




'''simple docstring'''





import copy
import re

class   _a							:
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		'hp'
     SCREAMING_SNAKE_CASE_				:       Dict       							=		{}
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		None






     @classmethod
     def 							_lowercase      (					cls   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[int]:
          _snake_case				= prefix
          _snake_case				= defaults
          cls.build_naming_info()






     @staticmethod
     def 							_lowercase      (					_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     List[Any]:


          if len(_SCREAMING_SNAKE_CASE       ) == 0:
               return ""
          _snake_case				= None
          if any(char.isdigit() for char in word       ):
               raise Exception(f"""Parameters should not contain numbers: \'{word}\' contains a number"""       )
          if word in info["short_word"]:
               return info["short_word"][word]
          for prefix_len in range(1   ,len(_SCREAMING_SNAKE_CASE       ) + 1       ):
               _snake_case				= word[:prefix_len]
               if prefix in info["reverse_short_word"]:
                    continue
               else:
                    _snake_case				= prefix
                    break

          if short_word is None:
               # Paranoid fallback
               def int_to_alphabetic(_SCREAMING_SNAKE_CASE       ):
                    _snake_case				= ''
                    while integer != 0:
                         _snake_case				= chr(ord("A"       ) + integer % 10       ) + s
                         integer //= 10
                    return s

               _snake_case				= 0
               while True:
                    _snake_case				= word + '#' + int_to_alphabetic(_SCREAMING_SNAKE_CASE       )
                    if sword in info["reverse_short_word"]:
                         continue
                    else:
                         _snake_case				= sword
                         break

          _snake_case				= short_word
          _snake_case				= word
          return short_word






     @staticmethod
     def 							_lowercase      (					_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= param_name.split("_"       )

          _snake_case				= [TrialShortNamer.shortname_for_word(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) for word in words]

          # We try to create a separatorless short name, but if there is a collision we have to fallback
          # to a separated short name
          _snake_case				= ['', '_']

          for separator in separators:
               _snake_case				= separator.join(_SCREAMING_SNAKE_CASE       )
               if shortname not in info["reverse_short_param"]:
                    _snake_case				= shortname
                    _snake_case				= param_name
                    return shortname

          return param_name






     @staticmethod
     def 							_lowercase      (					_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= TrialShortNamer.shortname_for_key(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= short_name
          _snake_case				= param_name






     @classmethod
     def 							_lowercase      (					cls       )				->     str:


          if cls.NAMING_INFO is not None:
               return

          _snake_case				= {
              'short_word': {},
              'reverse_short_word': {},
              'short_param': {},
              'reverse_short_param': {},
          }

          _snake_case				= list(cls.DEFAULTS.keys()       )

          for k in field_keys:
               cls.add_new_param_name(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

          _snake_case				= info






     @classmethod
     def 							_lowercase      (					cls   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          cls.build_naming_info()
          assert cls.PREFIX is not None
          _snake_case				= [copy.copy(cls.PREFIX       )]

          for k, v in params.items():
               if k not in cls.DEFAULTS:
                    raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""       )
               if v == cls.DEFAULTS[k]:
                    # The default value is not added to the name
                    continue

               _snake_case				= cls.NAMING_INFO['short_param'][k]

               if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _snake_case				= 1 if v else 0

               _snake_case				= '' if isinstance(_SCREAMING_SNAKE_CASE   ,(int, float)       ) else '-'
               _snake_case				= f"""{key}{sep}{v}"""
               name.append(_SCREAMING_SNAKE_CASE       )

          return "_".join(_SCREAMING_SNAKE_CASE       )






     @classmethod
     def 							_lowercase      (					cls   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= repr[len(cls.PREFIX       ) + 1 :]
          if repr == "":
               _snake_case				= []
          else:
               _snake_case				= repr.split("_"       )

          _snake_case				= {}

          for value in values:
               if "-" in value:
                    _snake_case				= value.split("-"       )
               else:
                    _snake_case				= re.sub("[0-9.]"   ,""   ,_SCREAMING_SNAKE_CASE       )
                    _snake_case				= float(re.sub("[^0-9.]"   ,""   ,_SCREAMING_SNAKE_CASE       )       )

               _snake_case				= cls.NAMING_INFO['reverse_short_param'][p_k]

               _snake_case				= p_v

          for k in cls.DEFAULTS:
               if k not in parameters:
                    _snake_case				= cls.DEFAULTS[k]

          return parameters







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Dict						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1