






'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						CycleDiffusionPipeline
   snake_case    				=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       '''negative_prompt''',
       '''height''',
       '''width''',
       '''negative_prompt_embeds''',
   }
   snake_case    				=						PipelineTesterMixin.required_optional_params - {'''latents'''}
   snake_case    				=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}						)
   snake_case    				=						IMAGE_TO_IMAGE_IMAGE_PARAMS
   snake_case    				=						IMAGE_TO_IMAGE_IMAGE_PARAMS

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         torch.manual_seed(0		)
         _A			     =	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			     =	DDIMScheduler(
             beta_start=0.00085      ,      beta_end=0.012      ,      beta_schedule="scaled_linear"      ,      num_train_timesteps=1000      ,      clip_sample=__UpperCAmelCase      ,      set_alpha_to_one=__UpperCAmelCase      ,      )
         torch.manual_seed(0		)
         _A			     =	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      ,      )
         torch.manual_seed(0		)
         _A			     =	CLIPTextConfig(
             bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1E-05      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
         _A			     =	CLIPTextModel(__UpperCAmelCase		)
         _A			     =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

         _A			     =	{
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Optional[Any]=0		):





         '''simple docstring'''





         _A			     =	floats_tensor((1, 3, 32, 32)      ,      rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
         _A			     =	image / 2 + 0.5
         if str(__UpperCAmelCase		).startswith("mps"		):
               _A			     =	torch.manual_seed(__UpperCAmelCase		)
         else:
               _A			     =	torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         _A			     =	{
             "prompt": "An astronaut riding an elephant",
             "source_prompt": "An astronaut riding a horse",
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
             "eta": 0.1,
             "strength": 0.8,
             "guidance_scale": 3,
             "source_guidance_scale": 1,
             "output_type": "numpy",
         }
         return inputs

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         _A			     =	"cpu"  # ensure determinism for the device-dependent torch.Generator

         _A			     =	self.get_dummy_components()
         _A			     =	CycleDiffusionPipeline(**__UpperCAmelCase		)
         _A			     =	pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         _A			     =	self.get_dummy_inputs(__UpperCAmelCase		)
         _A			     =	pipe(**__UpperCAmelCase		)
         _A			     =	output.images

         _A			     =	images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         _A			     =	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]		)

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

   @unittest.skipIf(torch_device != "cuda"      ,      "This test requires a GPU"		)
   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	self.get_dummy_components()
         for name, module in components.items():
               if hasattr(__UpperCAmelCase      ,      "half"		):
                     _A			     =	module.half()
         _A			     =	CycleDiffusionPipeline(**__UpperCAmelCase		)
         _A			     =	pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         _A			     =	self.get_dummy_inputs(__UpperCAmelCase		)
         _A			     =	pipe(**__UpperCAmelCase		)
         _A			     =	output.images

         _A			     =	images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         _A			     =	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]		)

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

   @skip_mps
   def 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





         return super().test_save_load_local()

   @unittest.skip("non-deterministic pipeline"		)
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         return super().test_inference_batch_single_identical()

   @skip_mps
   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         return super().test_dict_tuple_outputs_equivalent()

   @skip_mps
   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         return super().test_save_load_optional_components()

   @skip_mps
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         return super().test_attention_slicing_forward_pass()





@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 :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/cycle-diffusion/black_colored_car.png"		)
         _A			     =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"		)
         _A			     =	init_image.resize((512, 512)		)

         _A			     =	"CompVis/stable-diffusion-v1-4"
         _A			     =	DDIMScheduler.from_pretrained(__UpperCAmelCase      ,      subfolder="scheduler"		)
         _A			     =	CycleDiffusionPipeline.from_pretrained(
             __UpperCAmelCase      ,      scheduler=__UpperCAmelCase      ,      safety_checker=__UpperCAmelCase      ,      torch_dtype=torch.floataa      ,      revision="fp16"		)

         pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)
         pipe.enable_attention_slicing()

         _A			     =	"A black colored car"
         _A			     =	"A blue colored car"

         _A			     =	torch.manual_seed(0		)
         _A			     =	pipe(
             prompt=__UpperCAmelCase      ,      source_prompt=__UpperCAmelCase      ,      image=__UpperCAmelCase      ,      num_inference_steps=100      ,      eta=0.1      ,      strength=0.85      ,      guidance_scale=3      ,      source_guidance_scale=1      ,      generator=__UpperCAmelCase      ,      output_type="np"      ,      )
         _A			     =	output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image		).max() < 5E-1

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         _A			     =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/cycle-diffusion/black_colored_car.png"		)
         _A			     =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"		)
         _A			     =	init_image.resize((512, 512)		)

         _A			     =	"CompVis/stable-diffusion-v1-4"
         _A			     =	DDIMScheduler.from_pretrained(__UpperCAmelCase      ,      subfolder="scheduler"		)
         _A			     =	CycleDiffusionPipeline.from_pretrained(__UpperCAmelCase      ,      scheduler=__UpperCAmelCase      ,      safety_checker=__UpperCAmelCase		)

         pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)
         pipe.enable_attention_slicing()

         _A			     =	"A black colored car"
         _A			     =	"A blue colored car"

         _A			     =	torch.manual_seed(0		)
         _A			     =	pipe(
             prompt=__UpperCAmelCase      ,      source_prompt=__UpperCAmelCase      ,      image=__UpperCAmelCase      ,      num_inference_steps=100      ,      eta=0.1      ,      strength=0.85      ,      guidance_scale=3      ,      source_guidance_scale=1      ,      generator=__UpperCAmelCase      ,      output_type="np"      ,      )
         _A			     =	output.images

         assert np.abs(image - expected_image		).max() < 2E-2










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


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''roc_bert'''




							def __init__(       self     :       List[Any]				,				A_     :       Optional[Any]=3_0_5_2_2				,				A_     :       List[str]=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       List[str]=1_2				,				A_     :       List[str]=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       int=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       int=2				,				A_     :       List[str]=0.02				,				A_     :       Union[str, Any]=1e-12				,				A_     :       Union[str, Any]=True				,				A_     :       Tuple=0				,				A_     :       Union[str, Any]="absolute"				,				A_     :       Optional[Any]=None				,				A_     :       Any=True				,				A_     :       Optional[int]=True				,				A_     :       List[Any]=7_6_8				,				A_     :       str=9_1_0				,				A_     :       Dict=5_1_2				,				A_     :       Optional[int]=2_4_8_5_8				,				A_     :       Optional[Any]=True				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     List[str]									= vocab_size
									lowerCAmelCase_			:     Any									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Tuple									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     List[str]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Any									= initializer_range
									lowerCAmelCase_			:     Union[str, Any]									= type_vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[int]									= use_cache
									lowerCAmelCase_			:     Tuple									= enable_pronunciation
									lowerCAmelCase_			:     Optional[Any]									= enable_shape
									lowerCAmelCase_			:     Union[str, Any]									= pronunciation_embed_dim
									lowerCAmelCase_			:     List[Any]									= pronunciation_vocab_size
									lowerCAmelCase_			:     Tuple									= shape_embed_dim
									lowerCAmelCase_			:     str									= shape_vocab_size
									lowerCAmelCase_			:     Optional[int]									= concat_input
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= classifier_dropout
									super().__init__(pad_token_id=A_				,				**A_)





