


import unittest

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

from diffusers import KandinskyPriorPipeline, 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   A__							(						UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  KandinskyPriorPipeline
						UpperCAmelCase									=  ["prompt"]
						UpperCAmelCase									=  ["prompt", "negative_prompt"]
						UpperCAmelCase									=  [
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase									=  False




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


										"""simple docstring"""





										return 32




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


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase (				self    :					str     )							->   int:


										"""simple docstring"""





										return self.time_input_dim




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


										"""simple docstring"""





										return self.time_input_dim * 4




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


										"""simple docstring"""





										return 100




						@property
						def 							__UpperCamelCase (				self    :					Tuple     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )
										return tokenizer




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


										"""simple docstring"""





										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =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=1000 ,			)
										return CLIPTextModelWithProjection(_a     )




						@property
						def 							__UpperCamelCase (				self    :					str     )							->   int:


										"""simple docstring"""





										torch.manual_seed(0     )

										_SCREAMING_SNAKE_CASE	     ={
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 12,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										_SCREAMING_SNAKE_CASE	     =PriorTransformer(**_a     )
										# 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
										_SCREAMING_SNAKE_CASE	     =nn.Parameter(torch.ones(model.clip_std.shape     )     )
										return model




						@property
						def 							__UpperCamelCase (				self    :					Tuple     )							->   int:


										"""simple docstring"""





										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size ,			image_size=224 ,			projection_dim=self.text_embedder_hidden_size ,			intermediate_size=37 ,			num_attention_heads=4 ,			num_channels=3 ,			num_hidden_layers=5 ,			patch_size=14 ,			)

										_SCREAMING_SNAKE_CASE	     =CLIPVisionModelWithProjection(_a     )
										return model




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =CLIPImageProcessor(
										    crop_size=224 ,			do_center_crop=_a ,			do_normalize=_a ,			do_resize=_a ,			image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,			image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,			resample=3 ,			size=224 ,			)

										return image_processor




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.dummy_prior
										_SCREAMING_SNAKE_CASE	     =self.dummy_image_encoder
										_SCREAMING_SNAKE_CASE	     =self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE	     =self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE	     =self.dummy_image_processor

										_SCREAMING_SNAKE_CASE	     =UnCLIPScheduler(
										    variance_type='''fixed_small_log''' ,			prediction_type='''sample''' ,			num_train_timesteps=1000 ,			clip_sample=_a ,			clip_sample_range=10.0 ,			)

										_SCREAMING_SNAKE_CASE	     ={
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''scheduler''': scheduler,
										    '''image_processor''': image_processor,
										}

										return components




						def 							__UpperCamelCase (				self    :					str ,			_a    :					Dict ,			_a    :					List[str]=0     )							->   Dict:


										"""simple docstring"""





										if str(_a     ).startswith('''mps'''     ):
														_SCREAMING_SNAKE_CASE	     =torch.manual_seed(_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =torch.Generator(device=_a     ).manual_seed(_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''cpu'''

										_SCREAMING_SNAKE_CASE	     =self.get_dummy_components()

										_SCREAMING_SNAKE_CASE	     =self.pipeline_class(**_a     )
										_SCREAMING_SNAKE_CASE	     =pipe.to(_a     )

										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =pipe(**self.get_dummy_inputs(_a     )     )
										_SCREAMING_SNAKE_CASE	     =output.image_embeds

										_SCREAMING_SNAKE_CASE	     =pipe(
										    **self.get_dummy_inputs(_a     ) ,			return_dict=_a ,			)[0]

										_SCREAMING_SNAKE_CASE	     =image[0, -10:]
										_SCREAMING_SNAKE_CASE	     =image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										_SCREAMING_SNAKE_CASE	     =np.array(
										    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]     )

										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    :					List[Any]     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =False

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




						@skip_mps
						def 							__UpperCamelCase (				self    :					Tuple     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE	     =False

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

'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = 100      ,    )   ->				float:


		'''simple docstring'''


		snake_case   :    int			       =		x_start
		snake_case   :    Tuple			       =		fnc(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Dict			       =		0.0

		for _ in range(SCREAMING_SNAKE_CASE__       ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				snake_case   :    str			       =		(x_end - x_start) / steps + xa
				snake_case   :    str			       =		fnc(SCREAMING_SNAKE_CASE__       )
				area += abs(fxa + fxa       ) * (xa - xa) / 2

				# Increment step
				snake_case   :    Any			       =		xa
				snake_case   :    List[str]			       =		fxa
		return area


if __name__ == "__main__":




						def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				str:


								'''simple docstring'''


								return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						lowercase__		    =	1_0
						while i <= 1_0_0_0_0_0:
												lowercase__		    =	trapezoidal_area(f, -5, 5, i)
												print(f"with {i} steps: {area}")
												i *= 1_0


