
"""simple docstring"""

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							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							int							      =	TextToVideoSDPipeline
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	TEXT_TO_IMAGE_PARAMS
      __SCREAMING_SNAKE_CASE   :							str							      =	TEXT_TO_IMAGE_BATCH_PARAMS
      # No `output_type`.
      __SCREAMING_SNAKE_CASE   :							Dict							      =	frozenset(
          [
              "num_inference_steps",
              "generator",
              "latents",
              "return_dict",
              "callback",
              "callback_steps",
          ]							)




      def     a_      (							self							)						->					Any:
          torch.manual_seed(0							)
          UpperCAmelCase									=					UNetaDConditionModel(
              block_out_channels=(3_2, 6_4, 6_4, 6_4)   ,       layers_per_block=2   ,       sample_size=3_2   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')   ,       up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')   ,       cross_attention_dim=3_2   ,       attention_head_dim=4   ,       )
          UpperCAmelCase									=					DDIMScheduler(
              beta_start=0.0_0_0_8_5   ,       beta_end=0.0_1_2   ,       beta_schedule='scaled_linear'   ,       clip_sample=lowercase_   ,       set_alpha_to_one=lowercase_   ,       )
          torch.manual_seed(0							)
          UpperCAmelCase									=					AutoencoderKL(
              block_out_channels=[3_2, 6_4]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,       latent_channels=4   ,       sample_size=1_2_8   ,       )
          torch.manual_seed(0							)
          UpperCAmelCase									=					CLIPTextConfig(
              bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=3_2   ,       intermediate_size=3_7   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_0_0_0   ,       hidden_act='gelu'   ,       projection_dim=5_1_2   ,       )
          UpperCAmelCase									=					CLIPTextModel(lowercase_							)
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

          UpperCAmelCase									=					{
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
          }
          return components




      def     a_      (							self   ,       lowercase_   ,       lowercase_=0							)						->					List[Any]:
          if str(lowercase_							).startswith('mps'							):
              UpperCAmelCase									=					torch.manual_seed(lowercase_							)
          else:
              UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
          UpperCAmelCase									=					{
              '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     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					'cpu'  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase									=					self.get_dummy_components()
          UpperCAmelCase									=					TextToVideoSDPipeline(**lowercase_							)
          UpperCAmelCase									=					sd_pipe.to(lowercase_							)
          sd_pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_							)
          UpperCAmelCase									=					'np'
          UpperCAmelCase									=					sd_pipe(**lowercase_							).frames
          UpperCAmelCase									=					frames[0][-3:, -3:, -1]

          assert frames[0].shape == (6_4, 6_4, 3)
          UpperCAmelCase									=					np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]							)

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




      def     a_      (							self							)						->					int:
          self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_   ,       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     a_      (							self							)						->					Optional[int]:
          self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_   ,       expected_max_diff=1E-2							)




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




      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'							)
      def     a_      (							self							)						->					Optional[int]:
          pass




      @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'							)
      def     a_      (							self							)						->					str:
          pass




      def     a_      (							self							)						->					Tuple:
          return super().test_progress_bar()

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




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'							)

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

          UpperCAmelCase									=					'Spiderman is surfing'
          UpperCAmelCase									=					torch.Generator(device='cpu'							).manual_seed(0							)

          UpperCAmelCase									=					pipe(lowercase_   ,       generator=lowercase_   ,       num_inference_steps=2_5   ,       output_type='pt'							).frames
          UpperCAmelCase									=					video_frames.cpu().numpy()

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




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'							)

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

          UpperCAmelCase									=					'Spiderman is surfing'
          UpperCAmelCase									=					torch.Generator(device='cpu'							).manual_seed(0							)

          UpperCAmelCase									=					pipe(lowercase_   ,       generator=lowercase_   ,       num_inference_steps=2   ,       output_type='pt'							).frames
          UpperCAmelCase									=					video_frames.cpu().numpy()

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





"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"encoder-decoder"
      __SCREAMING_SNAKE_CASE   :							Any							      =	True




      def __init__(							self   ,       **lowercase_							)						->					str:
          super().__init__(**lowercase_							)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          UpperCAmelCase									=					kwargs.pop('encoder'							)
          UpperCAmelCase									=					encoder_config.pop('model_type'							)
          UpperCAmelCase									=					kwargs.pop('decoder'							)
          UpperCAmelCase									=					decoder_config.pop('model_type'							)

          from ..auto.configuration_auto import AutoConfig

          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					True




      @classmethod
      def     a_      (							cls   ,       lowercase_   ,       lowercase_   ,       **lowercase_							)						->					PretrainedConfig:
          logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
          UpperCAmelCase									=					True
          UpperCAmelCase									=					True

          return cls(encoder=encoder_config.to_dict()   ,       decoder=decoder_config.to_dict()   ,       **lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.encoder.to_dict()
          UpperCAmelCase									=					self.decoder.to_dict()
          UpperCAmelCase									=					self.__class__.model_type
          return output



