCase						)

       snake_case   :				str				 =   pipe(**self.get_dummy_inputs(__lowerCamelCase						)						)
       snake_case   :				int				 =   output.images

       snake_case   :				List[Any]				 =   pipe(
           **self.get_dummy_inputs(__lowerCamelCase						)						,     return_dict=__lowerCamelCase						,     )[0]

       snake_case   :				Union[str, Any]				 =   image[0, -3:, -3:, -1]
       snake_case   :				int				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       snake_case   :				Union[str, Any]				 =   np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]						)
       assert (
           np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
       ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
       ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Any				 =   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_img2img_frog.npy"						)

       snake_case   :				List[Any]				 =   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
       snake_case   :				List[Any]				 =   """A red cartoon frog, 4k"""

       snake_case   :				int				 =   KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"						,     torch_dtype=torch.floataa						)
       pipe_prior.to(__lowerCamelCase						)

       snake_case   :				int				 =   KandinskyImgaImgPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1"						,     torch_dtype=torch.floataa						)
       snake_case   :				Tuple				 =   pipeline.to(__lowerCamelCase						)

       pipeline.set_progress_bar_config(disable=__lowerCamelCase						)

       snake_case   :				Union[str, Any]				 =   torch.Generator(device="cpu"						).manual_seed(0						)
       snake_case   :				Optional[Any]				 =   pipe_prior(
           __lowerCamelCase						,     generator=__lowerCamelCase						,     num_inference_steps=5						,     negative_prompt=""						,     ).to_tuple()

       snake_case   :				Any				 =   pipeline(
           __lowerCamelCase						,     image=__lowerCamelCase						,     image_embeds=__lowerCamelCase						,     negative_image_embeds=__lowerCamelCase						,     generator=__lowerCamelCase						,     num_inference_steps=100						,     height=768						,     width=768						,     strength=0.2						,     output_type="np"						,     )

       snake_case   :				Optional[int]				 =   output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__lowerCamelCase						,     __lowerCamelCase						)






"""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 _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       self.assertEqual(len(UpperCamelCase__						)						,     len(UpperCamelCase__						)						)
       for a, b in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertAlmostEqual(UpperCamelCase__						,     UpperCamelCase__						,     delta=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[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(UpperCamelCase__						):
           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 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   None
       ops.enable_eager_execution_internal()
       snake_case   :				str				 =   tf.config.list_physical_devices("CPU"						)
       if len(UpperCamelCase__						) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]						,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       snake_case   :				Optional[int]				 =   tf.config.list_logical_devices(device_type="CPU"						)
       snake_case   :				List[str]				 =   tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
           snake_case   :				int				 =   GradientAccumulator()
           snake_case   :				int				 =   tf.Variable([4.0, 3.0]						)
           snake_case ,snake_case   :				Any				 =   create_optimizer(5e-5						,     10						,     5						)
           snake_case   :				Tuple				 =   tf.Variable([0.0, 0.0]						,     trainable=UpperCamelCase__						)

       def accumulate_on_replica(UpperCamelCase__						):
           accumulator([gradient]						)

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

       @tf.function
       def accumulate(UpperCamelCase__						,     UpperCamelCase__						):
           with strategy.scope():
               snake_case   :				Union[str, Any]				 =   strategy.experimental_local_results(UpperCamelCase__						)
               local_variables[0].assign(UpperCamelCase__						)
               local_variables[1].assign(UpperCamelCase__						)
               strategy.run(UpperCamelCase__						,     args=(gradient_placeholder,)						)

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

       def _check_local_values(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				List[Any]				 =   strategy.experimental_local_results(accumulator._gradients[0]						)
           self.assertListAlmostEqual(values[0].value()						,     UpperCamelCase__						,     tol=1e-2						)
           self.assertListAlmostEqual(values[1].value()						,     UpperCamelCase__						,     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]						)





