'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a		:  str   			=			logging.get_logger(__name__)




class     UpperCamelCase					(  snake_case__					):
  a    :Union[str, Any]      					=				'speech-encoder-decoder'
  a    :List[str]      					=				True



  def __init__( self		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      super().__init__(**__snake_case   )
      if "encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError(
              f'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
              f''' `decoder` sub-configurations are passed, but only {kwargs}'''   )

      _a							:       List[str]       					=			kwargs.pop("""encoder"""   )
      _a							:       str       					=			encoder_config.pop("""model_type"""   )
      _a							:       Optional[Any]       					=			kwargs.pop("""decoder"""   )
      _a							:       str       					=			decoder_config.pop("""model_type"""   )

      _a							:       int       					=			AutoConfig.for_model(__snake_case		,    **__snake_case   )
      _a							:       int       					=			AutoConfig.for_model(__snake_case		,    **__snake_case   )
      _a							:       Tuple       					=			True



  @classmethod
  def 						a_       ( cls		,    __lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""   )
      _a							:       Optional[Any]       					=			True
      _a							:       str       					=			True

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



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Tuple       					=			copy.deepcopy(self.__dict__   )
      _a							:       str       					=			self.encoder.to_dict()
      _a							:       Optional[int]       					=			self.decoder.to_dict()
      _a							:       Any       					=			self.__class__.model_type
      return output








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class     UpperCamelCase					(  unittest.TestCase					):



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			"""runwayml/stable-diffusion-v1-5"""
      _a							:       Tuple       					=			TextToVideoZeroPipeline.from_pretrained(__lowerCAmelCase		,    torch_dtype=torch.floataa   ).to("""cuda"""   )
      _a							:       Dict       					=			DDIMScheduler.from_config(pipe.scheduler.config   )
      _a							:       int       					=			torch.Generator(device="""cuda"""   ).manual_seed(0   )

      _a							:       Union[str, Any]       					=			"""A bear is playing a guitar on Times Square"""
      _a							:       List[Any]       					=			pipe(prompt=__lowerCAmelCase		,    generator=__lowerCAmelCase   ).images

      _a							:       Any       					=			load_pt(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"""   )

      assert_mean_pixel_difference(__lowerCAmelCase		,    __lowerCAmelCase   )


