




import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class       a__	( A__       , A__       , A__       , unittest.TestCase				):
     a		:      int		 =     StableDiffusionControlNetImgaImgPipeline
     a		:      str		 =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     a		:      Tuple		 =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     a		:      List[str]		 =     IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}				)
     a		:      Optional[Any]		 =     IMAGE_TO_IMAGE_IMAGE_PARAMS







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''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  ,		)
          torch.manual_seed(0			)
          a     =				ControlNetModel(
              block_out_channels=(32, 64)  ,		layers_per_block=2  ,		in_channels=4  ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,		cross_attention_dim=32  ,		conditioning_embedding_out_channels=(16, 32)  ,		)
          torch.manual_seed(0			)
          a     =				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     =				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(__A			)
          a     =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

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







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0			)  ->   List[str]:



          '''simple docstring'''



          if str(__A			).startswith("mps"			):
               a     =				torch.manual_seed(__A			)
          else:
               a     =				torch.Generator(device=__A			).manual_seed(__A			)

          a     =				2
          a     =				randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,		generator=__A  ,		device=torch.device(__A			)  ,		)
          a     =				floats_tensor(control_image.shape  ,		rng=random.Random(__A			)			).to(__A			)
          a     =				image.cpu().permute(0  ,		2  ,		3  ,		1			)[0]
          a     =				Image.fromarray(np.uinta(__A			)			).convert("RGB"			).resize((64, 64)			)
          a     =				{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """image""": image,
              """control_image""": control_image,
          }

          return inputs







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3			)







     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,		reason="XFormers attention is only available with CUDA and `xformers` installed"  ,		)
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          self._test_inference_batch_single_identical(expected_max_diff=2e-3			)







class       a__	( A__       , A__       , unittest.TestCase				):
     a		:      List[str]		 =     StableDiffusionControlNetImgaImgPipeline
     a		:      int		 =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     a		:      str		 =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     a		:      int		 =     frozenset([]				)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







     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  ,		)
          torch.manual_seed(0			)

          def init_weights(A			):
               if isinstance(__A  ,		torch.nn.Convad			):
                    torch.nn.init.normal(m.weight			)
                    m.bias.data.fill_(1.0			)

          a     =				ControlNetModel(
              block_out_channels=(32, 64)  ,		layers_per_block=2  ,		in_channels=4  ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,		cross_attention_dim=32  ,		conditioning_embedding_out_channels=(16, 32)  ,		)
          controlneta.controlnet_down_blocks.apply(__A			)

          torch.manual_seed(0			)
          a     =				ControlNetModel(
              block_out_channels=(32, 64)  ,		layers_per_block=2  ,		in_channels=4  ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,		cross_attention_dim=32  ,		conditioning_embedding_out_channels=(16, 32)  ,		)
          controlneta.controlnet_down_blocks.apply(__A			)

          torch.manual_seed(0			)
          a     =				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     =				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(__A			)
          a     =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

          a     =				MultiControlNetModel([controlneta, controlneta]			)

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







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0			)  ->   str:



          '''simple docstring'''



          if str(__A			).startswith("mps"			):
               a     =				torch.manual_seed(__A			)
          else:
               a     =				torch.Generator(device=__A			).manual_seed(__A			)

          a     =				2

          a     =				[
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,		generator=__A  ,		device=torch.device(__A			)  ,		),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,		generator=__A  ,		device=torch.device(__A			)  ,		),
          ]

          a     =				floats_tensor(control_image[0].shape  ,		rng=random.Random(__A			)			).to(__A			)
          a     =				image.cpu().permute(0  ,		2  ,		3  ,		1			)[0]
          a     =				Image.fromarray(np.uinta(__A			)			).convert("RGB"			).resize((64, 64)			)
          a     =				{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """image""": image,
              """control_image""": control_image,
          }

          return inputs







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.get_dummy_components()
          a     =				self.pipeline_class(**__A			)
          pipe.to(__A			)

          a     =				10.0
          a     =				4

          a     =				self.get_dummy_inputs(__A			)
          a     =				steps
          a     =				scale
          a     =				pipe(**__A			)[0]

          a     =				self.get_dummy_inputs(__A			)
          a     =				steps
          a     =				scale
          a     =				pipe(**__A  ,		control_guidance_start=0.1  ,		control_guidance_end=0.2			)[0]

          a     =				self.get_dummy_inputs(__A			)
          a     =				steps
          a     =				scale
          a     =				pipe(**__A  ,		control_guidance_start=[0.1, 0.3]  ,		control_guidance_end=[0.2, 0.7]			)[0]

          a     =				self.get_dummy_inputs(__A			)
          a     =				steps
          a     =				scale
          a     =				pipe(**__A  ,		control_guidance_start=0.4  ,		control_guidance_end=[0.5, 0.8]			)[0]

          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a			)			) > 1e-3
          assert np.sum(np.abs(output_a - output_a			)			) > 1e-3
          assert np.sum(np.abs(output_a - output_a			)			) > 1e-3







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3			)







     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,		reason="XFormers attention is only available with CUDA and `xformers` installed"  ,		)
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          self._test_inference_batch_single_identical(expected_max_diff=2e-3			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				self.get_dummy_components()
          a     =				self.pipeline_class(**__A			)
          pipe.to(__A			)
          pipe.set_progress_bar_config(disable=__A			)
          with tempfile.TemporaryDirectory() as tmpdir:
               try:
                    # save_pretrained is not implemented for Multi-ControlNet
                    pipe.save_pretrained(__A			)
               except NotImplementedError:
                    pass







@slow
@require_torch_gpu
class       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"			)

          a     =				StableDiffusionControlNetImgaImgPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		safety_checker=__A  ,		controlnet=__A			)
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=__A			)

          a     =				torch.Generator(device="cpu"			).manual_seed(0			)
          a     =				"""evil space-punk bird"""
          a     =				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"			).resize((512, 512)			)
          a     =				load_image(
              "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"			).resize((512, 512)			)

          a     =				pipe(
              __A  ,		__A  ,		control_image=__A  ,		generator=__A  ,		output_type="np"  ,		num_inference_steps=50  ,		strength=0.6  ,		)

          a     =				output.images[0]

          assert image.shape == (512, 512, 3)

          a     =				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"			)

          assert np.abs(expected_image - image			).max() < 9e-2











import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				"永和服装饰品有限公司,今天天气非常好"
          a     =				"永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

          self.assertListEqual(A  ,		output_text.split()			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass

