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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[str]:
          torch.manual_seed(0      )
          lowerCamelCase__       :						Tuple        =							UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, cross_attention_dim=3_2		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						List[Any]        =							ControlNetModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, in_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, cross_attention_dim=3_2		, conditioning_embedding_out_channels=(1_6, 3_2)		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[Any]        =							DDIMScheduler(
              beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=__A		, set_alpha_to_one=__A		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						List[str]        =							AutoencoderKL(
              block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Union[str, Any]        =							CLIPTextConfig(
              bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, )
          lowerCamelCase__       :						List[Any]        =							CLIPTextModel(__A      )
          lowerCamelCase__       :						int        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

          lowerCamelCase__       :						Union[str, Any]        =							{
              """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]		, A					:     Dict		, A					:     Tuple=0      )       ->List[str]:
          if str(__A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						Tuple        =							torch.manual_seed(__A      )
          else:
             lowerCamelCase__       :						Optional[int]        =							torch.Generator(device=__A      ).manual_seed(__A      )

          lowerCamelCase__       :						List[Any]        =							2
          lowerCamelCase__       :						int        =							randn_tensor(
              (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)		, generator=__A		, device=torch.device(__A      )		, )
          lowerCamelCase__       :						Optional[int]        =							floats_tensor(control_image.shape		, rng=random.Random(__A      )      ).to(__A      )
          lowerCamelCase__       :						Tuple        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						List[Any]        =							Image.fromarray(np.uinta(__A      )      ).convert('''RGB'''      ).resize((6_4, 6_4)      )
          lowerCamelCase__       :						Union[str, Any]        =							{
              """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					:     Tuple      )       ->int:
          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					:     List[Any]      )       ->Union[str, Any]:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          self._test_inference_batch_single_identical(expected_max_diff=2e-3      )


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







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
          torch.manual_seed(0      )
          lowerCamelCase__       :						Dict        =							UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, cross_attention_dim=3_2		, )
          torch.manual_seed(0      )

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

          lowerCamelCase__       :						List[Any]        =							ControlNetModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, in_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, cross_attention_dim=3_2		, conditioning_embedding_out_channels=(1_6, 3_2)		, )
          controlneta.controlnet_down_blocks.apply(__A      )

          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[Any]        =							ControlNetModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, in_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, cross_attention_dim=3_2		, conditioning_embedding_out_channels=(1_6, 3_2)		, )
          controlneta.controlnet_down_blocks.apply(__A      )

          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[Any]        =							DDIMScheduler(
              beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=__A		, set_alpha_to_one=__A		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[int]        =							AutoencoderKL(
              block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[Any]        =							CLIPTextConfig(
              bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, )
          lowerCamelCase__       :						str        =							CLIPTextModel(__A      )
          lowerCamelCase__       :						str        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

          lowerCamelCase__       :						Optional[Any]        =							MultiControlNetModel([controlneta, controlneta]      )

          lowerCamelCase__       :						List[Any]        =							{
              """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					:     List[str]		, A					:     Union[str, Any]		, A					:     Dict=0      )       ->str:
          if str(__A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						Optional[Any]        =							torch.manual_seed(__A      )
          else:
             lowerCamelCase__       :						List[Any]        =							torch.Generator(device=__A      ).manual_seed(__A      )

          lowerCamelCase__       :						Optional[Any]        =							2

          lowerCamelCase__       :						Optional[int]        =							[
              randn_tensor(
                  (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)		, generator=__A		, device=torch.device(__A      )		, ),
              randn_tensor(
                  (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)		, generator=__A		, device=torch.device(__A      )		, ),
          ]

          lowerCamelCase__       :						int        =							floats_tensor(control_image[0].shape		, rng=random.Random(__A      )      ).to(__A      )
          lowerCamelCase__       :						Optional[Any]        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						List[Any]        =							Image.fromarray(np.uinta(__A      )      ).convert('''RGB'''      ).resize((6_4, 6_4)      )
          lowerCamelCase__       :						List[str]        =							{
              """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]      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							self.get_dummy_components()
          lowerCamelCase__       :						Tuple        =							self.pipeline_class(**__A      )
          pipe.to(__A      )

