


'''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, KandinskyInpaintPipeline, 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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				KandinskyInpaintPipeline
     UpperCAmelCase 					=				['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
     UpperCAmelCase 					=				[
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
         '''mask_image''',
     ]
     UpperCAmelCase 					=				[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     UpperCAmelCase 					=				False






     @property
     def  a_					(			self		)       ->    List[Any]:
          return 32






     @property
     def  a_					(			self		)       ->    List[str]:
          return 32






     @property
     def  a_					(			self		)       ->    Any:
          return self.time_input_dim






     @property
     def  a_					(			self		)       ->    List[str]:
          return self.time_input_dim * 4






     @property
     def  a_					(			self		)       ->    int:
          return 100






     @property
     def  a_					(			self		)       ->    List[str]:
          _a						=		XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
          return tokenizer






     @property
     def  a_					(			self		)       ->    Dict:
          torch.manual_seed(0		)
          _a						=		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=1_005     ,							)

          _a						=		MultilingualCLIP(__UpperCamelCase		)
          _a						=		text_encoder.eval()

          return text_encoder






     @property
     def  a_					(			self		)       ->    Union[str, Any]:
          torch.manual_seed(0		)

          _a						=		{
              "in_channels": 9,
              # 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,
          }

          _a						=		UNetaDConditionModel(**__UpperCamelCase		)
          return model






     @property
     def  a_					(			self		)       ->    Union[str, Any]:
          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  a_					(			self		)       ->    Any:
          torch.manual_seed(0		)
          _a						=		VQModel(**self.dummy_movq_kwargs		)
          return model






     def  a_					(			self		)       ->    Dict:
          _a						=		self.dummy_text_encoder
          _a						=		self.dummy_tokenizer
          _a						=		self.dummy_unet
          _a						=		self.dummy_movq

          _a						=		DDIMScheduler(
              num_train_timesteps=1_000     ,							beta_schedule="linear"     ,							beta_start=0.0_00_85     ,							beta_end=0.0_12     ,							clip_sample=__UpperCamelCase     ,							set_alpha_to_one=__UpperCamelCase     ,							steps_offset=1     ,							prediction_type="epsilon"     ,							thresholding=__UpperCamelCase     ,							)

          _a						=		{
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=0		)       ->    Optional[int]:
          _a						=		floats_tensor((1, self.cross_attention_dim)     ,							rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
          _a						=		floats_tensor((1, self.cross_attention_dim)     ,							rng=random.Random(seed + 1		)		).to(__UpperCamelCase		)
          # create init_image
          _a						=		floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
          _a						=		image.cpu().permute(0     ,							2     ,							3     ,							1		)[0]
          _a						=		Image.fromarray(np.uinta(__UpperCamelCase		)		).convert("RGB"		).resize((256, 256)		)
          # create mask
          _a						=		np.ones((64, 64)     ,							dtype=np.floataa		)
          _a						=		0

          if str(__UpperCamelCase		).startswith("mps"		):
               _a						=		torch.manual_seed(__UpperCamelCase		)
          else:
               _a						=		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a						=		{
              "prompt": "horse",
              "image": init_image,
              "mask_image": mask,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "generator": generator,
              "height": 64,
              "width": 64,
              "num_inference_steps": 2,
              "guidance_scale": 4.0,
              "output_type": "np",
          }
          return inputs






     def  a_					(			self		)       ->    int:
          _a						=		"cpu"

          _a						=		self.get_dummy_components()

          _a						=		self.pipeline_class(**__UpperCamelCase		)
          _a						=		pipe.to(__UpperCamelCase		)

          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		pipe(**self.get_dummy_inputs(__UpperCamelCase		)		)
          _a						=		output.images

          _a						=		pipe(
              **self.get_dummy_inputs(__UpperCamelCase		)     ,							return_dict=__UpperCamelCase     ,							)[0]

          _a						=		image[0, -3:, -3:, -1]
          _a						=		image_from_tuple[0, -3:, -3:, -1]

          print(f"image.shape {image.shape}"		)

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

          _a						=		np.array(
              [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]		)

          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()}"






     def  a_					(			self		)       ->    Tuple:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







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






     def  a_					(			self		)       ->    Any:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"		)

          _a						=		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
          _a						=		np.ones((768, 768)     ,							dtype=np.floataa		)
          _a						=		0

          _a						=		"a hat"

          _a						=		KandinskyPriorPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-prior"     ,							torch_dtype=torch.floataa		)
          pipe_prior.to(__UpperCamelCase		)

          _a						=		KandinskyInpaintPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-inpaint"     ,							torch_dtype=torch.floataa		)
          _a						=		pipeline.to(__UpperCamelCase		)
          pipeline.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		torch.Generator(device="cpu"		).manual_seed(0		)
          _a			,			_a						=		pipe_prior(
              __UpperCamelCase     ,							generator=__UpperCamelCase     ,							num_inference_steps=5     ,							negative_prompt=""     ,							).to_tuple()

          _a						=		pipeline(
              __UpperCamelCase     ,							image=__UpperCamelCase     ,							mask_image=__UpperCamelCase     ,							image_embeds=__UpperCamelCase     ,							negative_image_embeds=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							num_inference_steps=100     ,							height=768     ,							width=768     ,							output_type="np"     ,							)

          _a						=		output.images[0]

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

          assert_mean_pixel_difference(__UpperCamelCase     ,							__UpperCamelCase		)





'''simple docstring'''

import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowercase__				     =		object()

# For specifying empty leaf dict `{}`
lowercase__				     =		object()






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Tuple				)      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		tuple((re.compile(x + "$"				) for x in qs)				)
     for i in range(len(__lowerCamelCase				) - len(__lowerCamelCase				) + 1				):
          _a						=		[x.match(__lowerCamelCase				) for x, y in zip(__lowerCamelCase      ,  ks[i:]				)]
          if matches and all(__lowerCamelCase				):
               return True
     return False






def __UpperCamelCase   (			__lowerCamelCase		: int				)      ->     Union[str, Any]:







     '''simple docstring'''






     def replace(__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Any				):
          for rule, replacement in rules:
               if _match(__lowerCamelCase      ,  __lowerCamelCase				):
                    return replacement
          return val

     return replace






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P("mp"      ,  __lowerCamelCase				)),
         (("transformer", "wte", "embedding"), P("mp"      ,  __lowerCamelCase				)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCamelCase      ,  "mp"				)),
         (("attention", "out_proj", "kernel"), P("mp"      ,  __lowerCamelCase				)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(__lowerCamelCase      ,  "mp"				)),
         (("mlp", "c_fc", "bias"), P("mp"				)),
         (("mlp", "c_proj", "kernel"), P("mp"      ,  __lowerCamelCase				)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]






def __UpperCamelCase   (			__lowerCamelCase		: Dict				)      ->     Tuple:







     '''simple docstring'''






     _a						=		_get_partition_rules()
     _a						=		_replacement_rules(__lowerCamelCase				)
     _a						=		{k: _unmatched for k in flatten_dict(__lowerCamelCase				)}
     _a						=		{k: replace(__lowerCamelCase      ,  __lowerCamelCase				) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(__lowerCamelCase				)				)

