






"""simple docstring"""




# 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        _UpperCamelCase		(			__snake_case    ,			__snake_case    ,			__snake_case    ,			unittest.TestCase):
    __lowerCamelCase    =	StableDiffusionControlNetImgaImgPipeline
    __lowerCamelCase    =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    __lowerCamelCase    =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase    =	IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"})
    __lowerCamelCase    =	IMAGE_TO_IMAGE_IMAGE_PARAMS






    def 			A     (self ):


      """simple docstring"""
      torch.manual_seed(0 )
      A__           =				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 )
      A__           =				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 )
      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=[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 )
      A__           =				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    ,   )
      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 			A     (self    ,   lowerCamelCase__    ,   lowerCamelCase__=0 ):


      """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, 3_2 * controlnet_embedder_scale_factor, 3_2 * 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((6_4, 6_4) )
      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 			A     (self ):


      """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 			A     (self ):


      """simple docstring"""
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )






    def 			A     (self ):


      """simple docstring"""
      self._test_inference_batch_single_identical(expected_max_diff=2E-3 )







class        _UpperCamelCase		(			__snake_case    ,			__snake_case    ,			unittest.TestCase):
    __lowerCamelCase    =	StableDiffusionControlNetImgaImgPipeline
    __lowerCamelCase    =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    __lowerCamelCase    =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase    =	frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






    def 			A     (self ):


      """simple docstring"""
      torch.manual_seed(0 )
      A__           =				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(lowerCamelCase__ ):
        if isinstance(__A    ,   torch.nn.Convad ):
          torch.nn.init.normal(m.weight )
          m.bias.data.fill_(1.0 )

      A__           =				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 )
      A__           =				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 )
      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=[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 )
      A__           =				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    ,   )
      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 			A     (self    ,   lowerCamelCase__    ,   lowerCamelCase__=0 ):


      """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, 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 )    ,   ),
      ]

      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((6_4, 6_4) )
      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 			A     (self ):


      """simple docstring"""
      A__           =				self.get_dummy_components()
      A__           =				self.pipeline_class(**__A )
      pipe.to(__A )

      A__           =				1_0.0
      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 			A     (self ):


      """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 			A     (self ):


      """simple docstring"""
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )






    def 			A     (self ):


      """simple docstring"""
      self._test_inference_batch_single_identical(expected_max_diff=2E-3 )






    def 			A     (self ):


      """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        _UpperCamelCase		(			unittest.TestCase):






    def 			A     (self ):


      """simple docstring"""
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 			A     (self ):


      """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((5_1_2, 5_1_2) )
      A__           =				load_image(
          """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((5_1_2, 5_1_2) )

      A__           =				pipe(
          __A    ,   __A    ,   control_image=__A    ,   generator=__A    ,   output_type="""np"""    ,   num_inference_steps=5_0    ,   strength=0.6    ,   )

      A__           =				output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 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 secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8				)			->						str:
       SCREAMING_SNAKE_CASE_							:				str		=ascii_letters + digits + punctuation
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=i // 3
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       SCREAMING_SNAKE_CASE_							:				int		=(
           chars_incl
           + random(UpperCAmelCase_			,							quotient + remainder				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
       )
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(UpperCAmelCase_				)
       shuffle(UpperCAmelCase_				)
       return "".join(UpperCAmelCase_				)

       # random is a generalised function for letters, characters and numbers
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Tuple				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Tuple				)			->						Dict:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     List[str]				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int = 8				)			->						bool:

       if len(UpperCAmelCase_				) < min_length:
              # Your Password must be at least 8 characters long
              return False

       SCREAMING_SNAKE_CASE_							:				List[str]		=any(char in ascii_uppercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=any(char in ascii_lowercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=any(char in digits for char in password				)
       SCREAMING_SNAKE_CASE_							:				str		=any(char in punctuation for char in password				)

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=int(input('''Please indicate the max length of your password: '''				).strip()				)
       SCREAMING_SNAKE_CASE_							:				Any		=input(
           '''Please indicate the characters that must be in your password: '''				).strip()
       print('''Password generated:'''			,							password_generator(UpperCAmelCase_				)				)
       print(
           '''Alternative Password generated:'''			,							alternative_password_generator(UpperCAmelCase_			,							UpperCAmelCase_				)			,							)
       print('''[If you are thinking of using this passsword, You better save it.]'''				)


if __name__ == "__main__":
 main()
