



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  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     KandinskyPriorPipeline
     _SCREAMING_SNAKE_CASE            =     ["""prompt"""]
     _SCREAMING_SNAKE_CASE            =     ["""prompt""", """negative_prompt"""]
     _SCREAMING_SNAKE_CASE            =     [
         """num_images_per_prompt""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE            =     False






     @property
     def        lowercase_       (	self  :  List[Any] )       ->int:
            return 3_2






     @property
     def        lowercase_       (	self  :  int )       ->int:
            return 3_2






     @property
     def        lowercase_       (	self  :  Dict )       ->List[Any]:
            return self.time_input_dim






     @property
     def        lowercase_       (	self  :  Dict )       ->str:
            return self.time_input_dim * 4






     @property
     def        lowercase_       (	self  :  Dict )       ->Optional[Any]:
            return 1_0_0






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






     @property
     def        lowercase_       (	self  :  Optional[int] )       ->List[Any]:
            torch.manual_seed(0 )
            snake_case__							:     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=3_7,			layer_norm_eps=1e-05,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=1_0_0_0,			)
            return CLIPTextModelWithProjection(_snake_case )






     @property
     def        lowercase_       (	self  :  List[str] )       ->int:
            torch.manual_seed(0 )

            snake_case__							:     Any            =     {
                'num_attention_heads': 2,
                'attention_head_dim': 1_2,
                'embedding_dim': self.text_embedder_hidden_size,
                'num_layers': 1,
            }

            snake_case__							:     Tuple            =     PriorTransformer(**_snake_case )
            # 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
            snake_case__							:     Optional[Any]            =     nn.Parameter(torch.ones(model.clip_std.shape ) )
            return model






     @property
     def        lowercase_       (	self  :  Any )       ->Optional[Any]:
            torch.manual_seed(0 )
            snake_case__							:     int            =     CLIPVisionConfig(
                hidden_size=self.text_embedder_hidden_size,			image_size=2_2_4,			projection_dim=self.text_embedder_hidden_size,			intermediate_size=3_7,			num_attention_heads=4,			num_channels=3,			num_hidden_layers=5,			patch_size=1_4,			)

            snake_case__							:     Tuple            =     CLIPVisionModelWithProjection(_snake_case )
            return model






     @property
     def        lowercase_       (	self  :  Optional[int] )       ->Tuple:
            snake_case__							:     Dict            =     CLIPImageProcessor(
                crop_size=2_2_4,			do_center_crop=_snake_case,			do_normalize=_snake_case,			do_resize=_snake_case,			image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],			image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],			resample=3,			size=2_2_4,			)

            return image_processor






     def        lowercase_       (	self  :  List[str] )       ->List[Any]:
            snake_case__							:     Dict            =     self.dummy_prior
            snake_case__							:     Optional[int]            =     self.dummy_image_encoder
            snake_case__							:     Optional[int]            =     self.dummy_text_encoder
            snake_case__							:     Tuple            =     self.dummy_tokenizer
            snake_case__							:     Tuple            =     self.dummy_image_processor

            snake_case__							:     str            =     UnCLIPScheduler(
                variance_type='fixed_small_log',			prediction_type='sample',			num_train_timesteps=1_0_0_0,			clip_sample=_snake_case,			clip_sample_range=1_0.0,			)

            snake_case__							:     Optional[Any]            =     {
                'prior': prior,
                'image_encoder': image_encoder,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'scheduler': scheduler,
                'image_processor': image_processor,
            }

            return components






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






     def        lowercase_       (	self  :  str )       ->Optional[int]:
            snake_case__							:     int            =     'cpu'

            snake_case__							:     Dict            =     self.get_dummy_components()

            snake_case__							:     Optional[int]            =     self.pipeline_class(**_snake_case )
            snake_case__							:     Optional[int]            =     pipe.to(_snake_case )

            pipe.set_progress_bar_config(disable=_snake_case )

            snake_case__							:     Optional[int]            =     pipe(**self.get_dummy_inputs(_snake_case ) )
            snake_case__							:     Optional[Any]            =     output.image_embeds

            snake_case__							:     Any            =     pipe(
                **self.get_dummy_inputs(_snake_case ),			return_dict=_snake_case,			)[0]

            snake_case__							:     Any            =     image[0, -1_0:]
            snake_case__							:     Union[str, Any]            =     image_from_tuple[0, -1_0:]

            assert image.shape == (1, 3_2)

            snake_case__							:     Tuple            =     np.array(
                [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )

            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        lowercase_       (	self  :  str )       ->List[str]:
            snake_case__							:     Any            =     torch_device == 'cpu'
            snake_case__							:     Any            =     True
            snake_case__							:     Optional[int]            =     False

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






     @skip_mps
     def        lowercase_       (	self  :  Optional[int] )       ->List[Any]:
            snake_case__							:     Dict            =     torch_device == 'cpu'
            snake_case__							:     Dict            =     False

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











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






