






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 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					str											=   AltDiffusionPipeline
	lowercase__						:					Union[str, Any]											=   TEXT_TO_IMAGE_PARAMS
	lowercase__						:					Optional[int]											=   TEXT_TO_IMAGE_BATCH_PARAMS
	lowercase__						:					Tuple											=   TEXT_TO_IMAGE_IMAGE_PARAMS
	lowercase__						:					Any											=   TEXT_TO_IMAGE_IMAGE_PARAMS
	def       __magic_name__    (    self			)							->				List[Any]:
							torch.manual_seed(0			)
							__magic_name__      :       Optional[int]       						= 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      ,       )
							__magic_name__      :       Optional[Any]       						= DDIMScheduler(
							    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="""scaled_linear"""      ,       clip_sample=lowerCAmelCase__      ,       set_alpha_to_one=lowerCAmelCase__      ,       )
							torch.manual_seed(0			)
							__magic_name__      :       Optional[int]       						= 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			)
							__magic_name__      :       List[Any]       						= 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=50_02      ,       )
							__magic_name__      :       Union[str, Any]       						= CLIPTextModel(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""			)
							__magic_name__      :       List[str]       						= 77

							__magic_name__      :       Dict       						= {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=0			)							->				List[str]:
							if str(lowerCAmelCase__			).startswith("""mps"""			):
													__magic_name__      :       List[str]       						= torch.manual_seed(lowerCAmelCase__			)
							else:
													__magic_name__      :       List[str]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
							__magic_name__      :       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       __magic_name__    (    self			)							->				List[Any]:
							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)
	def       __magic_name__    (    self			)							->				Any:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       str       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator

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

							__magic_name__      :       Optional[Any]       						= AltDiffusionPipeline(**lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= alt_pipe.to(lowerCAmelCase__			)
							alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

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

							assert image.shape == (1, 64, 64, 3)
							__magic_name__      :       str       						= np.array(
							    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]			)

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







	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       List[Any]       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator

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

							__magic_name__      :       Optional[int]       						= self.get_dummy_inputs(lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= alt_pipe(**lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= output.images
							__magic_name__      :       Dict       						= image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__magic_name__      :       Optional[Any]       						= np.array(
							    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]			)

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





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				Optional[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def       __magic_name__    (    self			)							->				str:
							# make sure here that pndm scheduler skips prk
							__magic_name__      :       str       						= AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       safety_checker=lowerCAmelCase__			)
							__magic_name__      :       Union[str, Any]       						= alt_pipe.to(lowerCAmelCase__			)
							alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

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

							__magic_name__      :       Union[str, Any]       						= output.images

							__magic_name__      :       Union[str, Any]       						= image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							__magic_name__      :       int       						= np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]			)

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







	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       List[str]       						= DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,       subfolder="""scheduler"""			)

							__magic_name__      :       Tuple       						= AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       scheduler=lowerCAmelCase__      ,       safety_checker=lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= alt_pipe.to(lowerCAmelCase__			)
							alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

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

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

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

							assert image.shape == (1, 5_12, 5_12, 3)
							__magic_name__      :       Optional[Any]       						= np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]			)

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








import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__magic_name__:					int							=     "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def    UpperCamelCase						(	_A,   _A=None			):






						"""simple docstring"""







						require_version(deps[pkg],   _A			)
