






'''simple docstring'''







import copy
import os
from typing import Union

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


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {
    '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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"bridgetower_vision_model"






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=768  ,						__SCREAMING_SNAKE_CASE      :			Tuple=12  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=3  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=16  ,						__SCREAMING_SNAKE_CASE      :			List[str]=288  ,						__SCREAMING_SNAKE_CASE      :			Dict=1  ,						__SCREAMING_SNAKE_CASE      :			Tuple=1e-05  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=True  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=False  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]  ,						)			->			int:
							super().__init__(**__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      hidden_size
							a_		:  int											=      num_hidden_layers
							a_		:  Optional[Any]											=      num_channels
							a_		:  List[Any]											=      patch_size
							a_		:  int											=      image_size
							a_		:  Optional[Any]											=      initializer_factor
							a_		:  Optional[int]											=      layer_norm_eps
							a_		:  Optional[int]											=      stop_gradient
							a_		:  Optional[int]											=      share_layernorm
							a_		:  int											=      remove_last_layer







			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, os.PathLike]  ,						**__SCREAMING_SNAKE_CASE      :			str					)			->			"PretrainedConfig":
							a_				,			a_		:  Tuple											=      cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

							if config_dict.get('''model_type'''					) == "bridgetower":
											a_		:  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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"bridgetower_text_model"






			def __init__( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Tuple=5_0265  ,						__SCREAMING_SNAKE_CASE      :			Any=768  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=12  ,						__SCREAMING_SNAKE_CASE      :			int=12  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=1  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=3072  ,						__SCREAMING_SNAKE_CASE      :			List[Any]="gelu"  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0.1  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.1  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=514  ,						__SCREAMING_SNAKE_CASE      :			str=1  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=1e-05  ,						__SCREAMING_SNAKE_CASE      :			Tuple=1  ,						__SCREAMING_SNAKE_CASE      :			Dict=0  ,						__SCREAMING_SNAKE_CASE      :			Tuple=2  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]="absolute"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=True  ,						**__SCREAMING_SNAKE_CASE      :			str  ,						)			->			Optional[Any]:
							super().__init__(**__SCREAMING_SNAKE_CASE					)

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







			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, os.PathLike]  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			"PretrainedConfig":
							a_				,			a_		:  Optional[int]											=      cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

							if config_dict.get('''model_type'''					) == "bridgetower":
											a_		:  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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"bridgetower"






			def __init__( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Tuple=True  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]="gelu"  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=768  ,						__SCREAMING_SNAKE_CASE      :			int=1  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=1e-05  ,						__SCREAMING_SNAKE_CASE      :			int=False  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]="add"  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=12  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=6  ,						__SCREAMING_SNAKE_CASE      :			Tuple=False  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=None  ,						__SCREAMING_SNAKE_CASE      :			Tuple=None  ,						**__SCREAMING_SNAKE_CASE      :			int  ,						)			->			Dict:
							# TODO: remove this once the Hub files are updated.
							a_		:  List[Any]											=      kwargs.pop('''text_config_dict'''  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      kwargs.pop('''vision_config_dict'''  ,						__SCREAMING_SNAKE_CASE					)

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

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

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

							a_		:  int											=      BridgeTowerTextConfig(**__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      BridgeTowerVisionConfig(**__SCREAMING_SNAKE_CASE					)






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			BridgeTowerTextConfig  ,						__SCREAMING_SNAKE_CASE      :			BridgeTowerVisionConfig  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			int:
							return cls(text_config=text_config.to_dict()  ,						vision_config=vision_config.to_dict()  ,						**__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							a_		:  Optional[Any]											=      copy.deepcopy(self.__dict__					)
							a_		:  List[str]											=      self.text_config.to_dict()
							a_		:  Optional[int]											=      self.vision_config.to_dict()
							a_		:  Optional[int]											=      self.__class__.model_type
							return output












'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Tuple											=      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 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							torch.manual_seed(0					)
							a_		:  List[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''')  ,						)
							a_		:  List[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:  Union[str, Any]											=      Mel(
							    x_res=self.dummy_unet.config.sample_size[1]  ,						y_res=self.dummy_unet.config.sample_size[0]  ,						)

							a_		:  Any											=      DDPMScheduler()
							a_		:  str											=      AudioDiffusionPipeline(vqvae=__SCREAMING_SNAKE_CASE  ,						unet=self.dummy_unet  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4					)
							a_		:  List[Any]											=      output.audios[0]
							a_		:  Dict											=      output.images[0]

							a_		:  Dict											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  Optional[Any]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, 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]
							)
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  str											=      np.frombuffer(image_from_tuple.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      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

							a_		:  str											=      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]  ,						)

							a_		:  int											=      DDIMScheduler()
							a_		:  Dict											=      self.dummy_vqvae_and_unet
							a_		:  List[str]											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  List[str]											=      np.random.uniform(-1  ,						1  ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
							a_		:  int											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  int											=      pipe(raw_audio=__SCREAMING_SNAKE_CASE  ,						generator=__SCREAMING_SNAKE_CASE  ,						start_step=5  ,						steps=10					)
							a_		:  List[str]											=      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]
							)
							a_		:  Optional[Any]											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

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

							a_		:  List[str]											=      self.dummy_unet_condition
							a_		:  Dict											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=__SCREAMING_SNAKE_CASE  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  Any											=      torch.rand((1, 1, 10)					)
							a_		:  Tuple											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						encoding=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.images[0]
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Any											=      torch_device

							a_		:  Optional[int]											=      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''					)
							a_		:  Dict											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.audios[0]
							a_		:  Tuple											=      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]
							a_		:  str											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  Tuple											=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

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




