




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     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Optional[Any]    		=   VideoToVideoSDPipeline
      UpperCAmelCase__      :	Tuple    		=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'}) - {'image', 'width', 'height'}
      UpperCAmelCase__      :	Dict    		=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'}) - {'image'}
      UpperCAmelCase__      :	Optional[int]    		=   PipelineTesterMixin.required_optional_params - {'latents'}
      UpperCAmelCase__      :	Any    		=   False

      # No `output_type`.
      UpperCAmelCase__      :	Optional[Any]    		=   frozenset(
          [
              'num_inference_steps',
              'generator',
              'latents',
              'return_dict',
              'callback',
              'callback_steps',
          ])
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						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    ,       )
             __lowerCamelCase   =						DDIMScheduler(
                 beta_start=0.0_0085    ,       beta_end=0.012    ,       beta_schedule="""scaled_linear"""    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       )
             torch.manual_seed(0   )
             __lowerCamelCase   =						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   )
             __lowerCamelCase   =						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    ,       )
             __lowerCamelCase   =						CLIPTextModel(UpperCamelCase_   )
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             __lowerCamelCase   =						{
                 """unet""": unet,
                 """scheduler""": scheduler,
                 """vae""": vae,
                 """text_encoder""": text_encoder,
                 """tokenizer""": tokenizer,
             }
             return components
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Dict=0   ):
             # 3 frames
             __lowerCamelCase   =						floats_tensor((1, 3, 3, 32, 32)    ,       rng=random.Random(UpperCamelCase_   )   ).to(UpperCamelCase_   )

             if str(UpperCamelCase_   ).startswith("""mps"""   ):
                    __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						{
                 """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 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.get_dummy_components()
             __lowerCamelCase   =						VideoToVideoSDPipeline(**UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

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

             assert frames[0].shape == (32, 32, 3)
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Any   ):
             self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCamelCase_    ,       expected_max_diff=5E-3   )
      @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             pass
      @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             pass
      @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             pass




      def 			lowerCAmelCase__						(				self: int   ):
             return super().test_progress_bar()





@slow
@skip_mps
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""    ,       torch_dtype=torch.floataa   )
             pipe.enable_model_cpu_offload()

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

             __lowerCamelCase   =						"""Spiderman is surfing"""

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

             __lowerCamelCase   =						np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656]   )
             assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array   ).sum() < 1E-2








'''simple docstring'''




from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




 def      lowercase	(					self       :    List[Any]    ):
      for p in range(self.N - 1	,			0	,			-1    ):
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


if __name__ == "__main__":
  from functools import reduce

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

  for index, value in test_updates.items():
    __SCREAMING_SNAKE_CASE     :str       =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()




