'''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


SCREAMING_SNAKE_CASE_	   =			False




@skip_mps
class    a (       __lowerCAmelCase						,  __lowerCAmelCase						,  __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					StableDiffusionAttendAndExcitePipeline
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					TEXT_TO_IMAGE_PARAMS
  __lowerCAmelCase				      =					TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}				)
  __lowerCAmelCase				      =					TEXT_TO_IMAGE_IMAGE_PARAMS
  __lowerCAmelCase				      =					TEXT_TO_IMAGE_IMAGE_PARAMS







  @classmethod
  def    lowercase_  (							cls					):

    '''simple docstring'''







    super().setUpClass()
    torch.use_deterministic_algorithms(snake_case_					)







  @classmethod
  def    lowercase_  (							cls					):

    '''simple docstring'''







    super().tearDownClass()
    torch.use_deterministic_algorithms(snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    torch.manual_seed(0					)
    __UpperCAmelCase:					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=snake_case_   ,    )
    __UpperCAmelCase:					List[str]		= DDIMScheduler(
        beta_start=0.0_0_0_8_5   ,    beta_end=0.0_1_2   ,    beta_schedule="""scaled_linear"""   ,    clip_sample=snake_case_   ,    set_alpha_to_one=snake_case_   ,    )
    torch.manual_seed(0					)
    __UpperCAmelCase:					Optional[int]		= 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					)
    __UpperCAmelCase:					Tuple		= 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=1000   ,    hidden_act="""gelu"""   ,    projection_dim=512   ,    )
    __UpperCAmelCase:					str		= CLIPTextModel(snake_case_					)
    __UpperCAmelCase:					List[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    lowercase_  (							self   ,    snake_case_   ,    snake_case_=0					):

    '''simple docstring'''







    if str(snake_case_					).startswith("""mps"""					):
      __UpperCAmelCase:					Optional[int]		= torch.manual_seed(snake_case_					)
    else:
      __UpperCAmelCase:					Any		= torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
    __UpperCAmelCase:					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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= """cpu"""

    __UpperCAmelCase:					int		= self.get_dummy_components()
    __UpperCAmelCase:					Dict		= self.pipeline_class(**snake_case_					)
    pipe.to(snake_case_					)
    pipe.set_progress_bar_config(disable=snake_case_					)

    __UpperCAmelCase:					Optional[Any]		= self.get_dummy_inputs(snake_case_					)
    __UpperCAmelCase:					Any		= pipe(**snake_case_					).images
    __UpperCAmelCase:					Union[str, Any]		= image[0, -3:, -3:, -1]

    self.assertEqual(image.shape   ,    (1, 64, 64, 3)					)
    __UpperCAmelCase:					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]					)
    __UpperCAmelCase:					int		= np.abs(image_slice.flatten() - expected_slice					).max()
    self.assertLessEqual(snake_case_   ,    1e-3					)







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







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




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



  """simple docstring"""







  @classmethod
  def    lowercase_  (							cls					):

    '''simple docstring'''







    super().setUpClass()
    torch.use_deterministic_algorithms(snake_case_					)







  @classmethod
  def    lowercase_  (							cls					):

    '''simple docstring'''







    super().tearDownClass()
    torch.use_deterministic_algorithms(snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= torch.manual_seed(51					)

    __UpperCAmelCase:					Any		= StableDiffusionAttendAndExcitePipeline.from_pretrained(
        """CompVis/stable-diffusion-v1-4"""   ,    safety_checker=snake_case_   ,    torch_dtype=torch.floataa					)
    pipe.to("""cuda"""					)

    __UpperCAmelCase:					Union[str, Any]		= """a painting of an elephant with glasses"""
    __UpperCAmelCase:					str		= [5, 7]

    __UpperCAmelCase:					List[str]		= pipe(
        prompt=snake_case_   ,    token_indices=snake_case_   ,    guidance_scale=7.5   ,    generator=snake_case_   ,    num_inference_steps=5   ,    max_iter_to_alter=5   ,    output_type="""numpy"""   ,    ).images[0]

    __UpperCAmelCase:					str		= 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 ....utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=None   ,    snake_case_=2048					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= config.__dict__
    __UpperCAmelCase:					str		= modal_hidden_size
    if num_labels:
      __UpperCAmelCase:					int		= num_labels