




from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)









"""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 							_UpperCAmelCase							( a		,a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     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  __lowerCAmelCase	(					cls		)							->					List[str]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





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





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,	layers_per_block=1       ,	sample_size=3_2       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,	cross_attention_dim=3_2       ,	attention_head_dim=(2, 4)       ,	use_linear_projection=A       ,	)
         _UpperCAmelCase							:  List[Any]											=    DDIMScheduler(
             beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='''scaled_linear'''       ,	clip_sample=A       ,	set_alpha_to_one=A       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,	latent_channels=4       ,	sample_size=1_2_8       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    CLIPTextConfig(
             bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=3_2       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	hidden_act='''gelu'''       ,	projection_dim=5_1_2       ,	)
         _UpperCAmelCase							:  List[str]											=    CLIPTextModel(A		)
         _UpperCAmelCase							:  str											=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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

         return components





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					List[Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Optional[int]											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    torch.Generator(device=A		).manual_seed(A		)
         _UpperCAmelCase							:  List[str]											=    {
             '''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  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[str]											=    '''cpu'''

         _UpperCAmelCase							:  Tuple											=    self.get_dummy_components()
         _UpperCAmelCase							:  int											=    self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

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

         self.assertEqual(image.shape       ,	(1, 6_4, 6_4, 3)		)
         _UpperCAmelCase							:  int											=    np.array(
             [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
         _UpperCAmelCase							:  Tuple											=    np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(A       ,	1E-3		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)





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





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(batch_size=2       ,	expected_max_diff=7E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					str:
         super().test_save_load_local(expected_max_difference=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         super().test_save_load_optional_components(expected_max_difference=4E-4		)



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





     '''simple docstring'''



     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Optional[int]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





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





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    torch.manual_seed(5_1		)

         _UpperCAmelCase							:  Optional[Any]											=    StableDiffusionAttendAndExcitePipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''       ,	safety_checker=A       ,	torch_dtype=torch.floataa		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  Optional[int]											=    '''a painting of an elephant with glasses'''
         _UpperCAmelCase							:  int											=    [5, 7]

         _UpperCAmelCase							:  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]

         _UpperCAmelCase							:  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


