



'''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__    :   Any           =					False



@skip_mps
class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= StableDiffusionAttendAndExcitePipeline
 A__				= False
 A__				= TEXT_TO_IMAGE_PARAMS
 A__				= TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}				)
 A__				= TEXT_TO_IMAGE_IMAGE_PARAMS
 A__				= TEXT_TO_IMAGE_IMAGE_PARAMS
 @classmethod
 def A_   (   cls						:							Any  )      ->   Union[str, Any]:







       '''simple docstring'''
       super().setUpClass()
       torch.use_deterministic_algorithms(__a  )
 @classmethod
 def A_   (   cls						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       super().tearDownClass()
       torch.use_deterministic_algorithms(__a  )
 def A_   (   self						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   Tuple													=    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      ,     )
       __snake_case :   str													=    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  )
       __snake_case :   List[str]													=    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  )
       __snake_case :   Optional[int]													=    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      ,     )
       __snake_case :   str													=    CLIPTextModel(__a  )
       __snake_case :   Dict													=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

       __snake_case :   Union[str, Any]													=    {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }

       return components
 def A_   (   self						:							Optional[int]      ,     __a						:							Tuple      ,     __a						:							Dict=0  )      ->   List[str]:







       '''simple docstring'''
       if str(__a  ).startswith('mps'  ):
             __snake_case :   Optional[Any]													=    torch.manual_seed(__a  )
       else:
             __snake_case :   int													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   Union[str, Any]													=    {
           '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						:							Optional[int]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   int													=    'cpu'

       __snake_case :   int													=    self.get_dummy_components()
       __snake_case :   Optional[int]													=    self.pipeline_class(**__a  )
       pipe.to(__a  )
       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   List[Any]													=    self.get_dummy_inputs(__a  )
       __snake_case :   Tuple													=    pipe(**__a  ).images
       __snake_case :   int													=    image[0, -3:, -3:, -1]

       self.assertEqual(image.shape      ,     (1, 64, 64, 3)  )
       __snake_case :   Dict													=    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]  )
       __snake_case :   Tuple													=    np.abs(image_slice.flatten() - expected_slice  ).max()
       self.assertLessEqual(__a      ,     1e-3  )
 def A_   (   self						:							Tuple  )      ->   List[Any]:







       '''simple docstring'''
       super().test_cpu_offload_forward_pass(expected_max_diff=5e-4  )
 def A_   (   self						:							List[Any]  )      ->   str:







       '''simple docstring'''
       # 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						:							List[Any]  )      ->   Any:







       '''simple docstring'''
       self._test_inference_batch_single_identical(batch_size=2      ,     expected_max_diff=7e-4  )
 def A_   (   self						:							Union[str, Any]  )      ->   Dict:







       '''simple docstring'''
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )
 def A_   (   self						:							Any  )      ->   Union[str, Any]:







       '''simple docstring'''
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4  )
 def A_   (   self						:							Dict  )      ->   Dict:







       '''simple docstring'''
       super().test_save_load_local(expected_max_difference=5e-4  )





 def A_   (   self						:							Optional[Any]  )      ->   int:







       '''simple docstring'''
       super().test_save_load_optional_components(expected_max_difference=4e-4  )



@require_torch_gpu
@slow
class  snake_case__						(    unittest.TestCase				):
 @classmethod
 def A_   (   cls						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       super().setUpClass()
       torch.use_deterministic_algorithms(__a  )
 @classmethod
 def A_   (   cls						:							Optional[Any]  )      ->   str:







       '''simple docstring'''
       super().tearDownClass()
       torch.use_deterministic_algorithms(__a  )
 def A_   (   self						:							Any  )      ->   List[str]:







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





 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    torch.manual_seed(51  )

       __snake_case :   Dict													=    StableDiffusionAttendAndExcitePipeline.from_pretrained(
           'CompVis/stable-diffusion-v1-4'      ,     safety_checker=__a      ,     torch_dtype=torch.floataa  )
       pipe.to('cuda'  )

       __snake_case :   List[Any]													=    'a painting of an elephant with glasses'
       __snake_case :   int													=    [5, 7]

       __snake_case :   Dict													=    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]

       __snake_case :   List[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








'''simple docstring'''





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[str]           =					{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Optional[Any]           =					[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    A__    :   Optional[int]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



