
"""simple docstring"""


import copy
import re


class 			A__	:


     """simple docstring"""
     snake_case__	:					Optional[Any]													='''hp'''
     snake_case__	:					Optional[Any]													={}
     snake_case__	:					int													=None

     @classmethod
     def 		a__						(	cls:						int     ,		__a:						int     ,		__a:						str	)->    List[str]:
           lowerCamelCase					:					Dict        =	prefix
           lowerCamelCase					:					Union[str, Any]        =	defaults
           cls.build_naming_info()

     @staticmethod
     def 		a__						(	__a:						Union[str, Any]     ,		__a:						List[str]	)->    Optional[int]:
           if len(__a	) == 0:
                 return ""
           lowerCamelCase					:					Tuple        =	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(__a	) + 1	):
                 lowerCamelCase					:					Tuple        =	word[:prefix_len]
                 if prefix in info["reverse_short_word"]:
                       continue
                 else:
                       lowerCamelCase					:					Any        =	prefix
                       break

           if short_word is None:
                 # Paranoid fallback
                 def int_to_alphabetic(__a:						Union[str, Any]	):
                       lowerCamelCase					:					List[str]        =	""""""
                       while integer != 0:
                             lowerCamelCase					:					List[Any]        =	chr(ord("""A"""	) + integer % 10	) + s
                             integer //= 10
                       return s

                 lowerCamelCase					:					Tuple        =	0
                 while True:
                       lowerCamelCase					:					str        =	word + """#""" + int_to_alphabetic(__a	)
                       if sword in info["reverse_short_word"]:
                             continue
                       else:
                             lowerCamelCase					:					List[Any]        =	sword
                             break

           lowerCamelCase					:					Dict        =	short_word
           lowerCamelCase					:					Dict        =	word
           return short_word

     @staticmethod
     def 		a__						(	__a:						List[str]     ,		__a:						Optional[int]	)->    str:
           lowerCamelCase					:					Tuple        =	param_name.split("""_"""	)

           lowerCamelCase					:					Dict        =	[TrialShortNamer.shortname_for_word(__a     ,		__a	) 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
           lowerCamelCase					:					List[str]        =	["""""", """_"""]

           for separator in separators:
                 lowerCamelCase					:					Any        =	separator.join(__a	)
                 if shortname not in info["reverse_short_param"]:
                       lowerCamelCase					:					List[str]        =	shortname
                       lowerCamelCase					:					int        =	param_name
                       return shortname

           return param_name

     @staticmethod
     def 		a__						(	__a:						Any     ,		__a:						Dict	)->    List[str]:
           lowerCamelCase					:					Dict        =	TrialShortNamer.shortname_for_key(__a     ,		__a	)
           lowerCamelCase					:					Optional[int]        =	short_name
           lowerCamelCase					:					Dict        =	param_name

     @classmethod
     def 		a__						(	cls:						int	)->    Optional[int]:
           if cls.NAMING_INFO is not None:
                 return

           lowerCamelCase					:					Dict        =	{
               """short_word""": {},
               """reverse_short_word""": {},
               """short_param""": {},
               """reverse_short_param""": {},
           }

           lowerCamelCase					:					int        =	list(cls.DEFAULTS.keys()	)

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

           lowerCamelCase					:					int        =	info

     @classmethod
     def 		a__						(	cls:						Tuple     ,		__a:						Union[str, Any]	)->    str:
           cls.build_naming_info()
           assert cls.PREFIX is not None
           lowerCamelCase					:					Union[str, Any]        =	[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

                 lowerCamelCase					:					Union[str, Any]        =	cls.NAMING_INFO["""short_param"""][k]

                 if isinstance(__a     ,		__a	):
                       lowerCamelCase					:					Optional[int]        =	1 if v else 0

                 lowerCamelCase					:					Tuple        =	"""""" if isinstance(__a     ,		(int, float)	) else """-"""
                 lowerCamelCase					:					List[str]        =	f'{key}{sep}{v}'
                 name.append(__a	)

           return "_".join(__a	)







     @classmethod
     def 		a__						(	cls:						Any     ,		__a:						Optional[Any]	)->    List[str]:
           lowerCamelCase					:					int        =	repr[len(cls.PREFIX	) + 1 :]
           if repr == "":
                 lowerCamelCase					:					Dict        =	[]
           else:
                 lowerCamelCase					:					List[str]        =	repr.split("""_"""	)

           lowerCamelCase					:					List[str]        =	{}

           for value in values:
                 if "-" in value:
                       lowerCamelCase					:					Any        =	value.split("""-"""	)
                 else:
                       lowerCamelCase					:					str        =	re.sub("""[0-9.]"""     ,		""""""     ,		__a	)
                       lowerCamelCase					:					Dict        =	float(re.sub("""[^0-9.]"""     ,		""""""     ,		__a	)	)

                 lowerCamelCase					:					Union[str, Any]        =	cls.NAMING_INFO["""reverse_short_param"""][p_k]

                 lowerCamelCase					:					str        =	p_v

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

           return parameters








"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__lowerCamelCase      :Any	    =		False


