


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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    VideoToVideoSDPipeline
       A__      		=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}) - {"image", "width", "height"}
       A__      		=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}) - {"image"}
       A__      		=    PipelineTesterMixin.required_optional_params - {"latents"}
       A__      		=    False

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





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       str					=						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__	:       Optional[Any]					=						DDIMScheduler(
                 beta_start=0.0_0_0_8_5      ,   beta_end=0.0_1_2      ,   beta_schedule="scaled_linear"      ,   clip_sample=__lowerCamelCase      ,   set_alpha_to_one=__lowerCamelCase      ,   )
             torch.manual_seed(0 )
             lowerCamelCase__	:       Union[str, Any]					=						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 )
             lowerCamelCase__	:       Union[str, Any]					=						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      ,   )
             lowerCamelCase__	:       Optional[int]					=						CLIPTextModel(__lowerCamelCase )
             lowerCamelCase__	:       int					=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

             lowerCamelCase__	:       Optional[Any]					=						{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
             }
             return components





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Tuple=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						floats_tensor((1, 3, 3, 32, 32)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )

             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       str					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Optional[Any]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						{
                 "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							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"cpu"  # ensure determinism for the device-dependent torch.Generator
             lowerCamelCase__	:       Any					=						self.get_dummy_components()
             lowerCamelCase__	:       List[Any]					=						VideoToVideoSDPipeline(**__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       int					=						self.get_dummy_inputs(__lowerCamelCase )
             lowerCamelCase__	:       int					=						"np"
             lowerCamelCase__	:       Tuple					=						sd_pipe(**__lowerCamelCase ).frames
             lowerCamelCase__	:       Union[str, Any]					=						frames[0][-3:, -3:, -1]

             assert frames[0].shape == (32, 32, 3)
             lowerCamelCase__	:       int					=						np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )

             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							:    Dict ):


             '''simple docstring'''



             self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__lowerCamelCase      ,   expected_max_diff=5E-3 )





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


             '''simple docstring'''



             pass





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


             '''simple docstring'''



             pass





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


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return super().test_progress_bar()







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


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"      ,   torch_dtype=torch.floataa )
             pipe.enable_model_cpu_offload()

             # 10 frames
             lowerCamelCase__	:       Optional[int]					=						torch.Generator(device="cpu" ).manual_seed(0 )
             lowerCamelCase__	:       Optional[int]					=						torch.randn((1, 10, 3, 1024, 576)      ,   generator=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						video.to("cuda" )

             lowerCamelCase__	:       Union[str, Any]					=						"Spiderman is surfing"

             lowerCamelCase__	:       Optional[Any]					=						pipe(__lowerCamelCase      ,   video=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=3      ,   output_type="pt" ).frames

             lowerCamelCase__	:       Dict					=						np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6] )
             assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


