



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 				snake_case__(_UpperCamelCase    ,      _UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		AutoencoderKL
					lowercase_								=		"""sample"""
					lowercase_								=		1e-2







					@property
					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   str													=					4
									lowercase__					:   Union[str, Any]													=					3
									lowercase__					:   Union[str, Any]													=					(32, 32)

									lowercase__					:   List[str]													=					floats_tensor((batch_size, num_channels) + sizes					).to(SCREAMING_SNAKE_CASE					)

									return {"sample": image}







					@property
					def 			snake_case					(							self :     Optional[Any]					):
									return (3, 32, 32)







					@property
					def 			snake_case					(							self :     Tuple					):
									return (3, 32, 32)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[Any]													=					{
									    "block_out_channels": [32, 64],
									    "in_channels": 3,
									    "out_channels": 3,
									    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
									    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "latent_channels": 4,
									}
									lowercase__					:   Tuple													=					self.dummy_input
									return init_dict, inputs_dict







					def 			snake_case					(							self :     List[Any]					):
									pass







					def 			snake_case					(							self :     Any					):
									pass







					@unittest.skipIf(torch_device == "mps"				,       "Gradient checkpointing skipped on MPS"					)
					def 			snake_case					(							self :     Tuple					):
									# enable deterministic behavior for gradient checkpointing
									lowercase__					:   Optional[int]													=					self.prepare_init_args_and_inputs_for_common()
									lowercase__					:   List[str]													=					self.model_class(**SCREAMING_SNAKE_CASE					)
									model.to(SCREAMING_SNAKE_CASE					)

									assert not model.is_gradient_checkpointing and model.training

									lowercase__					:   Tuple													=					model(**SCREAMING_SNAKE_CASE					).sample
									# run the backwards pass on the model. For backwards pass, for simplicity purpose,
									# we won't calculate the loss and rather backprop on out.sum()
									model.zero_grad()

									lowercase__					:   Optional[Any]													=					torch.randn_like(SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					(out - labels).mean()
									loss.backward()

									# re-instantiate the model now enabling gradient checkpointing
									lowercase__					:   Tuple													=					self.model_class(**SCREAMING_SNAKE_CASE					)
									# clone model
									model_a.load_state_dict(model.state_dict()					)
									model_a.to(SCREAMING_SNAKE_CASE					)
									model_a.enable_gradient_checkpointing()

									assert model_a.is_gradient_checkpointing and model_a.training

									lowercase__					:   Dict													=					model_a(**SCREAMING_SNAKE_CASE					).sample
									# run the backwards pass on the model. For backwards pass, for simplicity purpose,
									# we won't calculate the loss and rather backprop on out.sum()
									model_a.zero_grad()
									lowercase__					:   Tuple													=					(out_a - labels).mean()
									loss_a.backward()

									# compare the output and parameters gradients
									self.assertTrue((loss - loss_a).abs() < 1E-5					)
									lowercase__					:   Union[str, Any]													=					dict(model.named_parameters()					)
									lowercase__					:   Any													=					dict(model_a.named_parameters()					)
									for name, param in named_params.items():
													self.assertTrue(torch_all_close(param.grad.data				,       named_params_a[name].grad.data				,       atol=5E-5					)					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Tuple													=					AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"				,       output_loading_info=SCREAMING_SNAKE_CASE					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE					)
									self.assertEqual(len(loading_info["missing_keys"]					)				,       0					)

									model.to(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					model(**self.dummy_input					)

									assert image is not None, "Make sure output is not None"







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[Any]													=					AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"					)
									lowercase__					:   Dict													=					model.to(SCREAMING_SNAKE_CASE					)
									model.eval()

									if torch_device == "mps":
													lowercase__					:   List[Any]													=					torch.manual_seed(0					)
									else:
													lowercase__					:   Optional[Any]													=					torch.Generator(device=SCREAMING_SNAKE_CASE					).manual_seed(0					)

									lowercase__					:   Tuple													=					torch.randn(
									    1				,       model.config.in_channels				,       model.config.sample_size				,       model.config.sample_size				,       generator=torch.manual_seed(0					)				,       )
									lowercase__					:   Optional[int]													=					image.to(SCREAMING_SNAKE_CASE					)
									with torch.no_grad():
													lowercase__					:   int													=					model(SCREAMING_SNAKE_CASE				,       sample_posterior=SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE					).sample

									lowercase__					:   Optional[int]													=					output[0, -1, -3:, -3:].flatten().cpu()

									# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
									# the expected output slices are not the same for CPU and GPU.
									if torch_device == "mps":
													lowercase__					:   Any													=					torch.tensor(
													    [
													        -4.0_0_7_8E-0_1,
													        -3.8_3_2_3E-0_4,
													        -1.2_6_8_1E-0_1,
													        -1.1_4_6_2E-0_1,
													        2.0_0_9_5E-0_1,
													        1.0_8_9_3E-0_1,
													        -8.8_2_4_7E-0_2,
													        -3.0_3_6_1E-0_1,
													        -9.8_6_4_4E-0_3,
													    ]					)
									elif torch_device == "cpu":
													lowercase__					:   Union[str, Any]													=					torch.tensor(
													    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]					)
									else:
													lowercase__					:   List[str]													=					torch.tensor(
													    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]					)

									self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       rtol=1E-2					)					)






@slow
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Dict					):
									return f"""gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE					) for s in shape]					)}.npy"""







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







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=0				,       SCREAMING_SNAKE_CASE :     Optional[Any]=(4, 3, 512, 512)				,       SCREAMING_SNAKE_CASE :     Any=False					):
									lowercase__					:   List[Any]													=					torch.floataa if fpaa else torch.floataa
									lowercase__					:   Tuple													=					torch.from_numpy(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)					)					).to(SCREAMING_SNAKE_CASE					).to(SCREAMING_SNAKE_CASE					)
									return image







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     int="CompVis/stable-diffusion-v1-4"				,       SCREAMING_SNAKE_CASE :     str=False					):
									lowercase__					:   Tuple													=					"fp16" if fpaa else None
									lowercase__					:   List[str]													=					torch.floataa if fpaa else torch.floataa

									lowercase__					:   Any													=					AutoencoderKL.from_pretrained(
									    SCREAMING_SNAKE_CASE				,       subfolder="vae"				,       torch_dtype=SCREAMING_SNAKE_CASE				,       revision=SCREAMING_SNAKE_CASE				,       )
									model.to(SCREAMING_SNAKE_CASE					).eval()

									return model







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     List[Any]=0					):
									if torch_device == "mps":
													return torch.manual_seed(SCREAMING_SNAKE_CASE					)
									return torch.Generator(device=SCREAMING_SNAKE_CASE					).manual_seed(SCREAMING_SNAKE_CASE					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
					        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
					        # fmt: on
					    ]					)
					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict					):
									lowercase__					:   int													=					self.get_sd_vae_model()
									lowercase__					:   Dict													=					self.get_sd_image(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[str]													=					self.get_generator(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   Optional[Any]													=					model(SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE				,       sample_posterior=SCREAMING_SNAKE_CASE					).sample

									assert sample.shape == image.shape

									lowercase__					:   Dict													=					sample[-1, -2:, -2:, :2].flatten().float().cpu()
									lowercase__					:   List[Any]													=					torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=3E-3					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
					        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
					        # fmt: on
					    ]					)
					@require_torch_gpu
					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     str					):
									lowercase__					:   Any													=					self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					self.get_generator(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   str													=					model(SCREAMING_SNAKE_CASE				,       generator=SCREAMING_SNAKE_CASE				,       sample_posterior=SCREAMING_SNAKE_CASE					).sample

									assert sample.shape == image.shape

									lowercase__					:   List[str]													=					sample[-1, -2:, :2, -2:].flatten().float().cpu()
									lowercase__					:   Any													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-2					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
					        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
					        # fmt: on
					    ]					)
					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Optional[Any]													=					self.get_sd_vae_model()
									lowercase__					:   Tuple													=					self.get_sd_image(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   Union[str, Any]													=					model(SCREAMING_SNAKE_CASE					).sample

