




'''simple docstring'''




def   a_							(   __snake_case							:    list     )							->		bool:



       """simple docstring"""
       if not isinstance(__snake_case     , __snake_case     ):
              raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''     )
       if len(__snake_case     ) == 0:
              raise ValueError('''Input list must be a non empty list'''     )
       if len(__snake_case     ) == 1 and series[0] != 0:
              return True
       lowerCamelCase_							=[]
       lowerCamelCase_							=len(__snake_case     )
       for i in range(0     , __snake_case     ):
              if series[i] == 0:
                     raise ValueError('''Input series cannot have 0 as an element'''     )
              rec_series.append(1 / series[i]     )
       lowerCamelCase_							=rec_series[1] - rec_series[0]
       for index in range(2     , __snake_case     ):
              if rec_series[index] - rec_series[index - 1] != common_diff:
                     return False
       return True





def   a_							(   __snake_case							:    list     )							->		float:



       """simple docstring"""
       if not isinstance(__snake_case     , __snake_case     ):
              raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''     )
       if len(__snake_case     ) == 0:
              raise ValueError('''Input list must be a non empty list'''     )
       lowerCamelCase_							=0
       for val in series:
              answer += 1 / val
       return len(__snake_case     ) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Tuple   		=KandinskyPipeline
       lowercase			:					Optional[int]   		=[
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       lowercase			:					Union[str, Any]   		=['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       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			:					Dict   		=False






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim * 4






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 100






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
              return tokenizer






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=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,	)

              lowerCamelCase_							=MultilingualCLIP(lowerCAmelCase						)
              lowerCamelCase_							=text_encoder.eval()

              return text_encoder






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)

              lowerCamelCase_							={
                  '''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,
              }

              lowerCamelCase_							=UNetaDConditionModel(**lowerCAmelCase						)
              return model






       @property
       def   lowercase__  (							self						):



              """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   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=VQModel(**self.dummy_movq_kwargs						)
              return model






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=self.dummy_tokenizer
              lowerCamelCase_							=self.dummy_unet
              lowerCamelCase_							=self.dummy_movq

              lowerCamelCase_							=DDIMScheduler(
                  num_train_timesteps=1_000,	beta_schedule='''linear''',	beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	clip_sample=lowerCAmelCase,	set_alpha_to_one=lowerCAmelCase,	steps_offset=1,	prediction_type='''epsilon''',	thresholding=lowerCAmelCase,	)

              lowerCamelCase_							={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=0						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(seed + 1						)						).to(lowerCAmelCase						)
              if str(lowerCAmelCase						).startswith('''mps'''						):
                     lowerCamelCase_							=torch.manual_seed(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
              lowerCamelCase_							={
                  '''prompt''': '''horse''',
                  '''image_embeds''': image_embeds,
                  '''negative_image_embeds''': negative_image_embeds,
                  '''generator''': generator,
                  '''height''': 64,
                  '''width''': 64,
                  '''guidance_scale''': 4.0,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''np''',
              }
              return inputs






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''

              lowerCamelCase_							=self.get_dummy_components()

              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase						)
              lowerCamelCase_							=pipe.to(lowerCAmelCase						)

              pipe.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							=pipe(**self.get_dummy_inputs(lowerCAmelCase						)						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=pipe(
                  **self.get_dummy_inputs(lowerCAmelCase						),	return_dict=lowerCAmelCase,	)[0]

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

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

              lowerCamelCase_							=np.array(
                  [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

              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 						__UpperCamelCase (				unittest.TestCase       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''						)

              lowerCamelCase_							=KandinskyPriorPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-1-prior''',	torch_dtype=torch.floataa						)
              pipe_prior.to(lowerCAmelCase						)

              lowerCamelCase_							=KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipeline.to(lowerCAmelCase						)
              pipeline.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							='''red cat, 4k photo'''

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_,   lowerCamelCase_							=pipe_prior(
                  lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=5,	negative_prompt='''''',	).to_tuple()

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_							=pipeline(
                  lowerCAmelCase,	image_embeds=lowerCAmelCase,	negative_image_embeds=lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=100,	output_type='''np''',	)

              lowerCamelCase_							=output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(lowerCAmelCase,	lowerCAmelCase						)




