


'''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


UpperCAmelCase_ :     Any								=      False

@skip_mps
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		List[Any]    							=						StableDiffusionAttendAndExcitePipeline
 __lowercase				:		str    							=						False
 __lowercase				:		List[Any]    							=						TEXT_TO_IMAGE_PARAMS
 __lowercase				:		Union[str, Any]    							=						TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}				)
 __lowercase				:		List[str]    							=						TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowercase				:		Optional[Any]    							=						TEXT_TO_IMAGE_IMAGE_PARAMS


 @classmethod
 def  snake_case__   (      cls						):

  """simple docstring"""







  super().setUpClass()
  torch.use_deterministic_algorithms(__lowercase						)


 @classmethod
 def  snake_case__   (      cls						):

  """simple docstring"""







  super().tearDownClass()
  torch.use_deterministic_algorithms(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[Any]       =			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=__lowercase      ,      )
  __A  :		List[str]       =			DDIMScheduler(
      beta_start=0.0_0_0_8_5      ,      beta_end=0.0_1_2      ,      beta_schedule='scaled_linear'      ,      clip_sample=__lowercase      ,      set_alpha_to_one=__lowercase      ,      )
  torch.manual_seed(0						)
  __A  :		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						)
  __A  :		List[Any]       =			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=1_000      ,      hidden_act='gelu'      ,      projection_dim=512      ,      )
  __A  :		List[str]       =			CLIPTextModel(__lowercase						)
  __A  :		Optional[Any]       =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

  __A  :		Optional[int]       =			{
      'unet': unet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }

  return components


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0						):

  """simple docstring"""







  if str(__lowercase						).startswith('mps'						):
   __A  :		Tuple       =			torch.manual_seed(__lowercase						)
  else:
   __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
  __A  :		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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			'cpu'

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

  __A  :		Optional[Any]       =			self.get_dummy_inputs(__lowercase						)
  __A  :		Tuple       =			pipe(**__lowercase						).images
  __A  :		Any       =			image[0, -3:, -3:, -1]

  self.assertEqual(image.shape      ,      (1, 64, 64, 3)						)
  __A  :		List[Any]       =			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  :		Optional[int]       =			np.abs(image_slice.flatten() - expected_slice						).max()
  self.assertLessEqual(__lowercase      ,      1E-3						)


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







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


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




 '''simple docstring'''


 @classmethod
 def  snake_case__   (      cls						):

  """simple docstring"""







  super().setUpClass()
  torch.use_deterministic_algorithms(__lowercase						)


 @classmethod
 def  snake_case__   (      cls						):

  """simple docstring"""







  super().tearDownClass()
  torch.use_deterministic_algorithms(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			torch.manual_seed(51						)

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

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

  __A  :		Dict       =			pipe(
      prompt=__lowercase      ,      token_indices=__lowercase      ,      guidance_scale=7.5      ,      generator=__lowercase      ,      num_inference_steps=5      ,      max_iter_to_alter=5      ,      output_type='numpy'      ,      ).images[0]

  __A  :		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'''
import math

class 						_lowerCamelCase					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			0.0
  __A  :		Optional[int]       =			0.0
  for i in range(len(__lowercase						)						):
   da += math.pow((sample[i] - weights[0][i])      ,      2						)
   da += math.pow((sample[i] - weights[1][i])      ,      2						)
   return 0 if da > da else 1
  return 0







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  for i in range(len(__lowercase						)						):
   weights[j][i] += alpha * (sample[i] - weights[j][i])
  return weights


def 			_lowercase       (				):
 # Training Examples ( m, n )
 __A  :		Union[str, Any]       =			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

 # weight initialization ( n, C )
 __A  :		Tuple       =			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

 # training
 __A  :		Optional[int]       =			SelfOrganizingMap()
 __A  :		int       =			3
 __A  :		Optional[Any]       =			0.5

 for _ in range(UpperCamelCase__			):
  for j in range(len(UpperCamelCase__			)			):
   # training sample
   __A  :		Dict       =			training_samples[j]

   # Compute the winning vector
   __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

   # Update the winning vector
   __A  :		Union[str, Any]       =			self_organizing_map.update(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

    # classify test sample
 __A  :		int       =			[0, 0, 0, 1]
 __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

 # results
 print(f"""Clusters that the test sample belongs to : {winner}"""			)
 print(f"""Weights that have been trained : {weights}"""			)


# running the main() function
if __name__ == "__main__":
 main()






