
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     lowercase (		lowerCamelCase__  ,       unittest.TestCase       ):
		__lowercase	:	int     					=    KandinskyVaaPriorPipeline
		__lowercase	:	Tuple     					=    ['prompt']
		__lowercase	:	int     					=    ['prompt', 'negative_prompt']
		__lowercase	:	Optional[Any]     					=    [
		    'num_images_per_prompt',
		    'generator',
		    'num_inference_steps',
		    'latents',
		    'negative_prompt',
		    'guidance_scale',
		    'output_type',
		    'return_dict',
		]
		__lowercase	:	Optional[Any]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
				return tokenizer



		@property
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					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=1_000    ,				)
				return CLIPTextModelWithProjection(A_	)



		@property
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				torch.manual_seed(0	)

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

				UpperCamelCase          =					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
				UpperCamelCase          =					nn.Parameter(torch.ones(model.clip_std.shape	)	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					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          =					CLIPVisionModelWithProjection(A_	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					CLIPImageProcessor(
				    crop_size=224    ,				do_center_crop=A_    ,				do_normalize=A_    ,				do_resize=A_    ,				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=224    ,				)

				return image_processor



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_prior
				UpperCamelCase          =					self.dummy_image_encoder
				UpperCamelCase          =					self.dummy_text_encoder
				UpperCamelCase          =					self.dummy_tokenizer
				UpperCamelCase          =					self.dummy_image_processor

				UpperCamelCase          =					UnCLIPScheduler(
				    variance_type='fixed_small_log'    ,				prediction_type='sample'    ,				num_train_timesteps=1_000    ,				clip_sample=A_    ,				clip_sample_range=10.0    ,				)

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

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[str]:
				"""simple docstring"""







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



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'cpu'

				UpperCamelCase          =					self.get_dummy_components()

				UpperCamelCase          =					self.pipeline_class(**A_	)
				UpperCamelCase          =					pipe.to(A_	)

				pipe.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					pipe(**self.get_dummy_inputs(A_	)	)
				UpperCamelCase          =					output.image_embeds

				UpperCamelCase          =					pipe(
				    **self.get_dummy_inputs(A_	)    ,				return_dict=A_    ,				)[0]

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

				assert image.shape == (1, 32)

				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					torch_device == 'cpu'
				UpperCamelCase          =					True
				UpperCamelCase          =					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	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					torch_device == 'cpu'
				UpperCamelCase          =					False

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


from __future__ import annotations


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

		UpperCamelCase          =					[]
		UpperCamelCase     ,  UpperCamelCase          =					input_list[low:mid], input_list[mid : high + 1]
		while left and right:
				result.append((left if left[0] <= right[0] else right).pop(0			)			)
		UpperCamelCase          =					result + left + right
		return input_list


def 				A	( lowercase			)       ->  list:
		'''simple docstring'''

		if len(lowercase			) <= 1:
				return input_list
		UpperCamelCase          =					list(lowercase			)

		# iteration for two-way merging
		UpperCamelCase          =					2
		while p <= len(lowercase			):
				# getting low, high and middle value for merge-sort of single list
				for i in range(0   ,	len(lowercase			)   ,	lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					i + p - 1
						UpperCamelCase          =					(low + high + 1) // 2
						UpperCamelCase          =					merge(lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)
				# final merge of last two parts
				if p * 2 >= len(lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					merge(lowercase   ,	0   ,	lowercase   ,	len(lowercase			) - 1			)
						break
				p *= 2

		return input_list


if __name__ == "__main__":
					_UpperCAmelCase :			Any              =	input("Enter numbers separated by a comma:\n").strip()
					if user_input == "":
										_UpperCAmelCase :			Optional[Any]              =	[]
					else:
										_UpperCAmelCase :			Any              =	[int(item.strip()) for item in user_input.split(",")]
					print(iter_merge_sort(unsorted))
