


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 _lowerCamelCase(						_a, _a, unittest.TestCase   ):
       lowercase_ :	List[str]     =			CycleDiffusionPipeline
       lowercase_ :	Dict     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           """negative_prompt""",
           """height""",
           """width""",
           """negative_prompt_embeds""",
       }
       lowercase_ :	int     =			PipelineTesterMixin.required_optional_params - {"""latents"""}
       lowercase_ :	Any     =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
       lowercase_ :	Any     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
       lowercase_ :	Optional[int]     =			IMAGE_TO_IMAGE_IMAGE_PARAMS
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							int      		=		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,      )
           _lowercase       :							List[str]      		=		DDIMScheduler(
               beta_start=0.0_0_0_8_5,      beta_end=0.0_1_2,      beta_schedule='scaled_linear',      num_train_timesteps=10_00,      clip_sample=lowerCamelCase,      set_alpha_to_one=lowerCamelCase,      )
           torch.manual_seed(0)
           _lowercase       :							List[Any]      		=		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)
           _lowercase       :							List[str]      		=		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=10_00,      )
           _lowercase       :							List[Any]      		=		CLIPTextModel(lowerCamelCase)
           _lowercase       :							List[str]      		=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _lowercase       :							int      		=		{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   str:




           """simple docstring"""

           _lowercase       :							Dict      		=		floats_tensor((1, 3, 32, 32),      rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase       :							List[Any]      		=		image / 2 + 0.5
           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							str      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							List[str]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							str      		=		{
               '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 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Any      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator

           _lowercase       :							str      		=		self.get_dummy_components()
           _lowercase       :							str      		=		CycleDiffusionPipeline(**lowerCamelCase)
           _lowercase       :							List[Any]      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Dict      		=		pipe(**lowerCamelCase)
           _lowercase       :							Any      		=		output.images

           _lowercase       :							Any      		=		images[0, -3:, -3:, -1]

           assert images.shape == (1, 32, 32, 3)
           _lowercase       :							Tuple      		=		np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.get_dummy_components()
           for name, module in components.items():
               if hasattr(lowerCamelCase,      'half'):
                   _lowercase       :							Optional[int]      		=		module.half()
           _lowercase       :							List[str]      		=		CycleDiffusionPipeline(**lowerCamelCase)
           _lowercase       :							int      		=		pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[Any]      		=		self.get_dummy_inputs(lowerCamelCase)
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase)
           _lowercase       :							List[Any]      		=		output.images

           _lowercase       :							Optional[Any]      		=		images[0, -3:, -3:, -1]

           assert images.shape == (1, 32, 32, 3)
           _lowercase       :							List[Any]      		=		np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return super().test_save_load_local()
       @unittest.skip('non-deterministic pipeline')
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return super().test_inference_batch_single_identical()
       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return super().test_dict_tuple_outputs_equivalent()
       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           return super().test_save_load_optional_components()




       @skip_mps
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/cycle-diffusion/black_colored_car.png')
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy')
           _lowercase       :							str      		=		init_image.resize((5_12, 5_12))

           _lowercase       :							Dict      		=		'CompVis/stable-diffusion-v1-4'
           _lowercase       :							Optional[int]      		=		DDIMScheduler.from_pretrained(lowerCamelCase,      subfolder='scheduler')
           _lowercase       :							Optional[int]      		=		CycleDiffusionPipeline.from_pretrained(
               lowerCamelCase,      scheduler=lowerCamelCase,      safety_checker=lowerCamelCase,      torch_dtype=torch.floataa,      revision='fp16')

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

           _lowercase       :							int      		=		'A black colored car'
           _lowercase       :							Union[str, Any]      		=		'A blue colored car'

           _lowercase       :							Tuple      		=		torch.manual_seed(0)
           _lowercase       :							Tuple      		=		pipe(
               prompt=lowerCamelCase,      source_prompt=lowerCamelCase,      image=lowerCamelCase,      num_inference_steps=1_00,      eta=0.1,      strength=0.8_5,      guidance_scale=3,      source_guidance_scale=1,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Optional[int]      		=		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 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/cycle-diffusion/black_colored_car.png')
           _lowercase       :							Union[str, Any]      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy')
           _lowercase       :							Optional[Any]      		=		init_image.resize((5_12, 5_12))

           _lowercase       :							Optional[Any]      		=		'CompVis/stable-diffusion-v1-4'
           _lowercase       :							Tuple      		=		DDIMScheduler.from_pretrained(lowerCamelCase,      subfolder='scheduler')
           _lowercase       :							Any      		=		CycleDiffusionPipeline.from_pretrained(lowerCamelCase,      scheduler=lowerCamelCase,      safety_checker=lowerCamelCase)

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

           _lowercase       :							List[str]      		=		'A black colored car'
           _lowercase       :							Tuple      		=		'A blue colored car'

           _lowercase       :							int      		=		torch.manual_seed(0)
           _lowercase       :							Tuple      		=		pipe(
               prompt=lowerCamelCase,      source_prompt=lowerCamelCase,      image=lowerCamelCase,      num_inference_steps=1_00,      eta=0.1,      strength=0.8_5,      guidance_scale=3,      source_guidance_scale=1,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Tuple      		=		output.images

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



'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