@skip_mps
class 			A__	(						__lowercase ,     __lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=StableDiffusionAttendAndExcitePipeline
     snake_case__	:					Any													=False
     snake_case__	:					Dict													=TEXT_TO_IMAGE_PARAMS
     snake_case__	:					Any													=TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''})
     snake_case__	:					Dict													=TEXT_TO_IMAGE_IMAGE_PARAMS
     snake_case__	:					str													=TEXT_TO_IMAGE_IMAGE_PARAMS

     @classmethod
     def 		a__						(	cls:						Dict	)->    Tuple:
           super().setUpClass()
           torch.use_deterministic_algorithms(__a	)

     @classmethod
     def 		a__						(	cls:						Union[str, Any]	)->    Any:
           super().tearDownClass()
           torch.use_deterministic_algorithms(__a	)

     def 		a__						(	self:						Tuple	)->    Union[str, Any]:
           torch.manual_seed(0	)
           lowerCamelCase					:					str        =	UNetaDConditionModel(
               block_out_channels=(32, 64)     ,		layers_per_block=1     ,		sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,		cross_attention_dim=32     ,		attention_head_dim=(2, 4)     ,		use_linear_projection=__a     ,		)
           lowerCamelCase					:					Union[str, 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					:					Union[str, 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     ,		sample_size=128     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					str        =	CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-0_5     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_000     ,		hidden_act="""gelu"""     ,		projection_dim=512     ,		)
           lowerCamelCase					:					Optional[int]        =	CLIPTextModel(__a	)
           lowerCamelCase					:					str        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

           lowerCamelCase					:					List[str]        =	{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }

           return components

     def 		a__						(	self:						Tuple     ,		__a:						int     ,		__a:						Union[str, Any]=0	)->    Optional[Any]:
           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					Tuple        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					str        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					Dict        =	{
               """prompt""": """a cat and a frog""",
               """token_indices""": [2, 5],
               """generator""": generator,
               """num_inference_steps""": 1,
               """guidance_scale""": 6.0,
               """output_type""": """numpy""",
               """max_iter_to_alter""": 2,
               """thresholds""": {0: 0.7},
           }
           return inputs

     def 		a__						(	self:						Dict	)->    str:
           lowerCamelCase					:					Tuple        =	"""cpu"""

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

           lowerCamelCase					:					Any        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Union[str, Any]        =	pipe(**__a	).images
           lowerCamelCase					:					Tuple        =	image[0, -3:, -3:, -1]

           self.assertEqual(image.shape     ,		(1, 64, 64, 3)	)
           lowerCamelCase					:					Optional[Any]        =	np.array(
               [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]	)
           lowerCamelCase					:					Optional[Any]        =	np.abs(image_slice.flatten() - expected_slice	).max()
           self.assertLessEqual(__a     ,		1e-3	)

     def 		a__						(	self:						int	)->    Optional[Any]:
           super().test_cpu_offload_forward_pass(expected_max_diff=5e-4	)

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
           self._test_inference_batch_consistent(batch_sizes=[1, 2]	)

     def 		a__						(	self:						Tuple	)->    int:
           self._test_inference_batch_single_identical(batch_size=2     ,		expected_max_diff=7e-4	)

     def 		a__						(	self:						Dict	)->    List[Any]:
           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)

     def 		a__						(	self:						Optional[int]	)->    Dict:
           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4	)

     def 		a__						(	self:						Any	)->    Tuple:
           super().test_save_load_local(expected_max_difference=5e-4	)







     def 		a__						(	self:						str	)->    str:
           super().test_save_load_optional_components(expected_max_difference=4e-4	)


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


     """simple docstring"""

     @classmethod
     def 		a__						(	cls:						Any	)->    Tuple:
           super().setUpClass()
           torch.use_deterministic_algorithms(__a	)

     @classmethod
     def 		a__						(	cls:						Dict	)->    Optional[int]:
           super().tearDownClass()
           torch.use_deterministic_algorithms(__a	)

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







     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					List[Any]        =	torch.manual_seed(51	)

           lowerCamelCase					:					List[str]        =	StableDiffusionAttendAndExcitePipeline.from_pretrained(
               """CompVis/stable-diffusion-v1-4"""     ,		safety_checker=__a     ,		torch_dtype=torch.floataa	)
           pipe.to("""cuda"""	)

           lowerCamelCase					:					Dict        =	"""a painting of an elephant with glasses"""
           lowerCamelCase					:					Any        =	[5, 7]

           lowerCamelCase					:					Tuple        =	pipe(
               prompt=__a     ,		token_indices=__a     ,		guidance_scale=7.5     ,		generator=__a     ,		num_inference_steps=5     ,		max_iter_to_alter=5     ,		output_type="""numpy"""     ,		).images[0]

           lowerCamelCase					:					Union[str, Any]        =	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""	)
           assert np.abs((expected_image - image).max()	) < 5e-1




