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_     (   __SCREAMING_SNAKE_CASE						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Any						=      KandinskyVaaPriorPipeline
    a__	:						Tuple						=      ["""prompt"""]
    a__	:						Dict						=      ["""prompt""", """negative_prompt"""]
    a__	:						int						=      [
        """num_images_per_prompt""",
        """generator""",
        """num_inference_steps""",
        """latents""",
        """negative_prompt""",
        """guidance_scale""",
        """output_type""",
        """return_dict""",
    ]
    a__	:						List[str]						=      False







    @property
    def 			UpperCamelCase__   (	self)       ->	str:
        return 32







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return 32







    @property
    def 			UpperCamelCase__   (	self)       ->	Dict:
        return self.time_input_dim







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return self.time_input_dim * 4







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return 100







    @property
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Optional[Any]			  =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
        return tokenizer







    @property
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        torch.manual_seed(0)
        __UpperCamelCase     :Optional[Any]			  =		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-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
        return CLIPTextModelWithProjection(__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        torch.manual_seed(0)

        __UpperCamelCase     :Optional[int]			  =		{
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 12,
            '''embedding_dim''': self.text_embedder_hidden_size,
            '''num_layers''': 1,
        }

        __UpperCamelCase     :Dict			  =		PriorTransformer(**__lowercase)
        # 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
        __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.ones(model.clip_std.shape))
        return model







    @property
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        torch.manual_seed(0)
        __UpperCamelCase     :int			  =		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 , )

        __UpperCamelCase     :Optional[Any]			  =		CLIPVisionModelWithProjection(__lowercase)
        return model







    @property
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :List[str]			  =		CLIPImageProcessor(
            crop_size=224 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , 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)       ->	Any:
        __UpperCamelCase     :List[str]			  =		self.dummy_prior
        __UpperCamelCase     :Any			  =		self.dummy_image_encoder
        __UpperCamelCase     :Dict			  =		self.dummy_text_encoder
        __UpperCamelCase     :Union[str, Any]			  =		self.dummy_tokenizer
        __UpperCamelCase     :List[str]			  =		self.dummy_image_processor

        __UpperCamelCase     :int			  =		UnCLIPScheduler(
            variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1_000 , clip_sample=__lowercase , clip_sample_range=10.0 , )

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

        return components







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=0)       ->	Optional[Any]:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :List[Any]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :Optional[int]			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		{
            '''prompt''': '''horse''',
            '''generator''': generator,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Dict			  =		'''cpu'''

        __UpperCamelCase     :Optional[Any]			  =		self.get_dummy_components()

        __UpperCamelCase     :Dict			  =		self.pipeline_class(**__lowercase)
        __UpperCamelCase     :str			  =		pipe.to(__lowercase)

        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase     :Tuple			  =		pipe(**self.get_dummy_inputs(__lowercase))
        __UpperCamelCase     :Any			  =		output.image_embeds

        __UpperCamelCase     :int			  =		pipe(
            **self.get_dummy_inputs(__lowercase) , return_dict=__lowercase , )[0]

        __UpperCamelCase     :int			  =		image[0, -10:]
        __UpperCamelCase     :Optional[Any]			  =		image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

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

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







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :List[str]			  =		torch_device == '''cpu'''
        __UpperCamelCase     :int			  =		False

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







def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return lst

				__A   			= 1

				while i < len(a_						):
								if lst[i - 1] <= lst[i]:
												i += 1
								else:
												__A			,							__A   			= lst[i], lst[i - 1]
												i -= 1
												if i == 0:
																__A   			= 1

				return lst


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[Any]							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:List[Any]							 =				[int(item) for item in user_input.split(',')]
					print(gnome_sort(unsorted))
