


from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    data
         snake_case					=    None






class   A__ :







    """simple docstring"""

    def __init__(	self   ):
         snake_case					=    None
         snake_case					=    None
    def __iter__(	self   ):
         snake_case					=    self.head
         while self.head:
              yield node.data
              snake_case					=    node.next
              if node == self.head:
                   break
    def __len__(	self   ):
         return sum(1 for _ in self   )
    def __repr__(	self   ):
         return "->".join(str(__snake_case   ) for item in iter(self   )   )
    def 		a_      (	self      , __snake_case   ):
         self.insert_nth(len(self   )      , __snake_case   )
    def 		a_      (	self      , __snake_case   ):
         self.insert_nth(0      , __snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         if index < 0 or index > len(self   ):
              raise IndexError('''list index out of range.'''   )
         snake_case					=    Node(__snake_case   )
         if self.head is None:
              snake_case					=    new_node  # first node points itself
              snake_case					=    snake_case					=    new_node
         elif index == 0:  # insert at head
              snake_case					=    self.head
              snake_case					=    snake_case					=    new_node
         else:
              snake_case					=    self.head
              for _ in range(index - 1   ):
                   snake_case					=    temp.next
              snake_case					=    temp.next
              snake_case					=    new_node
              if index == len(self   ) - 1:  # insert at tail
                   snake_case					=    new_node
    def 		a_      (	self   ):
         return self.delete_nth(0   )
    def 		a_      (	self   ):
         return self.delete_nth(len(self   ) - 1   )
    def 		a_      (	self      , __snake_case = 0   ):
         if not 0 <= index < len(self   ):
              raise IndexError('''list index out of range.'''   )
         snake_case					=    self.head
         if self.head == self.tail:  # just one node
              snake_case					=    snake_case					=    None
         elif index == 0:  # delete head node
              snake_case					=    self.tail.next.next
              snake_case					=    self.head.next
         else:
              snake_case					=    self.head
              for _ in range(index - 1   ):
                   snake_case					=    temp.next
              snake_case					=    temp.next
              snake_case					=    temp.next.next
              if index == len(self   ) - 1:  # delete at tail
                   snake_case					=    temp
         return delete_node.data




    def 		a_      (	self   ):
         return len(self   ) == 0






def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    CircularLinkedList()
     assert len(UpperCamelCase_    ) == 0
     assert circular_linked_list.is_empty() is True
     assert str(UpperCamelCase_    ) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1    )
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0    )
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5    ):
          assert len(UpperCamelCase_    ) == i
          circular_linked_list.insert_nth(UpperCamelCase_							,i + 1    )
     assert str(UpperCamelCase_    ) == "->".join(str(UpperCamelCase_    ) for i in range(1							,6    )    )

     circular_linked_list.insert_tail(6    )
     assert str(UpperCamelCase_    ) == "->".join(str(UpperCamelCase_    ) for i in range(1							,7    )    )
     circular_linked_list.insert_head(0    )
     assert str(UpperCamelCase_    ) == "->".join(str(UpperCamelCase_    ) for i in range(0							,7    )    )

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(UpperCamelCase_    ) == "->".join(str(UpperCamelCase_    ) for i in range(1							,6    )    )
     assert circular_linked_list.delete_nth(2    ) == 3

     circular_linked_list.insert_nth(2							,3    )
     assert str(UpperCamelCase_    ) == "->".join(str(UpperCamelCase_    ) for i in range(1							,6    )    )

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		KandinskyImgaImgPipeline
    __magic_name__        =		['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    __magic_name__        =		[
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    __magic_name__        =		[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    MCLIPConfig(
             numDims=self.cross_attention_dim      , transformerDimensions=self.text_embedder_hidden_size      , hidden_size=self.text_embedder_hidden_size      , intermediate_size=3_7      , num_attention_heads=4      , num_hidden_layers=5      , vocab_size=1_0_0_5      , )

         snake_case					=    MultilingualCLIP(__snake_case   )
         snake_case					=    text_encoder.eval()

         return text_encoder
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_image''',
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''encoder_hid_dim''': self.text_embedder_hidden_size,
             '''encoder_hid_dim_type''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         return {
             "block_out_channels": [3_2, 6_4],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    VQModel(**self.dummy_movq_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_unet
         snake_case					=    self.dummy_movq

         snake_case					=    {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_0085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         snake_case					=    DDIMScheduler(**__snake_case   )

         snake_case					=    {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components
    def 		a_      (	self      , __snake_case      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, self.cross_attention_dim)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    floats_tensor((1, self.cross_attention_dim)      , rng=random.Random(seed + 1   )   ).to(__snake_case   )
         # create init_image
         snake_case					=    floats_tensor((1, 3, 6_4, 6_4)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   )[0]
         snake_case					=    Image.fromarray(np.uinta(__snake_case   )   ).convert('''RGB'''   ).resize((2_5_6, 2_5_6)   )

         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
         snake_case					=    {
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs




    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    pipe(**self.get_dummy_inputs(__snake_case   )   )
         snake_case					=    output.images

         snake_case					=    pipe(
             **self.get_dummy_inputs(__snake_case   )      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]   )
         assert (
             np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
         ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def 		a_      (	self   ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




    def 		a_      (	self   ):
         snake_case					=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_img2img_frog.npy'''   )

         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
         snake_case					=    '''A red cartoon frog, 4k'''

         snake_case					=    KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''      , torch_dtype=torch.floataa   )
         pipe_prior.to(__snake_case   )

         snake_case					=    KandinskyImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1'''      , torch_dtype=torch.floataa   )
         snake_case					=    pipeline.to(__snake_case   )

         pipeline.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device='''cpu'''   ).manual_seed(0   )
         snake_case			,  snake_case					=    pipe_prior(
             __snake_case      , generator=__snake_case      , num_inference_steps=5      , negative_prompt=''''''      , ).to_tuple()

         snake_case					=    pipeline(
             __snake_case      , image=__snake_case      , image_embeds=__snake_case      , negative_image_embeds=__snake_case      , generator=__snake_case      , num_inference_steps=1_0_0      , height=7_6_8      , width=7_6_8      , strength=0.2      , output_type='''np'''      , )

         snake_case					=    output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__snake_case      , __snake_case   )




