


'''simple docstring'''





from __future__ import annotations

from collections.abc import Iterator
from typing import Any




class 				UpperCAmelCase			:


 def __init__(      self							:	Dict			,       __lowerCamelCase							:	Any ):
    UpperCAmelCase__	:Any      =      data
    UpperCAmelCase__	:Node | None      =      None




class 				UpperCAmelCase			:


 def __init__(      self							:	List[str] ):
    UpperCAmelCase__	:str      =      None
    UpperCAmelCase__	:Any      =      None


 def __iter__(      self							:	List[Any] ):
    UpperCAmelCase__	:str      =      self.head
    while self.head:
       yield node.data
       UpperCAmelCase__	:Optional[int]      =      node.next
       if node == self.head:
          break


 def __len__(      self							:	str ):
    return sum(1 for _ in self )


 def __repr__(      self							:	Tuple ):
    return "->".join(str(__lowerCamelCase ) for item in iter(self ) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	Any ):
    self.insert_nth(len(self )			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	Any ):
    self.insert_nth(0			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Any ):
    if index < 0 or index > len(self ):
       raise IndexError('''list index out of range.''' )
    UpperCAmelCase__	:List[Any]      =      Node(__lowerCamelCase )
    if self.head is None:
       UpperCAmelCase__	:Union[str, Any]      =      new_node  # first node points itself
       UpperCAmelCase__	:Optional[Any]      =      new_node
    elif index == 0:  # insert at head
       UpperCAmelCase__	:Optional[int]      =      self.head
       UpperCAmelCase__	:Optional[Any]      =      new_node
    else:
       UpperCAmelCase__	:List[Any]      =      self.head
       for _ in range(index - 1 ):
          UpperCAmelCase__	:List[str]      =      temp.next
       UpperCAmelCase__	:Optional[int]      =      temp.next
       UpperCAmelCase__	:Union[str, Any]      =      new_node
       if index == len(self ) - 1:  # insert at tail
          UpperCAmelCase__	:Optional[Any]      =      new_node


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    return self.delete_nth(0 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return self.delete_nth(len(self ) - 1 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	int = 0 ):
    if not 0 <= index < len(self ):
       raise IndexError('''list index out of range.''' )
    UpperCAmelCase__	:Optional[int]      =      self.head
    if self.head == self.tail:  # just one node
       UpperCAmelCase__	:List[Any]      =      None
    elif index == 0:  # delete head node
       UpperCAmelCase__	:Tuple      =      self.tail.next.next
       UpperCAmelCase__	:Any      =      self.head.next
    else:
       UpperCAmelCase__	:Dict      =      self.head
       for _ in range(index - 1 ):
          UpperCAmelCase__	:Union[str, Any]      =      temp.next
       UpperCAmelCase__	:Tuple      =      temp.next
       UpperCAmelCase__	:Dict      =      temp.next.next
       if index == len(self ) - 1:  # delete at tail
          UpperCAmelCase__	:Union[str, Any]      =      temp
    return delete_node.data





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    return len(self ) == 0


def        a__     (					):
   UpperCAmelCase__	:List[Any]      =      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()







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCamelCase												=						False




class 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    return 1_2


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return 1_2


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    return 3_2


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:List[str]      =      VQModel(
        block_out_channels=[3_2, 6_4]			,       in_channels=3			,       out_channels=3			,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,       latent_channels=3			,       num_vq_embeddings=self.num_embed			,       vq_embed_dim=3			,       )
    return model


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Tuple      =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
    return tokenizer


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    torch.manual_seed(0 )
    UpperCAmelCase__	:Optional[Any]      =      CLIPTextConfig(
        bos_token_id=0			,       eos_token_id=2			,       hidden_size=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 CLIPTextModel(__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    torch.manual_seed(0 )

    UpperCAmelCase__	:List[str]      =      1_2
    UpperCAmelCase__	:Optional[int]      =      1_2

    UpperCAmelCase__	:Optional[int]      =      {
        '''attention_bias''': True,
        '''cross_attention_dim''': 3_2,
        '''attention_head_dim''': height * width,
        '''num_attention_heads''': 1,
        '''num_vector_embeds''': self.num_embed,
        '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
        '''norm_num_groups''': 3_2,
        '''sample_size''': width,
        '''activation_fn''': '''geglu-approximate''',
    }

    UpperCAmelCase__	:List[str]      =      TransformeraDModel(**__lowerCamelCase )
    return model


