






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 		_A						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			Union[str, Any]						=					KandinskyVaaPriorPipeline
  lowercase_						:			Dict						=					['''prompt''']
  lowercase_						:			str						=					['''prompt''', '''negative_prompt''']
  lowercase_						:			int						=					[
      '''num_images_per_prompt''',
      '''generator''',
      '''num_inference_steps''',
      '''latents''',
      '''negative_prompt''',
      '''guidance_scale''',
      '''output_type''',
      '''return_dict''',
  ]
  lowercase_						:			Union[str, Any]						=					False

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     return self.time_input_dim

  @property
  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     return self.time_input_dim * 4

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     return 1_00

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
     return tokenizer

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			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=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=10_00   ,				)
     return CLIPTextModelWithProjection(lowerCamelCase__			)

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     torch.manual_seed(0			)

     __UpperCamelCase	:			Union[str, Any]					=					{
         """num_attention_heads""": 2,
         """attention_head_dim""": 12,
         """embedding_dim""": self.text_embedder_hidden_size,
         """num_layers""": 1,
     }

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

  @property
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Optional[Any]					=					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   ,				)

     __UpperCamelCase	:			Dict					=					CLIPVisionModelWithProjection(lowerCamelCase__			)
     return model

  @property
  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					CLIPImageProcessor(
         crop_size=2_24   ,				do_center_crop=lowerCamelCase__   ,				do_normalize=lowerCamelCase__   ,				do_resize=lowerCamelCase__   ,				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=2_24   ,				)

     return image_processor

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.dummy_prior
     __UpperCamelCase	:			Dict					=					self.dummy_image_encoder
     __UpperCamelCase	:			Optional[Any]					=					self.dummy_text_encoder
     __UpperCamelCase	:			Dict					=					self.dummy_tokenizer
     __UpperCamelCase	:			Tuple					=					self.dummy_image_processor

     __UpperCamelCase	:			Dict					=					UnCLIPScheduler(
         variance_type="""fixed_small_log"""   ,				prediction_type="""sample"""   ,				num_train_timesteps=10_00   ,				clip_sample=lowerCamelCase__   ,				clip_sample_range=10.0   ,				)

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

     return components

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	Optional[Any]=0			):
     """simple docstring"""

     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			Union[str, Any]					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Optional[Any]					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[Any]					=					{
         """prompt""": """horse""",
         """generator""": generator,
         """guidance_scale""": 4.0,
         """num_inference_steps""": 2,
         """output_type""": """np""",
     }
     return inputs

  def    a					(    self						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					"""cpu"""

     __UpperCamelCase	:			List[str]					=					self.get_dummy_components()

     __UpperCamelCase	:			Dict					=					self.pipeline_class(**lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					pipe.to(lowerCamelCase__			)

     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			str					=					pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
     __UpperCamelCase	:			List[Any]					=					output.image_embeds

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

     __UpperCamelCase	:			List[str]					=					image[0, -10:]
     __UpperCamelCase	:			Union[str, Any]					=					image_from_tuple[0, -10:]

     assert image.shape == (1, 32)

     __UpperCamelCase	:			Dict					=					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    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					torch_device == """cpu"""
     __UpperCamelCase	:			Optional[int]					=					True
     __UpperCamelCase	:			List[Any]					=					False

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

  @skip_mps
  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					torch_device == """cpu"""
     __UpperCamelCase	:			Optional[Any]					=					False

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











from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



