

"""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 							lowercase_					(     self							:    Union[str, Any])->       List[str]:



						'''simple docstring'''


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

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

						torch.manual_seed(0)
						__lowerCAmelCase:	Dict   				=      UNetaDConditionModel(
						    sample_size=3_2					,  layers_per_block=1					,  block_out_channels=[3_2, 6_4]					,  down_block_types=[
						        "ResnetDownsampleBlock2D",
						        "SimpleCrossAttnDownBlock2D",
						    ]					,  mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,  up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,  in_channels=3					,  out_channels=6					,  cross_attention_dim=3_2					,  encoder_hid_dim=3_2					,  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)
						__lowerCAmelCase:	Optional[int]   				=      DDPMScheduler(
						    num_train_timesteps=1_0_0_0					,  beta_schedule="squaredcos_cap_v2"					,  beta_start=0.0001					,  beta_end=0.02					,  thresholding=lowercase_					,  dynamic_thresholding_ratio=0.95					,  sample_max_value=1.0					,  prediction_type="epsilon"					,  variance_type="learned_range"					,  )

						torch.manual_seed(0)
						__lowerCAmelCase:	Any   				=      IFWatermarker()

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

				def 							lowercase_					(     self							:    Tuple)->       List[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	Union[str, Any]   				=      TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")

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

						torch.manual_seed(0)
						__lowerCAmelCase:	Any   				=      UNetaDConditionModel(
						    sample_size=3_2					,  layers_per_block=[1, 2]					,  block_out_channels=[3_2, 6_4]					,  down_block_types=[
						        "ResnetDownsampleBlock2D",
						        "SimpleCrossAttnDownBlock2D",
						    ]					,  mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,  up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,  in_channels=6					,  out_channels=6					,  cross_attention_dim=3_2					,  encoder_hid_dim=3_2					,  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=1.414					,  time_embedding_act_fn="gelu"					,  time_embedding_dim=3_2					,  )
						unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

						torch.manual_seed(0)
						__lowerCAmelCase:	Tuple   				=      DDPMScheduler(
						    num_train_timesteps=1_0_0_0					,  beta_schedule="squaredcos_cap_v2"					,  beta_start=0.0001					,  beta_end=0.02					,  thresholding=lowercase_					,  dynamic_thresholding_ratio=0.95					,  sample_max_value=1.0					,  prediction_type="epsilon"					,  variance_type="learned_range"					,  )

						torch.manual_seed(0)
						__lowerCAmelCase:	int   				=      DDPMScheduler(
						    num_train_timesteps=1_0_0_0					,  beta_schedule="squaredcos_cap_v2"					,  beta_start=0.0001					,  beta_end=0.02					,  )

						torch.manual_seed(0)
						__lowerCAmelCase:	Optional[Any]   				=      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 							lowercase_					(     self							:    Optional[int])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.get_dummy_components()
						__lowerCAmelCase:	Dict   				=      self.pipeline_class(**lowercase_)
						pipe.to(lowercase_)
						pipe.set_progress_bar_config(disable=lowercase_)

						__lowerCAmelCase:	List[str]   				=      self.get_dummy_inputs(lowercase_)

						__lowerCAmelCase:	List[str]   				=      inputs["prompt"]
						__lowerCAmelCase:	Tuple   				=      inputs["generator"]
						__lowerCAmelCase:	List[Any]   				=      inputs["num_inference_steps"]
						__lowerCAmelCase:	List[Any]   				=      inputs["output_type"]

						if "image" in inputs:
								__lowerCAmelCase:	Optional[Any]   				=      inputs["image"]
						else:
								__lowerCAmelCase:	List[Any]   				=      None

						if "mask_image" in inputs:
								__lowerCAmelCase:	List[Any]   				=      inputs["mask_image"]
						else:
								__lowerCAmelCase:	str   				=      None

						if "original_image" in inputs:
								__lowerCAmelCase:	Tuple   				=      inputs["original_image"]
						else:
								__lowerCAmelCase:	List[Any]   				=      None

						__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      pipe.encode_prompt(lowercase_)

						# inputs with prompt converted to embeddings
						__lowerCAmelCase:	List[str]   				=      {
						    "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:
								__lowerCAmelCase:	Optional[Any]   				=      image

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

						if original_image is not None:
								__lowerCAmelCase:	Tuple   				=      original_image

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

						__lowerCAmelCase:	str   				=      pipe(**lowercase_)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowercase_)
								__lowerCAmelCase:	Optional[int]   				=      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."					,  )

						__lowerCAmelCase:	Optional[Any]   				=      self.get_dummy_inputs(lowercase_)

						__lowerCAmelCase:	Tuple   				=      inputs["generator"]
						__lowerCAmelCase:	Dict   				=      inputs["num_inference_steps"]
						__lowerCAmelCase:	List[Any]   				=      inputs["output_type"]

						# inputs with prompt converted to embeddings
						__lowerCAmelCase:	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:
								__lowerCAmelCase:	str   				=      image

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

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

						__lowerCAmelCase:	Tuple   				=      pipe_loaded(**lowercase_)[0]

						__lowerCAmelCase:	List[str]   				=      np.abs(to_np(lowercase_) - to_np(lowercase_)).max()
						self.assertLess(lowercase_					,  1e-4)




				def 							lowercase_					(     self							:    Dict)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.get_dummy_components()
						__lowerCAmelCase:	List[Any]   				=      self.pipeline_class(**lowercase_)
						pipe.to(lowercase_)
						pipe.set_progress_bar_config(disable=lowercase_)

						__lowerCAmelCase:	List[Any]   				=      self.get_dummy_inputs(lowercase_)
						__lowerCAmelCase:	Optional[Any]   				=      pipe(**lowercase_)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
								pipe.save_pretrained(lowercase_)
								__lowerCAmelCase:	str   				=      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

						__lowerCAmelCase:	Any   				=      self.get_dummy_inputs(lowercase_)
						__lowerCAmelCase:	List[Any]   				=      pipe_loaded(**lowercase_)[0]

						__lowerCAmelCase:	Tuple   				=      np.abs(to_np(lowercase_) - to_np(lowercase_)).max()
						self.assertLess(lowercase_					,  1e-4)





"""simple docstring"""


from __future__ import annotations

from math import pi






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     dict[str, float]:

		if (inductance, frequency, reactance).count(0			) != 1:
				raise ValueError("One and only one argument must be 0"			)
		if inductance < 0:
				raise ValueError("Inductance cannot be negative"			)
		if frequency < 0:
				raise ValueError("Frequency cannot be negative"			)
		if reactance < 0:
				raise ValueError("Inductive reactance cannot be negative"			)
		if inductance == 0:
				return {"inductance": reactance / (2 * pi * frequency)}
		elif frequency == 0:
				return {"frequency": reactance / (2 * pi * inductance)}
		elif reactance == 0:
				return {"reactance": 2 * pi * frequency * inductance}
		else:
				raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