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      '''cpu'''

    UpperCAmelCase__	:Optional[Any]      =      self.dummy_vqvae
    UpperCAmelCase__	:Optional[int]      =      self.dummy_text_encoder
    UpperCAmelCase__	:Union[str, Any]      =      self.dummy_tokenizer
    UpperCAmelCase__	:List[Any]      =      self.dummy_transformer
    UpperCAmelCase__	:str      =      VQDiffusionScheduler(self.num_embed )
    UpperCAmelCase__	:Optional[Any]      =      LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      VQDiffusionPipeline(
        vqvae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       transformer=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       learned_classifier_free_sampling_embeddings=__lowerCamelCase			,       )
    UpperCAmelCase__	:Any      =      pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:str      =      '''teddy bear playing in the pool'''

    UpperCAmelCase__	:Optional[int]      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Any      =      pipe([prompt]			,       generator=__lowerCamelCase			,       num_inference_steps=2			,       output_type='''np''' )
    UpperCAmelCase__	:str      =      output.images

    UpperCAmelCase__	:List[Any]      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Dict      =      pipe(
        [prompt]			,       generator=__lowerCamelCase			,       output_type='''np'''			,       return_dict=__lowerCamelCase			,       num_inference_steps=2 )[0]

    UpperCAmelCase__	:int      =      image[0, -3:, -3:, -1]
    UpperCAmelCase__	:List[Any]      =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 2_4, 2_4, 3)

    UpperCAmelCase__	:int      =      np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )

    assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:List[Any]      =      '''cpu'''

    UpperCAmelCase__	:int      =      self.dummy_vqvae
    UpperCAmelCase__	:List[str]      =      self.dummy_text_encoder
    UpperCAmelCase__	:Union[str, Any]      =      self.dummy_tokenizer
    UpperCAmelCase__	:Optional[Any]      =      self.dummy_transformer
    UpperCAmelCase__	:List[str]      =      VQDiffusionScheduler(self.num_embed )
    UpperCAmelCase__	:List[str]      =      LearnedClassifierFreeSamplingEmbeddings(
        learnable=__lowerCamelCase			,       hidden_size=self.text_embedder_hidden_size			,       length=tokenizer.model_max_length )

    UpperCAmelCase__	:List[Any]      =      VQDiffusionPipeline(
        vqvae=__lowerCamelCase			,       text_encoder=__lowerCamelCase			,       tokenizer=__lowerCamelCase			,       transformer=__lowerCamelCase			,       scheduler=__lowerCamelCase			,       learned_classifier_free_sampling_embeddings=__lowerCamelCase			,       )
    UpperCAmelCase__	:str      =      pipe.to(__lowerCamelCase )
    pipe.set_progress_bar_config(disable=__lowerCamelCase )

    UpperCAmelCase__	:str      =      '''teddy bear playing in the pool'''

    UpperCAmelCase__	:str      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      pipe([prompt]			,       generator=__lowerCamelCase			,       num_inference_steps=2			,       output_type='''np''' )
    UpperCAmelCase__	:Any      =      output.images

    UpperCAmelCase__	:Optional[int]      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:int      =      pipe(
        [prompt]			,       generator=__lowerCamelCase			,       output_type='''np'''			,       return_dict=__lowerCamelCase			,       num_inference_steps=2 )[0]

    UpperCAmelCase__	:List[Any]      =      image[0, -3:, -3:, -1]
    UpperCAmelCase__	:Optional[Any]      =      image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 2_4, 2_4, 3)

    UpperCAmelCase__	:Union[str, Any]      =      np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )

    assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2




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


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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

    UpperCAmelCase__	:Optional[Any]      =      VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
    UpperCAmelCase__	:List[str]      =      pipeline.to(__lowerCamelCase )
    pipeline.set_progress_bar_config(disable=__lowerCamelCase )

    # requires GPU generator for gumbel softmax
    # don't use GPU generator in tests though
    UpperCAmelCase__	:int      =      torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
    UpperCAmelCase__	:Union[str, Any]      =      pipeline(
        '''teddy bear playing in the pool'''			,       num_images_per_prompt=1			,       generator=__lowerCamelCase			,       output_type='''np'''			,       )

    UpperCAmelCase__	:List[str]      =      output.images[0]

    assert image.shape == (2_5_6, 2_5_6, 3)
    assert np.abs(expected_image - image ).max() < 2.0



