



"""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


__a       :	int             = False







@skip_mps
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =StableDiffusionAttendAndExcitePipeline
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_PARAMS
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}							)
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_IMAGE_PARAMS
							_SCREAMING_SNAKE_CASE    =TEXT_TO_IMAGE_IMAGE_PARAMS




							@classmethod
							def  lowercase		(   cls:     List[str]    ):

												'''simple docstring'''

												super().setUpClass()
												torch.use_deterministic_algorithms(__A    )




							@classmethod
							def  lowercase		(   cls:     str    ):

												'''simple docstring'''

												super().tearDownClass()
												torch.use_deterministic_algorithms(__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												torch.manual_seed(0    )
												a__          =  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				,		)
												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=[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    )
												a__          =  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=1000				,		hidden_act='''gelu'''				,		projection_dim=512				,		)
												a__          =  CLIPTextModel(__A    )
												a__          =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

												a__          =  {
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}

												return components




							def  lowercase		(   self:     int				,		__A:     List[Any]				,		__A:     int=0    ):

												'''simple docstring'''

												if str(__A    ).startswith('''mps'''    ):
																	a__          =  torch.manual_seed(__A    )
												else:
																	a__          =  torch.Generator(device=__A    ).manual_seed(__A    )
												a__          =  a__          =  {
												    '''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  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  '''cpu'''

												a__          =  self.get_dummy_components()
												a__          =  self.pipeline_class(**__A    )
												pipe.to(__A    )
												pipe.set_progress_bar_config(disable=__A    )

												a__          =  self.get_dummy_inputs(__A    )
												a__          =  pipe(**__A    ).images
												a__          =  image[0, -3:, -3:, -1]

												self.assertEqual(image.shape				,		(1, 64, 64, 3)    )
												a__          =  np.array(
												    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]    )
												a__          =  np.abs(image_slice.flatten() - expected_slice    ).max()
												self.assertLessEqual(__A				,		1e-3    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												super().test_cpu_offload_forward_pass(expected_max_diff=5e-4    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												self._test_inference_batch_consistent(batch_sizes=[1, 2]    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												self._test_inference_batch_single_identical(batch_size=2				,		expected_max_diff=7e-4    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												super().test_save_load_local(expected_max_difference=5e-4    )





							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												super().test_save_load_optional_components(expected_max_difference=4e-4    )







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





							"""simple docstring"""





							@classmethod
							def  lowercase		(   cls:     Any    ):

												'''simple docstring'''

												super().setUpClass()
												torch.use_deterministic_algorithms(__A    )




							@classmethod
							def  lowercase		(   cls:     int    ):

												'''simple docstring'''

												super().tearDownClass()
												torch.use_deterministic_algorithms(__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  torch.manual_seed(51    )

												a__          =  StableDiffusionAttendAndExcitePipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''				,		safety_checker=__A				,		torch_dtype=torch.floataa    )
												pipe.to('''cuda'''    )

												a__          =  '''a painting of an elephant with glasses'''
												a__          =  [5, 7]

												a__          =  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]

												a__          =  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










"""simple docstring"""







import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
	pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	List[Any]             = logging.get_logger(__name__)

__a       :	Tuple             = {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_text_model'




							def __init__(   self:     Tuple				,		__A:     Optional[int]=30522				,		__A:     Tuple=768				,		__A:     Any=12				,		__A:     Any=12				,		__A:     Dict=3072				,		__A:     Tuple="gelu"				,		__A:     Union[str, Any]=0.1				,		__A:     List[str]=0.1				,		__A:     Optional[int]=512				,		__A:     Tuple=2				,		__A:     str=0.0_2				,		__A:     int=1e-12				,		__A:     Optional[int]=0				,		__A:     Optional[int]="absolute"				,		__A:     List[Any]=True				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  hidden_act
												a__          =  intermediate_size
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  position_embedding_type
												a__          =  use_cache
												a__          =  pad_token_id





							@classmethod
							def  lowercase		(   cls:     Any				,		__A:     Union[str, os.PathLike]				,		**__A:     Tuple    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the text config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_vision_model'




							def __init__(   self:     Dict				,		__A:     int = 3				,		__A:     int = 600				,		__A:     float = 2.0				,		__A:     float = 3.1				,		__A:     int = 8				,		__A:     List[int] = [3, 3, 5, 3, 5, 5, 3]				,		__A:     List[int] = [32, 16, 24, 40, 80, 112, 192]				,		__A:     List[int] = [16, 24, 40, 80, 112, 192, 320]				,		__A:     List[int] = []				,		__A:     List[int] = [1, 2, 2, 2, 1, 2, 1]				,		__A:     List[int] = [1, 2, 2, 3, 3, 4, 1]				,		__A:     List[int] = [1, 6, 6, 6, 6, 6, 6]				,		__A:     float = 0.2_5				,		__A:     str = "swish"				,		__A:     int = 2560				,		__A:     str = "mean"				,		__A:     float = 0.0_2				,		__A:     float = 0.0_0_1				,		__A:     float = 0.9_9				,		__A:     float = 0.2				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  num_channels
												a__          =  image_size
												a__          =  width_coefficient
												a__          =  depth_coefficient
												a__          =  depth_divisor
												a__          =  kernel_sizes
												a__          =  in_channels
												a__          =  out_channels
												a__          =  depthwise_padding
												a__          =  strides
												a__          =  num_block_repeats
												a__          =  expand_ratios
												a__          =  squeeze_expansion_ratio
												a__          =  hidden_act
												a__          =  hidden_dim
												a__          =  pooling_type
												a__          =  initializer_range
												a__          =  batch_norm_eps
												a__          =  batch_norm_momentum
												a__          =  drop_connect_rate
												a__          =  sum(__A    ) * 4





							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     Union[str, os.PathLike]				,		**__A:     List[Any]    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the vision config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align'
							_SCREAMING_SNAKE_CASE    =True




							def __init__(   self:     Optional[int]				,		__A:     Optional[int]=None				,		__A:     Dict=None				,		__A:     List[str]=640				,		__A:     Optional[int]=1.0				,		__A:     str=0.0_2				,		**__A:     List[str]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												if text_config is None:
																	a__          =  {}
																	logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''    )

												if vision_config is None:
																	a__          =  {}
																	logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''    )

												a__          =  AlignTextConfig(**__A    )
												a__          =  AlignVisionConfig(**__A    )

												a__          =  projection_dim
												a__          =  temperature_init_value
												a__          =  initializer_range




							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     AlignTextConfig				,		__A:     AlignVisionConfig				,		**__A:     str    ):

												'''simple docstring'''

												return cls(text_config=text_config.to_dict()				,		vision_config=vision_config.to_dict()				,		**__A    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  copy.deepcopy(self.__dict__    )
												a__          =  self.text_config.to_dict()
												a__          =  self.vision_config.to_dict()
												a__          =  self.__class__.model_type
												return output


