



'''simple docstring'''






A_				:					Optional[Any]							=      [0.33, 0.44, 0.55, 0.44, 0.33]
A_				:					str							=      3
A_				:					List[Any]							=      5

# Buffer Space (with Parameters Space)
A_				:					list[float]							=      []
A_				:					list[float]							=      []

# Machine Time
A_				:					str							=      0




def        snake_case_   (	lowerCAmelCase_						)->						List[str]:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       # Choosing Dynamical Systems (All)
       for key, value in enumerate(lowerCAmelCase_						):
              # Evolution Parameter
              _UpperCAmelCase    :		List[Any]     	=					float(seed / value						)

              # Control Theory: Orbit Change
              _UpperCAmelCase    :		Any     	=					(buffer_space[(key + 1) % m] + e) % 1

              # Control Theory: Trajectory Change
              _UpperCAmelCase    :		List[str]     	=					(params_space[key] + e) % 1 + 3

              # Modification (Transition Function) - Jumps
              _UpperCAmelCase    :		int     	=					round(float(r * value * (1 - value)						)			,  10						)
              _UpperCAmelCase    :		Union[str, Any]     	=					r  # Saving to Parameters Space

       # Logistic Map
       assert max(lowerCAmelCase_						) < 1
       assert max(lowerCAmelCase_						) < 4

       # Machine Time
       machine_time += 1




def        snake_case_   (	)->						str:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       # PRNG (Xorshift by George Marsaglia)
       def xorshift(lowerCAmelCase_			,  lowerCAmelCase_						):
              x ^= y >> 13
              y ^= x << 17
              x ^= y >> 5
              return x

       # Choosing Dynamical Systems (Increment)
       _UpperCAmelCase    :		int     	=					machine_time % m

       # Evolution (Time Length)
       for _ in range(0			,  lowerCAmelCase_						):
              # Variables (Position + Parameters)
              _UpperCAmelCase    :		Tuple     	=					params_space[key]
              _UpperCAmelCase    :		Optional[Any]     	=					buffer_space[key]

              # Modification (Transition Function) - Flow
              _UpperCAmelCase    :		Any     	=					round(float(r * value * (1 - value)						)			,  10						)
              _UpperCAmelCase    :		str     	=					(machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

       # Choosing Chaotic Data
       _UpperCAmelCase    :		List[Any]     	=					int(buffer_space[(key + 2) % m] * (10**10)						)
       _UpperCAmelCase    :		Union[str, Any]     	=					int(buffer_space[(key - 2) % m] * (10**10)						)

       # Machine Time
       machine_time += 1

       return xorshift(lowerCAmelCase_			,  lowerCAmelCase_						) % 0xFF_FFF_FFF




def        snake_case_   (	)->						Union[str, Any]:





       '''simple docstring'''


       global buffer_space, params_space, machine_time, K, m, t

       _UpperCAmelCase    :		int     	=					K
       _UpperCAmelCase    :		List[Any]     	=					[0] * m
       _UpperCAmelCase    :		Optional[Any]     	=					0


if __name__ == "__main__":
   # Initialization
   reset()

   # Pushing Data (Input)
   import random

   A_				:					Dict							=      random.sample(range(0xffffffff), 1_0_0)
   for chunk in message:
      push(chunk)

   # for controlling
   A_				:					str							=      """"""

   # Pulling Data (Output)
   while inp in ("e", "E"):
      print(f"""{format(pull(), "#04x")}""")
      print(buffer_space)
      print(params_space)
      A_				:					Optional[Any]							=      input("""(e)exit? """).strip()









import random
import unittest

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

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     KandinskyVaaPriorEmbaEmbPipeline
     __UpperCAmelCase						:			str							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Dict							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Optional[Any]							=     [
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     __UpperCAmelCase						:			Dict							=     False

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         return 32

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






         """simple docstring"""
         return 32

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






         """simple docstring"""
         return self.time_input_dim

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[Any]:






         """simple docstring"""
         return self.time_input_dim * 4

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Tuple:






         """simple docstring"""
         return 1_00

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
         return tokenizer

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






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							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(A     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      List[Any]:






         """simple docstring"""
         torch.manual_seed(0     )

         a__  			=							{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

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

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							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    ,					)

         a__  			=							CLIPVisionModelWithProjection(A     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPImageProcessor(
             crop_size=2_24    ,					do_center_crop=A    ,					do_normalize=A    ,					do_resize=A    ,					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_24    ,					)

         return image_processor

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.dummy_prior
         a__  			=							self.dummy_image_encoder
         a__  			=							self.dummy_text_encoder
         a__  			=							self.dummy_tokenizer
         a__  			=							self.dummy_image_processor

         a__  			=							UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,					prediction_type="""sample"""    ,					num_train_timesteps=10_00    ,					clip_sample=A    ,					clip_sample_range=1_0.0    ,					)

         a__  			=							{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }

         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tuple    ,					A			:							Optional[Any]=0     )      ->      Any:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							floats_tensor((1, 3, 64, 64)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]
         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )

         a__  			=							{
             """prompt""": """horse""",
             """image""": init_image,
             """strength""": 0.5,
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							"""cpu"""

         a__  			=							self.get_dummy_components()

         a__  			=							self.pipeline_class(**A     )
         a__  			=							pipe.to(A     )

         pipe.set_progress_bar_config(disable=A     )

         a__  			=							pipe(**self.get_dummy_inputs(A     )     )
         a__  			=							output.image_embeds

         a__  			=							pipe(
             **self.get_dummy_inputs(A     )    ,					return_dict=A    ,					)[0]

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

         assert image.shape == (1, 32)

         a__  			=							np.array(
             [
                 0.1_0_7_1_2_8_4,
                 1.3_3_3_0_2_7_1,
                 0.6_1_2_6_0_2_2_3,
                 -0.6_6_9_1_0_6_5,
                 -0.3_8_4_6_8_5_2,
                 -1.0_3_0_3_6_6_1,
                 0.2_2_7_1_6_1_1_1,
                 0.0_3_3_4_8_9_0_1,
                 0.3_0_0_4_0_6_7_5,
                 -0.2_4_8_0_5_0_2_9,
             ]     )

         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 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							True
         a__  			=							False

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






     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							False

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






