






"""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			(	lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__		,     unittest.TestCase      ):
 UpperCamelCase					:       Optional[Any]			=					AltDiffusionPipeline
 UpperCamelCase					:       str			=					TEXT_TO_IMAGE_PARAMS
 UpperCamelCase					:       Dict			=					TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCamelCase					:       Dict			=					TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCamelCase					:       Any			=					TEXT_TO_IMAGE_IMAGE_PARAMS


 def _lowercase	(     self		:						List[str]							)					->       Dict:
   torch.manual_seed(0							)
   _a  :       Dict             =	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							,						)
   _a  :       Any             =	DDIMScheduler(
       beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule="""scaled_linear"""							,						clip_sample=__a							,						set_alpha_to_one=__a							,						)
   torch.manual_seed(0							)
   _a  :       Tuple             =	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							)
   _a  :       str             =	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=5002							,						)
   _a  :       Union[str, Any]             =	CLIPTextModel(__a							)

   _a  :       Optional[int]             =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""							)
   _a  :       Dict             =	77

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


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						str=0							)					->       str:
   if str(__a							).startswith("""mps"""							):
     _a  :       int             =	torch.manual_seed(__a							)
   else:
     _a  :       Optional[int]             =	torch.Generator(device=__a							).manual_seed(__a							)
   _a  :       Optional[int]             =	{
       """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 _lowercase	(     self		:						Union[str, Any]							)					->       List[Any]:
   super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)


 def _lowercase	(     self		:						List[str]							)					->       Union[str, Any]:
   super().test_inference_batch_single_identical(expected_max_diff=3E-3							)


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

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

   _a  :       Optional[int]             =	AltDiffusionPipeline(**__a							)
   _a  :       Tuple             =	alt_pipe.to(__a							)
   alt_pipe.set_progress_bar_config(disable=__a							)

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

   assert image.shape == (1, 64, 64, 3)
   _a  :       Optional[Any]             =	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 _lowercase	(     self		:						Tuple							)					->       Optional[Any]:
   _a  :       Union[str, Any]             =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

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

   _a  :       Any             =	self.get_dummy_inputs(__a							)
   _a  :       List[str]             =	alt_pipe(**__a							)
   _a  :       List[str]             =	output.images
   _a  :       int             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   _a  :       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        UpperCamelCase			(	unittest.TestCase      ):


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


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

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

   _a  :       Dict             =	output.images

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

   assert image.shape == (1, 512, 512, 3)
   _a  :       Any             =	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 _lowercase	(     self		:						Optional[Any]							)					->       Optional[Any]:
   _a  :       Optional[Any]             =	DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""							,						subfolder="""scheduler"""							)

   _a  :       str             =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""							,						scheduler=__a							,						safety_checker=__a							)
   _a  :       int             =	alt_pipe.to(__a							)
   alt_pipe.set_progress_bar_config(disable=__a							)

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

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

   _a  :       str             =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Tuple             =	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





"""simple docstring"""


A     :   int			    =			{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}




