




import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    int          =  ProphetNetTokenizer
   UpperCAmelCase__  :    List[str]          =  False







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        super().setUp()

        UpperCAmelCase_          =							[
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        with open(self.vocab_file					,						'''w'''					,						encoding='''utf-8''') as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''UNwant\u00E9d,running'''
        UpperCAmelCase_          =							'''unwanted, running'''
        return input_text, output_text







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class(self.vocab_file)

        UpperCAmelCase_          =							tokenizer.tokenize('''UNwant\u00E9d,running''')
        self.assertListEqual(_snake_case					,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''])
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)					,						[9, 6, 7, 12, 10, 11])







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''')					,						['''ah''', '''\u535A''', '''\u63A8''', '''zz'''])







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''')					,						['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''])
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')					,						['''hello'''])







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case					,						strip_accents=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')					,						['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')					,						['''h\u00E9llo'''])







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case					,						strip_accents=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')					,						['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')					,						['''hello'''])







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')					,						['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''])
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''')					,						['''hello'''])







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''')					,						['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case					,						strip_accents=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')					,						['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case					,						strip_accents=_snake_case)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''')					,						['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''])







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							BasicTokenizer(do_lower_case=_snake_case					,						never_split=['''[UNK]'''])

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''')					,						['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''])







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

        UpperCAmelCase_          =							{}
        for i, token in enumerate(_snake_case):
             UpperCAmelCase_          =							i
        UpperCAmelCase_          =							WordpieceTokenizer(vocab=_snake_case					,						unk_token='''[UNK]''')

        self.assertListEqual(tokenizer.tokenize('''''')					,						[])

        self.assertListEqual(tokenizer.tokenize('''unwanted running''')					,						['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''])

        self.assertListEqual(tokenizer.tokenize('''unwantedX running''')					,						['''[UNK]''', '''runn''', '''##ing'''])







   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''')

        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_          =							[1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
        UpperCAmelCase_          =							tokenizer(_snake_case					,						padding=_snake_case					,						return_tensors='''pt''')
        self.assertIsInstance(_snake_case					,						_snake_case)
        UpperCAmelCase_          =							list(batch.input_ids.numpy()[0])
        self.assertListEqual(_snake_case					,						_snake_case)

        self.assertEqual((2, 9)					,						batch.input_ids.shape)
        self.assertEqual((2, 9)					,						batch.attention_mask.shape)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        self.assertTrue(_is_whitespace(''' '''))
        self.assertTrue(_is_whitespace('''\t'''))
        self.assertTrue(_is_whitespace('''\r'''))
        self.assertTrue(_is_whitespace('''\n'''))
        self.assertTrue(_is_whitespace('''\u00A0'''))

        self.assertFalse(_is_whitespace('''A'''))
        self.assertFalse(_is_whitespace('''-'''))







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        self.assertTrue(_is_control('''\u0005'''))

        self.assertFalse(_is_control('''A'''))
        self.assertFalse(_is_control(''' '''))
        self.assertFalse(_is_control('''\t'''))
        self.assertFalse(_is_control('''\r'''))







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.assertTrue(_is_punctuation('''-'''))
        self.assertTrue(_is_punctuation('''$'''))
        self.assertTrue(_is_punctuation('''`'''))
        self.assertTrue(_is_punctuation('''.'''))

        self.assertFalse(_is_punctuation('''A'''))
        self.assertFalse(_is_punctuation(''' '''))







   @slow
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''')

        UpperCAmelCase_          =							tokenizer.encode('''sequence builders'''					,						add_special_tokens=_snake_case)
        UpperCAmelCase_          =							tokenizer.encode('''multi-sequence build'''					,						add_special_tokens=_snake_case)

        UpperCAmelCase_          =							tokenizer.build_inputs_with_special_tokens(_snake_case)
        UpperCAmelCase_          =							tokenizer.build_inputs_with_special_tokens(_snake_case					,						_snake_case)

        assert encoded_sentence == text + [102]
        assert encoded_pair == text + [102] + text_a + [102]












# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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 __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    Union[str, Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}		)
   UpperCAmelCase__  :    Optional[Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							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					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							{
            '''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		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Dict=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)

        UpperCAmelCase_          =							2
        UpperCAmelCase_          =							randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=_snake_case					,						device=torch.device(_snake_case)					,						)
        UpperCAmelCase_          =							floats_tensor(control_image.shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''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		:						Any):

        """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		:						Any):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







class __snake_case			(			a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    List[str]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    str          =  frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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(_snake_case		:						Optional[int]):
             if isinstance(_snake_case					,						torch.nn.Convad):
                  torch.nn.init.normal(m.weight)
                  m.bias.data.fill_(1.0)

        UpperCAmelCase_          =							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(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							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(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							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					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							MultiControlNetModel([controlneta, controlneta])

        UpperCAmelCase_          =							{
            '''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		:						int					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)

        UpperCAmelCase_          =							2

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

        UpperCAmelCase_          =							floats_tensor(control_image[0].shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''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"""


        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)

        UpperCAmelCase_          =							1_0.0
        UpperCAmelCase_          =							4

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case)[0]

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

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

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case					,						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		:						int):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''')

        UpperCAmelCase_          =							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''					,						safety_checker=_snake_case					,						controlnet=_snake_case)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_snake_case)

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

        UpperCAmelCase_          =							pipe(
            _snake_case					,						_snake_case					,						control_image=_snake_case					,						generator=_snake_case					,						output_type='''np'''					,						num_inference_steps=50					,						strength=0.6					,						)

        UpperCAmelCase_          =							output.images[0]

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

        UpperCAmelCase_          =							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






