



'''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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				KandinskyInpaintPipeline
  __lowercase							:				Union[str, Any]           =				['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
  __lowercase							:				List[str]           =				[
      '''prompt''',
      '''negative_prompt''',
      '''image_embeds''',
      '''negative_image_embeds''',
      '''image''',
      '''mask_image''',
  ]
  __lowercase							:				Dict           =				[
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''negative_prompt''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  __lowercase							:				str           =				False




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




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      return 32




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      return self.time_input_dim




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      return self.time_input_dim * 4




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      return 100




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  Optional[int]											= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
      return tokenizer




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      torch.manual_seed(0				)
      lowerCAmelCase__			:  List[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  ,)

      lowerCAmelCase__			:  Any											= MultilingualCLIP(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= text_encoder.eval()

      return text_encoder




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

      lowerCAmelCase__			:  Any											= {
          """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,
      }

      lowerCAmelCase__			:  Optional[int]											= UNetaDConditionModel(**__UpperCAmelCase				)
      return model




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      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 							UpperCAmelCase_     (   self				)   ->		Dict:
      torch.manual_seed(0				)
      lowerCAmelCase__			:  Dict											= VQModel(**self.dummy_movq_kwargs				)
      return model




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  int											= self.dummy_text_encoder
      lowerCAmelCase__			:  str											= self.dummy_tokenizer
      lowerCAmelCase__			:  Union[str, Any]											= self.dummy_unet
      lowerCAmelCase__			:  str											= self.dummy_movq

      lowerCAmelCase__			:  Any											= DDIMScheduler(
          num_train_timesteps=1000  ,beta_schedule="""linear"""  ,beta_start=0.0_0_0_8_5  ,beta_end=0.0_1_2  ,clip_sample=__UpperCAmelCase  ,set_alpha_to_one=__UpperCAmelCase  ,steps_offset=1  ,prediction_type="""epsilon"""  ,thresholding=__UpperCAmelCase  ,)

      lowerCAmelCase__			:  Union[str, Any]											= {
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components




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

      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  List[Any]											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  str											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[str]											= {
          """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 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  str											= """cpu"""

      lowerCAmelCase__			:  Tuple											= self.get_dummy_components()

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

      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= pipe(**self.get_dummy_inputs(__UpperCAmelCase				)				)
      lowerCAmelCase__			:  Union[str, Any]											= output.images

      lowerCAmelCase__			:  Tuple											= pipe(
          **self.get_dummy_inputs(__UpperCAmelCase				)  ,return_dict=__UpperCAmelCase  ,)[0]

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

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

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

      lowerCAmelCase__			:  str											= np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]				)

      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 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  Any											= load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""				)

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

      lowerCAmelCase__			:  Optional[Any]											= """a hat"""

      lowerCAmelCase__			:  List[Any]											= KandinskyPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1-prior"""  ,torch_dtype=torch.floataa				)
      pipe_prior.to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= KandinskyInpaintPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1-inpaint"""  ,torch_dtype=torch.floataa				)
      lowerCAmelCase__			:  Dict											= pipeline.to(__UpperCAmelCase				)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase				)

      lowerCAmelCase__			:  List[str]											= torch.Generator(device="""cpu"""				).manual_seed(0				)
      lowerCAmelCase__				,       lowerCAmelCase__			:  int											= pipe_prior(
          __UpperCAmelCase  ,generator=__UpperCAmelCase  ,num_inference_steps=5  ,negative_prompt=""""""  ,).to_tuple()

      lowerCAmelCase__			:  Union[str, Any]											= 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"""  ,)

      lowerCAmelCase__			:  int											= output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase  ,__UpperCAmelCase				)







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations(UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item      ) for item in array      )

    return count_of_possible_combinations(UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase							,   UpperCamelCase      ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        lowerCAmelCase__			:  Any											= sum(
            count_of_possible_combinations_with_dp_array(target - item							,   UpperCamelCase      )
            for item in array      )
        lowerCAmelCase__			:  Tuple											= answer
        return answer

    lowerCAmelCase__			:  Optional[int]											= [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,   UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= [0] * (target + 1)
    lowerCAmelCase__			:  List[Any]											= 1

    for i in range(1							,   target + 1      ):
        for j in range(UpperCamelCase      ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase										=     3
 _lowerCAmelCase										=     5
 _lowerCAmelCase										=     [1, 2, 5]
 print(combination_sum_iv(n, array, target))


