





'''simple docstring'''




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__					(					lowercase_						,					lowercase_						,					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	AltDiffusionPipeline
					SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_PARAMS
					SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_BATCH_PARAMS
					SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_IMAGE_PARAMS
					SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_IMAGE_PARAMS


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				str		=			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      ,						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DDIMScheduler(
							    beta_start=0.00_085      ,						beta_end=0.012      ,						beta_schedule="""scaled_linear"""      ,						clip_sample=lowerCamelCase_      ,						set_alpha_to_one=lowerCamelCase_      ,						)
							torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				Optional[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						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			CLIPTextConfig(
							    bos_token_id=0      ,						eos_token_id=2      ,						hidden_size=32      ,						projection_dim=32      ,						intermediate_size=37      ,						layer_norm_eps=1e-05      ,						num_attention_heads=4      ,						num_hidden_layers=5      ,						pad_token_id=1      ,						vocab_size=50_02      ,						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			CLIPTextModel(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""						)
							SCREAMING_SNAKE_CASE :				Any		=			77

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						lowerCamelCase_				:			Optional[int]      ,						lowerCamelCase_				:			Optional[Any]=0						):

							'''simple docstring'''



							if str(lowerCamelCase_						).startswith("""mps"""						):
									SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.manual_seed(lowerCamelCase_						)
							else:
									SCREAMING_SNAKE_CASE :				int		=			torch.Generator(device=lowerCamelCase_						).manual_seed(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{
							    """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 lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

							SCREAMING_SNAKE_CASE :				str		=			self.get_dummy_components()
							torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			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=50_02      ,						)
							# TODO: remove after fixing the non-deterministic text encoder
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			RobertaSeriesModelWithTransformation(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			text_encoder

							SCREAMING_SNAKE_CASE :				List[Any]		=			AltDiffusionPipeline(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			alt_pipe.to(lowerCamelCase_						)
							alt_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

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

							assert image.shape == (1, 64, 64, 3)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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 lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

							SCREAMING_SNAKE_CASE :				int		=			self.get_dummy_components()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			PNDMScheduler(skip_prk_steps=lowerCamelCase_						)
							torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			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=50_02      ,						)
							# TODO: remove after fixing the non-deterministic text encoder
							SCREAMING_SNAKE_CASE :				Tuple		=			RobertaSeriesModelWithTransformation(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			text_encoder
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			AltDiffusionPipeline(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			alt_pipe.to(lowerCamelCase_						)
							alt_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_dummy_inputs(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			alt_pipe(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			output.images
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			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			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,						safety_checker=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			alt_pipe.to(lowerCamelCase_						)
							alt_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			"""A painting of a squirrel eating a burger"""
							SCREAMING_SNAKE_CASE :				Tuple		=			torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				int		=			alt_pipe([prompt]      ,						generator=lowerCamelCase_      ,						guidance_scale=6.0      ,						num_inference_steps=20      ,						output_type="""np"""						)

							SCREAMING_SNAKE_CASE :				Any		=			output.images

							SCREAMING_SNAKE_CASE :				Tuple		=			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							SCREAMING_SNAKE_CASE :				List[Any]		=			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 lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,						subfolder="""scheduler"""						)

							SCREAMING_SNAKE_CASE :				Any		=			AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,						scheduler=lowerCamelCase_      ,						safety_checker=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			alt_pipe.to(lowerCamelCase_						)
							alt_pipe.set_progress_bar_config(disable=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			"""A painting of a squirrel eating a burger"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.manual_seed(0						)

							SCREAMING_SNAKE_CASE :				Tuple		=			alt_pipe([prompt]      ,						generator=lowerCamelCase_      ,						num_inference_steps=2      ,						output_type="""numpy"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			output.images

							SCREAMING_SNAKE_CASE :				Any		=			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							SCREAMING_SNAKE_CASE :				str		=			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










'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



