



import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




  '''simple docstring'''



  snake_case_			=KandinskyVaaPriorPipeline
  snake_case_			=["""prompt"""]
  snake_case_			=["""prompt""", """negative_prompt"""]
  snake_case_			=[
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  snake_case_			=False



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      return 32



  @property
  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      return 32



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      return self.time_input_dim



  @property
  def 	lowerCAmelCase__   (self				)  ->    str:

      """simple docstring"""






      return self.time_input_dim * 4



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      return 1_00



  @property
  def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       Optional[Any]				=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
      return tokenizer



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[str]:

      """simple docstring"""






      torch.manual_seed(0				)
      lowerCAmelCase__  :       Tuple				=  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=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=10_00	,)
      return CLIPTextModelWithProjection(__lowerCamelCase				)



  @property
  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      torch.manual_seed(0				)

      lowerCAmelCase__  :       List[Any]				=  {
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      lowerCAmelCase__  :       Any				=  PriorTransformer(**__lowerCamelCase				)
      # 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__  :       Any				=  nn.Parameter(torch.ones(model.clip_std.shape				)				)
      return model



  @property
  def 	lowerCAmelCase__   (self				)  ->    Tuple:

      """simple docstring"""






      torch.manual_seed(0				)
      lowerCAmelCase__  :       int				=  CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size	,image_size=2_24	,projection_dim=self.text_embedder_hidden_size	,intermediate_size=37	,num_attention_heads=4	,num_channels=3	,num_hidden_layers=5	,patch_size=14	,)

      lowerCAmelCase__  :       str				=  CLIPVisionModelWithProjection(__lowerCamelCase				)
      return model



  @property
  def 	lowerCAmelCase__   (self				)  ->    List[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       Any				=  CLIPImageProcessor(
          crop_size=2_24	,do_center_crop=__lowerCamelCase	,do_normalize=__lowerCamelCase	,do_resize=__lowerCamelCase	,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]	,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]	,resample=3	,size=2_24	,)

      return image_processor



  def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       Any				=  self.dummy_prior
      lowerCAmelCase__  :       int				=  self.dummy_image_encoder
      lowerCAmelCase__  :       Union[str, Any]				=  self.dummy_text_encoder
      lowerCAmelCase__  :       Optional[int]				=  self.dummy_tokenizer
      lowerCAmelCase__  :       Dict				=  self.dummy_image_processor

      lowerCAmelCase__  :       Tuple				=  UnCLIPScheduler(
          variance_type='''fixed_small_log'''	,prediction_type='''sample'''	,num_train_timesteps=10_00	,clip_sample=__lowerCamelCase	,clip_sample_range=10.0	,)

      lowerCAmelCase__  :       List[Any]				=  {
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''scheduler''': scheduler,
          '''image_processor''': image_processor,
      }

      return components



  def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=0				)  ->    List[Any]:

      """simple docstring"""






      if str(__lowerCamelCase				).startswith('''mps'''				):
          lowerCAmelCase__  :       List[Any]				=  torch.manual_seed(__lowerCamelCase				)
      else:
          lowerCAmelCase__  :       int				=  torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
      lowerCAmelCase__  :       Tuple				=  {
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



  def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       int				=  '''cpu'''

      lowerCAmelCase__  :       str				=  self.get_dummy_components()

      lowerCAmelCase__  :       List[Any]				=  self.pipeline_class(**__lowerCamelCase				)
      lowerCAmelCase__  :       int				=  pipe.to(__lowerCamelCase				)

      pipe.set_progress_bar_config(disable=__lowerCamelCase				)

      lowerCAmelCase__  :       Optional[int]				=  pipe(**self.get_dummy_inputs(__lowerCamelCase				)				)
      lowerCAmelCase__  :       Optional[int]				=  output.image_embeds

      lowerCAmelCase__  :       Optional[Any]				=  pipe(
          **self.get_dummy_inputs(__lowerCamelCase				)	,return_dict=__lowerCamelCase	,)[0]

      lowerCAmelCase__  :       Optional[int]				=  image[0, -10:]
      lowerCAmelCase__  :       Tuple				=  image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      lowerCAmelCase__  :       Dict				=  np.array(
          [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]				)

      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 	lowerCAmelCase__   (self				)  ->    int:

      """simple docstring"""






      lowerCAmelCase__  :       int				=  torch_device == '''cpu'''
      lowerCAmelCase__  :       List[str]				=  True
      lowerCAmelCase__  :       Optional[int]				=  False

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



  @skip_mps
  def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

      """simple docstring"""






      lowerCAmelCase__  :       Optional[Any]				=  torch_device == '''cpu'''
      lowerCAmelCase__  :       Union[str, Any]				=  False

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  [0 for i in range(r + 1)]
				# nc0 = 1
				lowerCAmelCase__  :       Tuple				=  1
				for i in range(1 ,n + 1):
								# to compute current row from previous row.
								lowerCAmelCase__  :       Optional[int]				=  min(lowerCamelCase_ ,lowerCamelCase_)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]

print(binomial_coefficient(n=1_0, r=5))

