import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			VideoToVideoSDPipeline
  _SCREAMING_SNAKE_CASE    				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""} ) - {"""image""", """width""", """height"""}
  _SCREAMING_SNAKE_CASE    				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""} ) - {"""image"""}
  _SCREAMING_SNAKE_CASE    				=			PipelineTesterMixin.required_optional_params - {"""latents"""}
  _SCREAMING_SNAKE_CASE    				=			False

  # No `output_type`.
  _SCREAMING_SNAKE_CASE    				=			frozenset(
      [
          """num_inference_steps""",
          """generator""",
          """latents""",
          """return_dict""",
          """callback""",
          """callback_steps""",
      ] )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  UNetaDConditionModel(
       block_out_channels=(32, 64, 64, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")     ,							up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")     ,							cross_attention_dim=32     ,							attention_head_dim=4     ,							)
   __a       =  DDIMScheduler(
       beta_start=0.0_00_85     ,							beta_end=0.0_12     ,							beta_schedule="scaled_linear"     ,							clip_sample=__a     ,							set_alpha_to_one=__a     ,							)
   torch.manual_seed(0       )
   __a       =  AutoencoderKL(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=4     ,							sample_size=1_28     ,							)
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							hidden_act="gelu"     ,							projection_dim=5_12     ,							)
   __a       =  CLIPTextModel(__a       )
   __a       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

   __a       =  {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
   }
   return components

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int=0       ):
   __a       =  floats_tensor((1, 3, 3, 32, 32)     ,							rng=random.Random(__a       )       ).to(__a       )

   if str(__a       ).startswith("mps"       ):
    __a       =  torch.manual_seed(__a       )
   else:
    __a       =  torch.Generator(device=__a       ).manual_seed(__a       )
   __a       =  {
       "prompt": "A painting of a squirrel eating a burger",
       "video": video,
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 6.0,
       "output_type": "pt",
   }
   return inputs

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
   __a       =  self.get_dummy_components()
   __a       =  VideoToVideoSDPipeline(**__a       )
   __a       =  sd_pipe.to(__a       )
   sd_pipe.set_progress_bar_config(disable=__a       )

   __a       =  self.get_dummy_inputs(__a       )
   __a       =  "np"
   __a       =  sd_pipe(**__a       ).frames
   __a       =  frames[0][-3:, -3:, -1]

   assert frames[0].shape == (32, 32, 3)
   __a       =  np.array([1_06, 1_17, 1_13, 1_74, 1_37, 1_12, 1_48, 1_51, 1_31]       )

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

  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()     ,							reason="XFormers attention is only available with CUDA and `xformers` installed"     ,							)
  def 		_UpperCAmelCase   (		self :      int       ):
   self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a     ,							expected_max_diff=5E-3       )

  @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
  def 		_UpperCAmelCase   (		self :      Any       ):
   pass

  @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   pass

  @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."       )
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   pass

  def 		_UpperCAmelCase   (		self :      Any       ):
   return super().test_progress_bar()
@slow
@skip_mps
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"     ,							torch_dtype=torch.floataa       )
   pipe.enable_model_cpu_offload()

   # 10 frames
   __a       =  torch.Generator(device="cpu"       ).manual_seed(0       )
   __a       =  torch.randn((1, 10, 3, 10_24, 5_76)     ,							generator=__a       )
   __a       =  video.to("cuda"       )

   __a       =  "Spiderman is surfing"

   __a       =  pipe(__a     ,							video=__a     ,							generator=__a     ,							num_inference_steps=3     ,							output_type="pt"       ).frames

   __a       =  np.array([-1.0_45_89_84, -1.1_27_92_97, -0.9_66_30_86, -0.91_50_39_06, -0.75_09_76_56]       )
   assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1E-2



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


