


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__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     str        =						KandinskyInpaintPipeline
      snake_case__				:     Dict        =						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      snake_case__				:     Union[str, Any]        =						[
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      snake_case__				:     str        =						[
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      snake_case__				:     List[Any]        =						False
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             return 3_2
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             return 3_2
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Any:
             return self.time_input_dim
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[Any]:
             return self.time_input_dim * 4
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			List[Any]:
             return 1_0_0
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			List[Any]:
             a_					:		Optional[Any]							=  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'     )
             return tokenizer
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Optional[Any]:
             torch.manual_seed(0     )
             a_					:		Optional[Any]							=  MCLIPConfig(
                 numDims=self.cross_attention_dim     ,							transformerDimensions=self.text_embedder_hidden_size     ,							hidden_size=self.text_embedder_hidden_size     ,							intermediate_size=3_7     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							vocab_size=1_0_0_5     ,							)

             a_					:		Optional[int]							=  MultilingualCLIP(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  text_encoder.eval()

             return text_encoder
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Dict:
             torch.manual_seed(0     )

             a_					:		str							=  {
                 '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_					:		Optional[int]							=  UNetaDConditionModel(**SCREAMING_SNAKE_CASE__     )
             return model
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             return {
                 "block_out_channels": [3_2, 6_4],
                 "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": 1_2,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Tuple:
             torch.manual_seed(0     )
             a_					:		Union[str, Any]							=  VQModel(**self.dummy_movq_kwargs     )
             return model
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             a_					:		Tuple							=  self.dummy_text_encoder
             a_					:		Union[str, Any]							=  self.dummy_tokenizer
             a_					:		List[Any]							=  self.dummy_unet
             a_					:		Optional[Any]							=  self.dummy_movq

             a_					:		Any							=  DDIMScheduler(
                 num_train_timesteps=1_0_0_0     ,							beta_schedule='linear'     ,							beta_start=0.00085     ,							beta_end=0.012     ,							clip_sample=SCREAMING_SNAKE_CASE__     ,							set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,							steps_offset=1     ,							prediction_type='epsilon'     ,							thresholding=SCREAMING_SNAKE_CASE__     ,							)

             a_					:		List[Any]							=  {
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'unet': unet,
                 'scheduler': scheduler,
                 'movq': movq,
             }

             return components
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Any=0     )		->			Dict:
             a_					:		Tuple							=  floats_tensor((1, self.cross_attention_dim)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     ).to(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  floats_tensor((1, self.cross_attention_dim)     ,							rng=random.Random(seed + 1     )     ).to(SCREAMING_SNAKE_CASE__     )
             # create init_image
             a_					:		int							=  floats_tensor((1, 3, 6_4, 6_4)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     ).to(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  image.cpu().permute(0     ,							2     ,							3     ,							1     )[0]
             a_					:		Union[str, Any]							=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__     )     ).convert('RGB'     ).resize((2_5_6, 2_5_6)     )
             # create mask
             a_					:		Optional[int]							=  np.ones((6_4, 6_4)     ,							dtype=np.floataa     )
             a_					:		Dict							=  0

             if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                    a_					:		Optional[int]							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		int							=  torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  {
                 'prompt': 'horse',
                 'image': init_image,
                 'mask_image': mask,
                 'image_embeds': image_embeds,
                 'negative_image_embeds': negative_image_embeds,
                 'generator': generator,
                 'height': 6_4,
                 'width': 6_4,
                 'num_inference_steps': 2,
                 'guidance_scale': 4.0,
                 'output_type': 'np',
             }
             return inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_					:		Optional[Any]							=  'cpu'

             a_					:		List[Any]							=  self.get_dummy_components()

             a_					:		List[str]							=  self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  pipe.to(SCREAMING_SNAKE_CASE__     )

             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Tuple							=  output.images

             a_					:		Optional[int]							=  pipe(
                 **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )     ,							return_dict=SCREAMING_SNAKE_CASE__     ,							)[0]

             a_					:		List[str]							=  image[0, -3:, -3:, -1]
             a_					:		List[Any]							=  image_from_tuple[0, -3:, -3:, -1]

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

             assert image.shape == (1, 6_4, 6_4, 3)

             a_					:		List[str]							=  np.array(
                 [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]     )

             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        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             super().test_inference_batch_single_identical(expected_max_diff=3E-3     )
@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Any:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

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

             a_					:		str							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'     )
             a_					:		Union[str, Any]							=  np.ones((7_6_8, 7_6_8)     ,							dtype=np.floataa     )
             a_					:		Tuple							=  0

             a_					:		Union[str, Any]							=  'a hat'

             a_					:		List[str]							=  KandinskyPriorPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-1-prior'     ,							torch_dtype=torch.floataa     )
             pipe_prior.to(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  KandinskyInpaintPipeline.from_pretrained(
                 'kandinsky-community/kandinsky-2-1-inpaint'     ,							torch_dtype=torch.floataa     )
             a_					:		Union[str, Any]							=  pipeline.to(SCREAMING_SNAKE_CASE__     )
             pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  torch.Generator(device='cpu'     ).manual_seed(0     )
             a_							,		a_					:		List[str]							=  pipe_prior(
                 SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							num_inference_steps=5     ,							negative_prompt=''     ,							).to_tuple()

             a_					:		str							=  pipeline(
                 SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							mask_image=SCREAMING_SNAKE_CASE__     ,							image_embeds=SCREAMING_SNAKE_CASE__     ,							negative_image_embeds=SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							num_inference_steps=1_0_0     ,							height=7_6_8     ,							width=7_6_8     ,							output_type='np'     ,							)

             a_					:		List[str]							=  output.images[0]

             assert image.shape == (7_6_8, 7_6_8, 3)

             assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )






import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