									assert sample.shape == image.shape

									lowercase__					:   List[str]													=					sample[-1, -2:, -2:, :2].flatten().float().cpu()
									lowercase__					:   Tuple													=					torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=3E-3					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
					        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
					        # fmt: on
					    ]					)
					@require_torch_gpu
					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   List[str]													=					self.get_sd_vae_model()
									lowercase__					:   List[str]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)					)

									with torch.no_grad():
													lowercase__					:   Dict													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									lowercase__					:   List[Any]													=					sample[-1, -2:, :2, -2:].flatten().cpu()
									lowercase__					:   List[Any]													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-3					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
					        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
					        # fmt: on
					    ]					)
					@require_torch_gpu
					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   str													=					self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)				,       fpaa=SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									lowercase__					:   int													=					sample[-1, -2:, :2, -2:].flatten().float().cpu()
									lowercase__					:   str													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=5E-3					)







					@parameterized.expand([(13,), (16,), (27,)]					)
					@require_torch_gpu
					@unittest.skipIf(not is_xformers_available()				,       reason="xformers is not required when using PyTorch 2.0."					)
					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Tuple					):
									lowercase__					:   int													=					self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)				,       fpaa=SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
													lowercase__					:   Optional[int]													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-1					)







					@parameterized.expand([(13,), (16,), (37,)]					)
					@require_torch_gpu
					@unittest.skipIf(not is_xformers_available()				,       reason="xformers is not required when using PyTorch 2.0."					)
					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   List[str]													=					self.get_sd_vae_model()
									lowercase__					:   Optional[int]													=					self.get_sd_image(SCREAMING_SNAKE_CASE				,       shape=(3, 4, 64, 64)					)

									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									model.enable_xformers_memory_efficient_attention()
									with torch.no_grad():
													lowercase__					:   int													=					model.decode(SCREAMING_SNAKE_CASE					).sample

									assert list(sample.shape					) == [3, 3, 512, 512]

									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-2					)







					@parameterized.expand(
					    [
					        # fmt: off
					        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
					        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
					        # fmt: on
					    ]					)
					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Optional[int]													=					self.get_sd_vae_model()
									lowercase__					:   Tuple													=					self.get_sd_image(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					self.get_generator(SCREAMING_SNAKE_CASE					)

									with torch.no_grad():
													lowercase__					:   Any													=					model.encode(SCREAMING_SNAKE_CASE					).latent_dist
													lowercase__					:   Optional[int]													=					dist.sample(generator=SCREAMING_SNAKE_CASE					)

									assert list(sample.shape					) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

									lowercase__					:   Dict													=					sample[0, -1, -3:, -3:].flatten().cpu()
									lowercase__					:   Optional[Any]													=					torch.tensor(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Any													=					3E-3 if torch_device != "mps" else 1E-2
									assert torch_all_close(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=SCREAMING_SNAKE_CASE					)









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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""bridgetower_vision_model"""







					def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     Dict=768				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=12				,       SCREAMING_SNAKE_CASE :     Dict=3				,       SCREAMING_SNAKE_CASE :     Dict=16				,       SCREAMING_SNAKE_CASE :     List[Any]=288				,       SCREAMING_SNAKE_CASE :     List[Any]=1				,       SCREAMING_SNAKE_CASE :     List[str]=1E-0_5				,       SCREAMING_SNAKE_CASE :     str=False				,       SCREAMING_SNAKE_CASE :     Any=True				,       SCREAMING_SNAKE_CASE :     Tuple=False				,       **SCREAMING_SNAKE_CASE :     List[str]				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					hidden_size
									lowercase__					:   str													=					num_hidden_layers
									lowercase__					:   str													=					num_channels
									lowercase__					:   Optional[int]													=					patch_size
									lowercase__					:   Dict													=					image_size
									lowercase__					:   List[Any]													=					initializer_factor
									lowercase__					:   int													=					layer_norm_eps
									lowercase__					:   List[str]													=					stop_gradient
									lowercase__					:   Optional[int]													=					share_layernorm
									lowercase__					:   Optional[int]													=					remove_last_layer







					@classmethod
					def 			snake_case					(							cls :     Tuple				,       SCREAMING_SNAKE_CASE :     Union[str, os.PathLike]				,       **SCREAMING_SNAKE_CASE :     str					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					cls.get_config_dict(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

									if config_dict.get("model_type"					) == "bridgetower":
													lowercase__					:   Optional[int]													=					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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""bridgetower_text_model"""







