
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     UpperCAmelCase						(		A_			,unittest.TestCase					):
   A__   :						Optional[int]			=	VideoToVideoSDPipeline
   A__   :						Optional[Any]			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}					) - {"image", "width", "height"}
   A__   :						Any			=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}					) - {"image"}
   A__   :						Dict			=	PipelineTesterMixin.required_optional_params - {"latents"}
   A__   :						Dict			=	False

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



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


          '''simple docstring'''


          torch.manual_seed(0					)
          snake_case			:  List[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       ,		)
          snake_case			:  Any     							= DDIMScheduler(
              beta_start=0.00085       ,		beta_end=0.012       ,		beta_schedule="scaled_linear"       ,		clip_sample=snake_case__       ,		set_alpha_to_one=snake_case__       ,		)
          torch.manual_seed(0					)
          snake_case			:  List[str]     							= 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					)
          snake_case			:  Any     							= 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       ,		)
          snake_case			:  Any     							= CLIPTextModel(snake_case__					)
          snake_case			:  Dict     							= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   Dict       ,		snake_case__						:   Optional[int]=0					)						->      str:


          '''simple docstring'''


          snake_case			:  Tuple     							= floats_tensor((1, 3, 3, 32, 32)       ,		rng=random.Random(snake_case__					)					).to(snake_case__					)

          if str(snake_case__					).startswith("mps"					):
                 snake_case			:  List[str]     							= torch.manual_seed(snake_case__					)
          else:
                 snake_case			:  Any     							= torch.Generator(device=snake_case__					).manual_seed(snake_case__					)
          snake_case			:  Any     							= {
              "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 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Tuple     							= "cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case			:  Optional[Any]     							= self.get_dummy_components()
          snake_case			:  Any     							= VideoToVideoSDPipeline(**snake_case__					)
          snake_case			:  List[str]     							= sd_pipe.to(snake_case__					)
          sd_pipe.set_progress_bar_config(disable=snake_case__					)

          snake_case			:  Union[str, Any]     							= self.get_dummy_inputs(snake_case__					)
          snake_case			:  Optional[int]     							= "np"
          snake_case			:  List[str]     							= sd_pipe(**snake_case__					).frames
          snake_case			:  Union[str, Any]     							= frames[0][-3:, -3:, -1]

          assert frames[0].shape == (32, 32, 3)
          snake_case			:  Optional[int]     							= 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 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      List[str]:


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case__       ,		expected_max_diff=5e-3					)



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          return super().test_progress_bar()






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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"       ,		torch_dtype=torch.floataa					)
          pipe.enable_model_cpu_offload()

          # 10 frames
          snake_case			:  Optional[int]     							= torch.Generator(device="cpu"					).manual_seed(0					)
          snake_case			:  Dict     							= torch.randn((1, 10, 3, 10_24, 5_76)       ,		generator=snake_case__					)
          snake_case			:  Optional[Any]     							= video.to("cuda"					)

          snake_case			:  str     							= "Spiderman is surfing"

          snake_case			:  str     							= pipe(snake_case__       ,		video=snake_case__       ,		generator=snake_case__       ,		num_inference_steps=3       ,		output_type="pt"					).frames

          snake_case			:  Dict     							= np.array([-1.0458984, -1.1279297, -0.9663086, -0.91503906, -0.75097656]					)
          assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array					).sum() < 1e-2







import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCamelCase   (       __lowerCamelCase	:					int = 8       ):
       snake_case			:  int     							= ascii_letters + digits + punctuation
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(__lowerCamelCase       )
       snake_case			:  Any     							= i // 3
       snake_case			:  Optional[int]     							= i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       snake_case			:  Tuple     							= (
           chars_incl
           + random(__lowerCamelCase							,   quotient + remainder       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
           + random(__lowerCamelCase							,   __lowerCamelCase       )
       )
       snake_case			:  Optional[Any]     							= list(__lowerCamelCase       )
       shuffle(__lowerCamelCase       )
       return "".join(__lowerCamelCase       )

       # random is a generalised function for letters, characters and numbers


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int       ):
       return "".join(secrets.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					List[str]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					Any							,   __lowerCamelCase	:					List[Any]       ):
       pass  # Put your code here...


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					int = 8       ):

       if len(__lowerCamelCase       ) < min_length:
              # Your Password must be at least 8 characters long
              return False

       snake_case			:  Dict     							= any(char in ascii_uppercase for char in password       )
       snake_case			:  Optional[int]     							= any(char in ascii_lowercase for char in password       )
       snake_case			:  str     							= any(char in digits for char in password       )
       snake_case			:  str     							= any(char in punctuation for char in password       )

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters


def      UpperCamelCase   (       ):
       snake_case			:  int     							= int(input("Please indicate the max length of your password: "       ).strip()       )
       snake_case			:  Union[str, Any]     							= input(
           "Please indicate the characters that must be in your password: "       ).strip()
       print("Password generated:"							,   password_generator(__lowerCamelCase       )       )
       print(
           "Alternative Password generated:"							,   alternative_password_generator(__lowerCamelCase							,   __lowerCamelCase       )							,   )
       print("[If you are thinking of using this passsword, You better save it.]"       )


if __name__ == "__main__":
   main()





