
import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 							A__		(     __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
      _UpperCAmelCase    :   Optional[int]   	=      KandinskyVaaPriorEmbaEmbPipeline
      _UpperCAmelCase    :   Tuple   	=      ["""prompt""", """image"""]
      _UpperCAmelCase    :   Dict   	=      ["""prompt""", """image"""]
      _UpperCAmelCase    :   Optional[int]   	=      [
          """num_images_per_prompt""",
          """strength""",
          """generator""",
          """num_inference_steps""",
          """latents""",
          """negative_prompt""",
          """guidance_scale""",
          """output_type""",
          """return_dict""",
      ]
      _UpperCAmelCase    :   Optional[Any]   	=      False
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim * 4
      @property
      def 						UpperCamelCase__							(				self				):
       return 1_0_0
      @property
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)
       return tokenizer
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Optional[int]          =				CLIPTextConfig(
           bos_token_id=0   ,  eos_token_id=2   ,  hidden_size=self.text_embedder_hidden_size   ,  projection_dim=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  layer_norm_eps=1e-05   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  pad_token_id=1   ,  vocab_size=1_0_0_0   ,  )
       return CLIPTextModelWithProjection(__magic_name__				)
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)

       lowerCamelCase		:     int          =				{
           """num_attention_heads""": 2,
           """attention_head_dim""": 1_2,
           """embedding_dim""": self.text_embedder_hidden_size,
           """num_layers""": 1,
       }

       lowerCamelCase		:     Optional[int]          =				PriorTransformer(**__magic_name__				)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowerCamelCase		:     Optional[Any]          =				nn.Parameter(torch.ones(model.clip_std.shape				)				)
       return model
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Optional[Any]          =				CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size   ,  image_size=2_2_4   ,  projection_dim=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  num_attention_heads=4   ,  num_channels=3   ,  num_hidden_layers=5   ,  patch_size=1_4   ,  )

       lowerCamelCase		:     Any          =				CLIPVisionModelWithProjection(__magic_name__				)
       return model
      @property
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				CLIPImageProcessor(
           crop_size=2_2_4   ,  do_center_crop=__magic_name__   ,  do_normalize=__magic_name__   ,  do_resize=__magic_name__   ,  image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,  image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,  resample=3   ,  size=2_2_4   ,  )

       return image_processor
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				self.dummy_prior
       lowerCamelCase		:     str          =				self.dummy_image_encoder
       lowerCamelCase		:     str          =				self.dummy_text_encoder
       lowerCamelCase		:     int          =				self.dummy_tokenizer
       lowerCamelCase		:     Tuple          =				self.dummy_image_processor

       lowerCamelCase		:     List[Any]          =				UnCLIPScheduler(
           variance_type="""fixed_small_log"""   ,  prediction_type="""sample"""   ,  num_train_timesteps=1_0_0_0   ,  clip_sample=__magic_name__   ,  clip_sample_range=10.0   ,  )

       lowerCamelCase		:     int          =				{
           """prior""": prior,
           """image_encoder""": image_encoder,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """scheduler""": scheduler,
           """image_processor""": image_processor,
       }

       return components
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=0				):
       if str(__magic_name__				).startswith("""mps"""				):
        lowerCamelCase		:     int          =				torch.manual_seed(__magic_name__				)
       else:
        lowerCamelCase		:     Optional[Any]          =				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)

       lowerCamelCase		:     Dict          =				floats_tensor((1, 3, 6_4, 6_4)   ,  rng=random.Random(__magic_name__				)				).to(__magic_name__				)
       lowerCamelCase		:     Dict          =				image.cpu().permute(0   ,  2   ,  3   ,  1				)[0]
       lowerCamelCase		:     str          =				Image.fromarray(np.uinta(__magic_name__				)				).convert("""RGB"""				).resize((2_5_6, 2_5_6)				)

       lowerCamelCase		:     str          =				{
           """prompt""": """horse""",
           """image""": init_image,
           """strength""": 0.5,
           """generator""": generator,
           """guidance_scale""": 4.0,
           """num_inference_steps""": 2,
           """output_type""": """np""",
       }
       return inputs
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[Any]          =				"""cpu"""

       lowerCamelCase		:     int          =				self.get_dummy_components()

       lowerCamelCase		:     Optional[int]          =				self.pipeline_class(**__magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				pipe.to(__magic_name__				)

       pipe.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     Union[str, Any]          =				pipe(**self.get_dummy_inputs(__magic_name__				)				)
       lowerCamelCase		:     Union[str, Any]          =				output.image_embeds

       lowerCamelCase		:     Any          =				pipe(
           **self.get_dummy_inputs(__magic_name__				)   ,  return_dict=__magic_name__   ,  )[0]

       lowerCamelCase		:     Any          =				image[0, -1_0:]
       lowerCamelCase		:     Optional[Any]          =				image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowerCamelCase		:     List[Any]          =				np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]				)

       assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
      @skip_mps
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				torch_device == """cpu"""
       lowerCamelCase		:     List[str]          =				True
       lowerCamelCase		:     int          =				False

       self._test_inference_batch_single_identical(
           test_max_difference=__magic_name__   ,  relax_max_difference=__magic_name__   ,  test_mean_pixel_difference=__magic_name__   ,  )






      @skip_mps
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				torch_device == """cpu"""
       lowerCamelCase		:     int          =				False

       self._test_attention_slicing_forward_pass(
           test_max_difference=__magic_name__   ,  test_mean_pixel_difference=__magic_name__   ,  )






import sys





def    _a (						lowerCamelCase,     lowerCamelCase							):
 if not numbers:
  return 0
 if target == 0 and target in numbers:
  return 0
 if not isinstance(lowerCamelCase,     (list, tuple)							) or not all(
     isinstance(lowerCamelCase,     lowerCamelCase							) for number in numbers							):
  raise ValueError("""numbers must be an iterable of integers"""							)

 lowerCamelCase		:     str          =				0
 lowerCamelCase		:     List[str]          =				sys.maxsize

 while right < len(lowerCamelCase							):
  curr_sum += numbers[right]
  while curr_sum >= target and left <= right:
   lowerCamelCase		:     Any          =				min(lowerCamelCase,     right - left + 1							)
   curr_sum -= numbers[left]
   left += 1
  right += 1

 return 0 if min_len == sys.maxsize else min_len