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=50_265				,       SCREAMING_SNAKE_CASE :     Optional[int]=768				,       SCREAMING_SNAKE_CASE :     List[str]=12				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=12				,       SCREAMING_SNAKE_CASE :     List[Any]=1				,       SCREAMING_SNAKE_CASE :     Any=3_072				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="gelu"				,       SCREAMING_SNAKE_CASE :     Optional[int]=0.1				,       SCREAMING_SNAKE_CASE :     List[str]=0.1				,       SCREAMING_SNAKE_CASE :     Tuple=514				,       SCREAMING_SNAKE_CASE :     List[str]=1				,       SCREAMING_SNAKE_CASE :     Dict=1E-0_5				,       SCREAMING_SNAKE_CASE :     List[str]=1				,       SCREAMING_SNAKE_CASE :     str=0				,       SCREAMING_SNAKE_CASE :     List[Any]=2				,       SCREAMING_SNAKE_CASE :     Optional[Any]="absolute"				,       SCREAMING_SNAKE_CASE :     int=True				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)

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







					@classmethod
					def 			snake_case					(							cls :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Union[str, os.PathLike]				,       **SCREAMING_SNAKE_CASE :     str					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					cls.get_config_dict(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

									if config_dict.get("model_type"					) == "bridgetower":
													lowercase__					:   List[Any]													=					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 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""bridgetower"""







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]=True				,       SCREAMING_SNAKE_CASE :     Dict="gelu"				,       SCREAMING_SNAKE_CASE :     Any=768				,       SCREAMING_SNAKE_CASE :     Dict=1				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=1E-0_5				,       SCREAMING_SNAKE_CASE :     Tuple=False				,       SCREAMING_SNAKE_CASE :     List[Any]="add"				,       SCREAMING_SNAKE_CASE :     Any=12				,       SCREAMING_SNAKE_CASE :     Any=6				,       SCREAMING_SNAKE_CASE :     Any=False				,       SCREAMING_SNAKE_CASE :     List[Any]=False				,       SCREAMING_SNAKE_CASE :     str=None				,       SCREAMING_SNAKE_CASE :     Dict=None				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
									# TODO: remove this once the Hub files are updated.
									lowercase__					:   int													=					kwargs.pop("text_config_dict"				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   int													=					kwargs.pop("vision_config_dict"				,       SCREAMING_SNAKE_CASE					)

									super().__init__(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					share_cross_modal_transformer_layers
									lowercase__					:   int													=					hidden_act
									lowercase__					:   int													=					hidden_size
									lowercase__					:   Optional[int]													=					initializer_factor
									lowercase__					:   Optional[int]													=					layer_norm_eps
									lowercase__					:   str													=					share_link_tower_layers
									lowercase__					:   Optional[int]													=					link_tower_type
									lowercase__					:   List[Any]													=					num_attention_heads
									lowercase__					:   Dict													=					num_hidden_layers
									lowercase__					:   Dict													=					tie_word_embeddings
									lowercase__					:   Optional[Any]													=					init_layernorm_from_vision_encoder

									if text_config is None:
													lowercase__					:   Optional[Any]													=					{}
													logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."					)

									if vision_config is None:
													lowercase__					:   List[Any]													=					{}
													logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."					)

									lowercase__					:   Any													=					BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE					)







					@classmethod
					def 			snake_case					(							cls :     Optional[int]				,       SCREAMING_SNAKE_CASE :     BridgeTowerTextConfig				,       SCREAMING_SNAKE_CASE :     BridgeTowerVisionConfig				,       **SCREAMING_SNAKE_CASE :     List[str]					):
									return cls(text_config=text_config.to_dict()				,       vision_config=vision_config.to_dict()				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[str]													=					copy.deepcopy(self.__dict__					)
									lowercase__					:   Dict													=					self.text_config.to_dict()
									lowercase__					:   Tuple													=					self.vision_config.to_dict()
									lowercase__					:   Optional[Any]													=					self.__class__.model_type
									return output




