





from collections.abc import Callable

class 		UpperCamelCase__ :



     '''simple docstring'''

     def __init__(							self						:    List[str]						,lowerCamelCase__						:    Callable | None = None       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           # Stores indexes of each item for supporting updates and deletion.
           SCREAMING_SNAKE_CASE		=						{}
           # Stores current size of heap.
           SCREAMING_SNAKE_CASE		=						0
           # Stores function used to evaluate the score of an item on which basis ordering
           # will be done.
           SCREAMING_SNAKE_CASE		=						key or (lambda lowerCamelCase__       : x)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    int       )     ->		int | None:




           '''simple docstring'''


           return int((i - 1) / 2       ) if i > 0 else None






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    int       )     ->		int | None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						int(2 * i + 1       )
           return left if 0 < left < self.size else None






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    int       )     ->		int | None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						int(2 * i + 2       )
           return right if 0 < right < self.size else None






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						(
               self.pos_map[self.arr[j][0]],
               self.pos_map[self.arr[i][0]],
           )
           # Then swap the items in the list.
           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						self.arr[j], self.arr[i]






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		bool:




           '''simple docstring'''


           return self.arr[i][1] < self.arr[j][1]






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._left(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						self._right(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						i

           if left is not None and not self._cmp(lowerCamelCase__						,lowerCamelCase__       ):
                 SCREAMING_SNAKE_CASE		=						left
           if right is not None and not self._cmp(lowerCamelCase__						,lowerCamelCase__       ):
                 SCREAMING_SNAKE_CASE		=						right

           return valid_parent






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._parent(lowerCamelCase__       )
           while parent is not None and not self._cmp(lowerCamelCase__						,lowerCamelCase__       ):
                 self._swap(lowerCamelCase__						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						parent, self._parent(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._get_valid_parent(lowerCamelCase__       )
           while valid_parent != index:
                 self._swap(lowerCamelCase__						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						valid_parent, self._get_valid_parent(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           if item not in self.pos_map:
                 return
           SCREAMING_SNAKE_CASE		=						self.pos_map[item]
           SCREAMING_SNAKE_CASE		=						[item, self.key(lowerCamelCase__       )]
           # Make sure heap is right in both up and down direction.
           # Ideally only one of them will make any change.
           self._heapify_up(lowerCamelCase__       )
           self._heapify_down(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           if item not in self.pos_map:
                 return
           SCREAMING_SNAKE_CASE		=						self.pos_map[item]
           del self.pos_map[item]
           SCREAMING_SNAKE_CASE		=						self.arr[self.size - 1]
           SCREAMING_SNAKE_CASE		=						index
           self.size -= 1
           # Make sure heap is right in both up and down direction. Ideally only one
           # of them will make any change- so no performance loss in calling both.
           if self.size > index:
                 self._heapify_up(lowerCamelCase__       )
                 self._heapify_down(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						len(self.arr       )
           if arr_len == self.size:
                 self.arr.append([item, self.key(lowerCamelCase__       )]       )
           else:
                 SCREAMING_SNAKE_CASE		=						[item, self.key(lowerCamelCase__       )]
           SCREAMING_SNAKE_CASE		=						self.size
           self.size += 1
           self._heapify_up(self.size - 1       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		tuple | None:




           '''simple docstring'''


           return self.arr[0] if self.size else None






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		tuple | None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_top()
           if top_item_tuple:
                 self.delete_item(top_item_tuple[0]       )
           return top_item_tuple



def    __lowercase   (      ) ->   None:


      '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()









import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

@skip_mps
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			TextToVideoSDPipeline
     __snake_case    :	int			=			TEXT_TO_IMAGE_PARAMS
     __snake_case    :	Tuple			=			TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     __snake_case    :	Dict			=			frozenset(
         [
             "num_inference_steps",
             "generator",
             "latents",
             "return_dict",
             "callback",
             "callback_steps",
         ]   )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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=128						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,hidden_act="""gelu"""						,projection_dim=512						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    int=0       )     ->		List[Any]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						sd_pipe.to(lowerCamelCase__       )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

           assert frames[0].shape == (64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		int:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=3e-3       )






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()						,reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,)
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=1e-2       )






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[Any]:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           pass






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Optional[Any]:




           '''simple docstring'''


           return super().test_progress_bar()

@slow
@skip_mps
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=25						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

           assert np.abs(expected_video - video       ).mean() < 5e-2






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

           assert np.abs(expected_video - video       ).mean() < 5e-2