          lowerCamelCase__       :						Union[str, Any]        =							1_0.0
          lowerCamelCase__       :						Union[str, Any]        =							4

          lowerCamelCase__       :						Tuple        =							self.get_dummy_inputs(__A      )
          lowerCamelCase__       :						List[str]        =							steps
          lowerCamelCase__       :						int        =							scale
          lowerCamelCase__       :						Union[str, Any]        =							pipe(**__A      )[0]

          lowerCamelCase__       :						Any        =							self.get_dummy_inputs(__A      )
          lowerCamelCase__       :						str        =							steps
          lowerCamelCase__       :						str        =							scale
          lowerCamelCase__       :						Tuple        =							pipe(**__A		, control_guidance_start=0.1		, control_guidance_end=0.2      )[0]

          lowerCamelCase__       :						Optional[Any]        =							self.get_dummy_inputs(__A      )
          lowerCamelCase__       :						Union[str, Any]        =							steps
          lowerCamelCase__       :						Union[str, Any]        =							scale
          lowerCamelCase__       :						str        =							pipe(**__A		, control_guidance_start=[0.1, 0.3]		, control_guidance_end=[0.2, 0.7]      )[0]

          lowerCamelCase__       :						List[str]        =							self.get_dummy_inputs(__A      )
          lowerCamelCase__       :						Optional[int]        =							steps
          lowerCamelCase__       :						Tuple        =							scale
          lowerCamelCase__       :						str        =							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					:     Optional[int]      )       ->Dict:
          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					:     Optional[int]      )       ->Tuple:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[int]:
          self._test_inference_batch_single_identical(expected_max_diff=2e-3      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[str]:
          lowerCamelCase__       :						str        =							self.get_dummy_components()
          lowerCamelCase__       :						Tuple        =							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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->int:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def 						__lowerCamelCase   (				self					:     Dict      )       ->str:
          lowerCamelCase__       :						Any        =							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''      )

          lowerCamelCase__       :						int        =							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      )

          lowerCamelCase__       :						List[str]        =							torch.Generator(device='''cpu'''      ).manual_seed(0      )
          lowerCamelCase__       :						List[Any]        =							"""evil space-punk bird"""
          lowerCamelCase__       :						List[Any]        =							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''      ).resize((5_1_2, 5_1_2)      )
          lowerCamelCase__       :						int        =							load_image(
              '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''      ).resize((5_1_2, 5_1_2)      )

          lowerCamelCase__       :						Union[str, Any]        =							pipe(
              __A		, __A		, control_image=__A		, generator=__A		, output_type='''np'''		, num_inference_steps=5_0		, strength=0.6		, )

          lowerCamelCase__       :						Tuple        =							output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)

          lowerCamelCase__       :						Tuple        =							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










from __future__ import annotations

_A :   List[str]  			=  '#'


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]      )       ->None:
          lowerCamelCase__       :						dict        =							{}







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     str      )       ->None:
          lowerCamelCase__       :						Any        =							self._trie
          for char in text:
             if char not in trie:
                lowerCamelCase__       :						Any        =							{}
             lowerCamelCase__       :						Any        =							trie[char]
          lowerCamelCase__       :						List[str]        =							True







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str      )       ->tuple | list:
          lowerCamelCase__       :						Dict        =							self._trie
          for char in prefix:
             if char in trie:
                lowerCamelCase__       :						List[Any]        =							trie[char]
             else:
                return []
          return self._elements(A      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     dict      )       ->tuple:
          lowerCamelCase__       :						Optional[Any]        =							[]
          for c, v in d.items():
             lowerCamelCase__       :						Any        =							[''' '''] if c == END else [(c + s) for s in self._elements(A      )]
             result.extend(A      )
          return tuple(A      )


_A :   str  			=  Trie()
_A :   List[Any]  			=  ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
       trie.insert_word(word)



def 	_a						(   UpperCAmelCase					)    ->    tuple:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							trie.find_word(UpperCAmelCase					)
   return tuple(string + word for word in suffixes					)



def 	_a						(   )    ->    None:







   """simple docstring"""
   print(autocomplete_using_trie('''de'''					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()



