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			:   Tuple							):
   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=__SCREAMING_SNAKE_CASE  ,				set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,				)
   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(__SCREAMING_SNAKE_CASE							)
   __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			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0							):
   # 3 frames
   __a							=	floats_tensor((1, 3, 3, 32, 32)  ,				rng=random.Random(__SCREAMING_SNAKE_CASE							)							).to(__SCREAMING_SNAKE_CASE							)

   if str(__SCREAMING_SNAKE_CASE							).startswith("mps"							):
    __a							=	torch.manual_seed(__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)
   __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[int]							):
   __a							=	"cpu"  # ensure determinism for the device-dependent torch.Generator
   __a							=	self.get_dummy_components()
   __a							=	VideoToVideoSDPipeline(**__SCREAMING_SNAKE_CASE							)
   __a							=	sd_pipe.to(__SCREAMING_SNAKE_CASE							)
   sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
   __a							=	"np"
   __a							=	sd_pipe(**__SCREAMING_SNAKE_CASE							).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			:   Optional[int]							):
   self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE  ,				expected_max_diff=5E-3							)

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

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

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

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return super().test_progress_bar()
@slow
@skip_mps
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   List[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=__SCREAMING_SNAKE_CASE							)
   __a							=	video.to("cuda"							)

   __a							=	"Spiderman is surfing"

   __a							=	pipe(__SCREAMING_SNAKE_CASE  ,				video=__SCREAMING_SNAKE_CASE  ,				generator=__SCREAMING_SNAKE_CASE  ,				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



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""T5Config"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig


