import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        UpperCamelCase						(				__a	,	__a	,	__a	,	unittest.TestCase				):
						a__		:Any      =			AltDiffusionPipeline
						a__		:int      =			TEXT_TO_IMAGE_PARAMS
						a__		:int      =			TEXT_TO_IMAGE_BATCH_PARAMS
						a__		:Union[str, Any]      =			TEXT_TO_IMAGE_IMAGE_PARAMS
						a__		:Optional[Any]      =			TEXT_TO_IMAGE_IMAGE_PARAMS





						def        A_							(self						)				->				int:
												torch.manual_seed(0						)
												UpperCamelCase_		:							Union[str, Any]  =		UNetaDConditionModel(
												    block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,     cross_attention_dim=32			,     )
												UpperCamelCase_		:							Tuple  =		DDIMScheduler(
												    beta_start=0.00_085			,     beta_end=0.012			,     beta_schedule="""scaled_linear"""			,     clip_sample=__UpperCamelCase			,     set_alpha_to_one=__UpperCamelCase			,     )
												torch.manual_seed(0						)
												UpperCamelCase_		:							Union[str, Any]  =		AutoencoderKL(
												    block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,     latent_channels=4			,     )

												# TODO: address the non-deterministic text encoder (fails for save-load tests)
												# torch.manual_seed(0)
												# text_encoder_config = RobertaSeriesConfig(
												#     hidden_size=32,
												#     project_dim=32,
												#     intermediate_size=37,
												#     layer_norm_eps=1e-05,
												#     num_attention_heads=4,
												#     num_hidden_layers=5,
												#     vocab_size=5002,
												# )
												# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

												torch.manual_seed(0						)
												UpperCamelCase_		:							List[str]  =		CLIPTextConfig(
												    bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     projection_dim=32			,     intermediate_size=37			,     layer_norm_eps=1E-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=5_002			,     )
												UpperCamelCase_		:							Tuple  =		CLIPTextModel(__UpperCamelCase						)

												UpperCamelCase_		:							Optional[int]  =		XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""						)
												UpperCamelCase_		:							Union[str, Any]  =		77

												UpperCamelCase_		:							Any  =		{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}
												return components





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=0						)				->				Union[str, Any]:
												if str(__UpperCamelCase						).startswith("""mps"""						):
																		UpperCamelCase_		:							Optional[Any]  =		torch.manual_seed(__UpperCamelCase						)
												else:
																		UpperCamelCase_		:							Optional[Any]  =		torch.Generator(device=__UpperCamelCase						).manual_seed(__UpperCamelCase						)
												UpperCamelCase_		:							str  =		{
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """guidance_scale""": 6.0,
												    """output_type""": """numpy""",
												}
												return inputs





						def        A_							(self						)				->				str:
												super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)





						def        A_							(self						)				->				Optional[Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3						)





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator

												UpperCamelCase_		:							Dict  =		self.get_dummy_components()
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		RobertaSeriesConfig(
												    hidden_size=32			,     project_dim=32			,     intermediate_size=37			,     layer_norm_eps=1E-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=5_002			,     )
												# TODO: remove after fixing the non-deterministic text encoder
												UpperCamelCase_		:							Tuple  =		RobertaSeriesModelWithTransformation(__UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		text_encoder

												UpperCamelCase_		:							Optional[int]  =		AltDiffusionPipeline(**__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		alt_pipe.to(__UpperCamelCase						)
												alt_pipe.set_progress_bar_config(disable=__UpperCamelCase						)

												UpperCamelCase_		:							int  =		self.get_dummy_inputs(__UpperCamelCase						)
												UpperCamelCase_		:							List[str]  =		"""A photo of an astronaut"""
												UpperCamelCase_		:							Dict  =		alt_pipe(**__UpperCamelCase						)
												UpperCamelCase_		:							int  =		output.images
												UpperCamelCase_		:							str  =		image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												UpperCamelCase_		:							str  =		np.array(
												    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]						)

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





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator

												UpperCamelCase_		:							Dict  =		self.get_dummy_components()
												UpperCamelCase_		:							Dict  =		PNDMScheduler(skip_prk_steps=__UpperCamelCase						)
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		RobertaSeriesConfig(
												    hidden_size=32			,     project_dim=32			,     intermediate_size=37			,     layer_norm_eps=1E-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=5_002			,     )
												# TODO: remove after fixing the non-deterministic text encoder
												UpperCamelCase_		:							Any  =		RobertaSeriesModelWithTransformation(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		text_encoder
												UpperCamelCase_		:							str  =		AltDiffusionPipeline(**__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		alt_pipe.to(__UpperCamelCase						)
												alt_pipe.set_progress_bar_config(disable=__UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		self.get_dummy_inputs(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		alt_pipe(**__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		output.images
												UpperCamelCase_		:							Optional[int]  =		image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												UpperCamelCase_		:							int  =		np.array(
												    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]						)

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


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





						def        A_							(self						)				->				int:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





						def        A_							(self						)				->				List[str]:
												# make sure here that pndm scheduler skips prk
												UpperCamelCase_		:							Union[str, Any]  =		AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""			,     safety_checker=__UpperCamelCase						)
												UpperCamelCase_		:							Optional[int]  =		alt_pipe.to(__UpperCamelCase						)
												alt_pipe.set_progress_bar_config(disable=__UpperCamelCase						)

												UpperCamelCase_		:							List[Any]  =		"""A painting of a squirrel eating a burger"""
												UpperCamelCase_		:							Optional[Any]  =		torch.manual_seed(0						)
												UpperCamelCase_		:							Union[str, Any]  =		alt_pipe([prompt]			,     generator=__UpperCamelCase			,     guidance_scale=6.0			,     num_inference_steps=20			,     output_type="""np"""						)

												UpperCamelCase_		:							Tuple  =		output.images

												UpperCamelCase_		:							Dict  =		image[0, -3:, -3:, -1]

												assert image.shape == (1, 512, 512, 3)
												UpperCamelCase_		:							Tuple  =		np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]						)

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





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_		:							Optional[int]  =		DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""			,     subfolder="""scheduler"""						)

												UpperCamelCase_		:							str  =		AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""			,     scheduler=__UpperCamelCase			,     safety_checker=__UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		alt_pipe.to(__UpperCamelCase						)
												alt_pipe.set_progress_bar_config(disable=__UpperCamelCase						)

												UpperCamelCase_		:							int  =		"""A painting of a squirrel eating a burger"""
												UpperCamelCase_		:							Dict  =		torch.manual_seed(0						)

												UpperCamelCase_		:							int  =		alt_pipe([prompt]			,     generator=__UpperCamelCase			,     num_inference_steps=2			,     output_type="""numpy"""						)
												UpperCamelCase_		:							List[Any]  =		output.images

												UpperCamelCase_		:							Optional[int]  =		image[0, -3:, -3:, -1]

												assert image.shape == (1, 512, 512, 3)
												UpperCamelCase_		:							Optional[int]  =		np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]						)

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






