



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))

