

"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class       snake_case__  :







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								torch.manual_seed(0  )
								UpperCAmelCase			:   List[Any]      =				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   Optional[Any]      =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   Tuple      =				UNetaDConditionModel(
								    sample_size=32			,  layers_per_block=1			,  block_out_channels=[32, 64]			,  down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]			,  mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,  up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,  in_channels=3			,  out_channels=6			,  cross_attention_dim=32			,  encoder_hid_dim=32			,  attention_head_dim=8			,  addition_embed_type="text"			,  addition_embed_type_num_heads=2			,  cross_attention_norm="group_norm"			,  resnet_time_scale_shift="scale_shift"			,  act_fn="gelu"			,  )
								unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

								torch.manual_seed(0  )
								UpperCAmelCase			:   List[str]      =				DDPMScheduler(
								    num_train_timesteps=10_00			,  beta_schedule="squaredcos_cap_v2"			,  beta_start=0.0_0_0_1			,  beta_end=0.0_2			,  thresholding=lowercase			,  dynamic_thresholding_ratio=0.9_5			,  sample_max_value=1.0			,  prediction_type="epsilon"			,  variance_type="learned_range"			,  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   List[Any]      =				IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								torch.manual_seed(0  )
								UpperCAmelCase			:   Tuple      =				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   List[str]      =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   Optional[int]      =				UNetaDConditionModel(
								    sample_size=32			,  layers_per_block=[1, 2]			,  block_out_channels=[32, 64]			,  down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]			,  mid_block_type="UNetMidBlock2DSimpleCrossAttn"			,  up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]			,  in_channels=6			,  out_channels=6			,  cross_attention_dim=32			,  encoder_hid_dim=32			,  attention_head_dim=8			,  addition_embed_type="text"			,  addition_embed_type_num_heads=2			,  cross_attention_norm="group_norm"			,  resnet_time_scale_shift="scale_shift"			,  act_fn="gelu"			,  class_embed_type="timestep"			,  mid_block_scale_factor=1.4_1_4			,  time_embedding_act_fn="gelu"			,  time_embedding_dim=32			,  )
								unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

								torch.manual_seed(0  )
								UpperCAmelCase			:   Tuple      =				DDPMScheduler(
								    num_train_timesteps=10_00			,  beta_schedule="squaredcos_cap_v2"			,  beta_start=0.0_0_0_1			,  beta_end=0.0_2			,  thresholding=lowercase			,  dynamic_thresholding_ratio=0.9_5			,  sample_max_value=1.0			,  prediction_type="epsilon"			,  variance_type="learned_range"			,  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   Dict      =				DDPMScheduler(
								    num_train_timesteps=10_00			,  beta_schedule="squaredcos_cap_v2"			,  beta_start=0.0_0_0_1			,  beta_end=0.0_2			,  )

								torch.manual_seed(0  )
								UpperCAmelCase			:   Dict      =				IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.get_dummy_components()
								UpperCAmelCase			:   List[str]      =				self.pipeline_class(**lowercase  )
								pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

								UpperCAmelCase			:   int      =				self.get_dummy_inputs(lowercase  )

								UpperCAmelCase			:   Tuple      =				inputs["prompt"]
								UpperCAmelCase			:   Union[str, Any]      =				inputs["generator"]
								UpperCAmelCase			:   List[str]      =				inputs["num_inference_steps"]
								UpperCAmelCase			:   Any      =				inputs["output_type"]

								if "image" in inputs:
													UpperCAmelCase			:   str      =				inputs["image"]
								else:
													UpperCAmelCase			:   List[str]      =				None

								if "mask_image" in inputs:
													UpperCAmelCase			:   Optional[int]      =				inputs["mask_image"]
								else:
													UpperCAmelCase			:   List[str]      =				None

								if "original_image" in inputs:
													UpperCAmelCase			:   Optional[Any]      =				inputs["original_image"]
								else:
													UpperCAmelCase			:   Optional[Any]      =				None

								UpperCAmelCase      ,							UpperCAmelCase			:   Dict      =				pipe.encode_prompt(lowercase  )

								# inputs with prompt converted to embeddings
								UpperCAmelCase			:   Optional[Any]      =				{
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
													UpperCAmelCase			:   str      =				image

								if mask_image is not None:
													UpperCAmelCase			:   str      =				mask_image

								if original_image is not None:
													UpperCAmelCase			:   Union[str, Any]      =				original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
													setattr(lowercase			,  lowercase			,  lowercase  )

								UpperCAmelCase			:   Union[str, Any]      =				pipe(**lowercase  )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowercase  )
													UpperCAmelCase			:   Dict      =				self.pipeline_class.from_pretrained(lowercase  )
													pipe_loaded.to(lowercase  )
													pipe_loaded.set_progress_bar_config(disable=lowercase  )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

								for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(lowercase			,  lowercase  ) is None			,  f"""`{optional_component}` did not stay set to None after loading."""			,  )

								UpperCAmelCase			:   Tuple      =				self.get_dummy_inputs(lowercase  )

								UpperCAmelCase			:   Union[str, Any]      =				inputs["generator"]
								UpperCAmelCase			:   Dict      =				inputs["num_inference_steps"]
								UpperCAmelCase			:   Dict      =				inputs["output_type"]

								# inputs with prompt converted to embeddings
								UpperCAmelCase			:   Optional[int]      =				{
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
													UpperCAmelCase			:   int      =				image

								if mask_image is not None:
													UpperCAmelCase			:   List[str]      =				mask_image

								if original_image is not None:
													UpperCAmelCase			:   List[Any]      =				original_image

								UpperCAmelCase			:   Any      =				pipe_loaded(**lowercase  )[0]

								UpperCAmelCase			:   Tuple      =				np.abs(to_np(lowercase  ) - to_np(lowercase  )  ).max()
								self.assertLess(lowercase			,  1E-4  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				self.get_dummy_components()
								UpperCAmelCase			:   Optional[int]      =				self.pipeline_class(**lowercase  )
								pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

								UpperCAmelCase			:   List[str]      =				self.get_dummy_inputs(lowercase  )
								UpperCAmelCase			:   Any      =				pipe(**lowercase  )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowercase  )
													UpperCAmelCase			:   Tuple      =				self.pipeline_class.from_pretrained(lowercase  )
													pipe_loaded.to(lowercase  )
													pipe_loaded.set_progress_bar_config(disable=lowercase  )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

								UpperCAmelCase			:   Tuple      =				self.get_dummy_inputs(lowercase  )
								UpperCAmelCase			:   str      =				pipe_loaded(**lowercase  )[0]

								UpperCAmelCase			:   Any      =				np.abs(to_np(lowercase  ) - to_np(lowercase  )  ).max()
								self.assertLess(lowercase			,  1E-4  )





"""simple docstring"""






snake_case_   :   List[str]        =       6_5_5_2_1



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				1
					UpperCAmelCase			:   Optional[int]      =				0
					for plain_chr in plain_text:
										UpperCAmelCase			:   int      =				(a + ord(_lowercase	)) % MOD_ADLER
										UpperCAmelCase			:   str      =				(b + a) % MOD_ADLER
					return (b << 16) | a


