





'''simple docstring'''



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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   KandinskyVaaPriorPipeline
     UpperCAmelCase__       :						List[Any]      =   ['''prompt''']
     UpperCAmelCase__       :						int      =   ['''prompt''', '''negative_prompt''']
     UpperCAmelCase__       :						Any      =   [
         '''num_images_per_prompt''',
         '''generator''',
         '''num_inference_steps''',
         '''latents''',
         '''negative_prompt''',
         '''guidance_scale''',
         '''output_type''',
         '''return_dict''',
     ]
     UpperCAmelCase__       :						Any      =   False

     @property
     def 	UpperCamelCase(					self	):
       return 32

     @property
     def 	UpperCamelCase(					self	):
       return 32

     @property
     def 	UpperCamelCase(					self	):
       return self.time_input_dim

     @property
     def 	UpperCamelCase(					self	):
       return self.time_input_dim * 4

     @property
     def 	UpperCamelCase(					self	):
       return 100

     @property
     def 	UpperCamelCase(					self	):
       _snake_case     		=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
       return tokenizer

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				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(lowerCamelCase	)

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)

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

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

     @property
     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				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						,   )

       _snake_case     		=				CLIPVisionModelWithProjection(lowerCamelCase	)
       return model

     @property
     def 	UpperCamelCase(					self	):
       _snake_case     		=				CLIPImageProcessor(
           crop_size=224						,   do_center_crop=lowerCamelCase						,   do_normalize=lowerCamelCase						,   do_resize=lowerCamelCase						,   image_mean=[0.48145466, 0.4578275, 0.40821073]						,   image_std=[0.26862954, 0.26130258, 0.27577711]						,   resample=3						,   size=224						,   )

       return image_processor

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.dummy_prior
       _snake_case     		=				self.dummy_image_encoder
       _snake_case     		=				self.dummy_text_encoder
       _snake_case     		=				self.dummy_tokenizer
       _snake_case     		=				self.dummy_image_processor

       _snake_case     		=				UnCLIPScheduler(
           variance_type="fixed_small_log"						,   prediction_type="sample"						,   num_train_timesteps=1_000						,   clip_sample=lowerCamelCase						,   clip_sample_range=10.0						,   )

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

       return components

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=0	):
       if str(lowerCamelCase	).startswith("mps"	):
         _snake_case     		=				torch.manual_seed(lowerCamelCase	)
       else:
         _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       _snake_case     		=				{
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"

       _snake_case     		=				self.get_dummy_components()

       _snake_case     		=				self.pipeline_class(**lowerCamelCase	)
       _snake_case     		=				pipe.to(lowerCamelCase	)

       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				pipe(**self.get_dummy_inputs(lowerCamelCase	)	)
       _snake_case     		=				output.image_embeds

       _snake_case     		=				pipe(
           **self.get_dummy_inputs(lowerCamelCase	)						,   return_dict=lowerCamelCase						,   )[0]

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

       assert image.shape == (1, 32)

       _snake_case     		=				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	):
       _snake_case     		=				torch_device == "cpu"
       _snake_case     		=				True
       _snake_case     		=				False

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






     @skip_mps
     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch_device == "cpu"
       _snake_case     		=				False

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









'''simple docstring'''



def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[0 for i in range(len(SCREAMING_SNAKE_CASE__       )       )]

  # initialize interval's left pointer and right pointer
  _snake_case		,		_snake_case     		=				0, 0

  for i in range(1					,	len(SCREAMING_SNAKE_CASE__       )       ):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case     		=				min(right_pointer - i + 1					,	z_result[i - left_pointer]       )
      _snake_case     		=				min_edge

    while go_next(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case		,		_snake_case     		=				i, i + z_result[i] - 1

  return z_result




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return i + z_result[i] < len(SCREAMING_SNAKE_CASE__       ) and s[z_result[i]] == s[i + z_result[i]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case     		=				z_function(pattern + input_str       )

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(SCREAMING_SNAKE_CASE__       ):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


