

import copy
import os
from typing import Union

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


lowerCAmelCase     :   Any           =  logging.get_logger(__name__)

lowerCAmelCase     :   Tuple           =  {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      int       =  '''bridgetower_vision_model'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE=288 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1e-05 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  str           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  Tuple           =     num_channels
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     patch_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     image_size
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     initializer_factor
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  Tuple           =     stop_gradient
        SCREAMING_SNAKE_CASE_				:  Any           =     share_layernorm
        SCREAMING_SNAKE_CASE_				:  str           =     remove_last_layer



  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     cls.get_config_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        if config_dict.get('model_type'					) == "bridgetower":
              SCREAMING_SNAKE_CASE_				:  str           =     config_dict['text_config']

        if "model_type" in config_dict and hasattr(cls ,					'model_type'					) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                  f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

        return cls.from_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  '''bridgetower_text_model'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=5_0265 ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=514 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1e-05 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE="absolute" ,					_SCREAMING_SNAKE_CASE=True ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     vocab_size
        SCREAMING_SNAKE_CASE_				:  Any           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  Tuple           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  Tuple           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     initializer_factor
        SCREAMING_SNAKE_CASE_				:  List[str]           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  Tuple           =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     max_position_embeddings
        SCREAMING_SNAKE_CASE_				:  Tuple           =     type_vocab_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  int           =     position_embedding_type
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     use_cache
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pad_token_id
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     bos_token_id
        SCREAMING_SNAKE_CASE_				:  str           =     eos_token_id



  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     cls.get_config_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        if config_dict.get('model_type'					) == "bridgetower":
              SCREAMING_SNAKE_CASE_				:  Dict           =     config_dict['text_config']

        if "model_type" in config_dict and hasattr(cls ,					'model_type'					) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                  f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

        return cls.from_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Tuple       =  '''bridgetower'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1e-05 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="add" ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     kwargs.pop('text_config_dict' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     kwargs.pop('vision_config_dict' ,					_SCREAMING_SNAKE_CASE					)

        super().__init__(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     share_cross_modal_transformer_layers
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  List[str]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     initializer_factor
        SCREAMING_SNAKE_CASE_				:  int           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  int           =     share_link_tower_layers
        SCREAMING_SNAKE_CASE_				:  int           =     link_tower_type
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  Any           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  int           =     tie_word_embeddings
        SCREAMING_SNAKE_CASE_				:  int           =     init_layernorm_from_vision_encoder

        if text_config is None:
              SCREAMING_SNAKE_CASE_				:  int           =     {}
              logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'					)

        if vision_config is None:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {}
              logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'					)

        SCREAMING_SNAKE_CASE_				:  Any           =     BridgeTowerTextConfig(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     BridgeTowerVisionConfig(**_SCREAMING_SNAKE_CASE					)

  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return cls(text_config=text_config.to_dict() ,					vision_config=vision_config.to_dict() ,					**_SCREAMING_SNAKE_CASE					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     copy.deepcopy(self.__dict__					)
        SCREAMING_SNAKE_CASE_				:  int           =     self.text_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.vision_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.__class__.model_type
        return output









import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     UNetaDModel(
            sample_size=(32, 64) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     UNetaDConditionModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,					cross_attention_dim=10 ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoencoderKL(
            sample_size=(128, 64) ,					in_channels=1 ,					out_channels=1 ,					latent_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     UNetaDModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return vqvae, unet



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Any           =     Mel(
            x_res=self.dummy_unet.config.sample_size[1] ,					y_res=self.dummy_unet.config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_				:  str           =     AudioDiffusionPipeline(vqvae=_SCREAMING_SNAKE_CASE ,					unet=self.dummy_unet ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4 ,					return_dict=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     output[0][0]

        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
        assert (
            image.height == self.dummy_unet.config.sample_size[0]
            and image.width == self.dummy_unet.config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image_from_tuple.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     Mel(
            x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  str           =     DDIMScheduler()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_vqvae_and_unet
        SCREAMING_SNAKE_CASE_				:  int           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=dummy_vqvae_and_unet[1] ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.random.uniform(-1 ,					1 ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     pipe(raw_audio=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					start_step=5 ,					steps=10					)
        SCREAMING_SNAKE_CASE_				:  int           =     output.images[0]

        assert (
            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  int           =     np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_unet_condition
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=_SCREAMING_SNAKE_CASE ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.rand((1, 1, 10)					)
        SCREAMING_SNAKE_CASE_				:  Any           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					encoding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch_device

        SCREAMING_SNAKE_CASE_				:  str           =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     output.images[0]

        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0






