
"""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__					(						A__   ,							A__   ,							A__   ,							unittest.TestCase       ):
     UpperCAmelCase__         =      StableDiffusionAttendAndExcitePipeline
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      TEXT_TO_IMAGE_PARAMS
     UpperCAmelCase__         =      TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}       )
     UpperCAmelCase__         =      TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__         =      TEXT_TO_IMAGE_IMAGE_PARAMS



     @classmethod
     def  lowerCamelCase_						(							cls :Optional[int]   ):







      '''simple docstring'''



      super().setUpClass()
      torch.use_deterministic_algorithms(_lowerCamelCase   )



     @classmethod
     def  lowerCamelCase_						(							cls :int   ):







      '''simple docstring'''



      super().tearDownClass()
      torch.use_deterministic_algorithms(_lowerCamelCase   )



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







      '''simple docstring'''



      torch.manual_seed(0   )
      UpperCamelCase_     :					int   	=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=_lowerCamelCase      ,  )
      UpperCamelCase_     :					List[Any]   	=DDIMScheduler(
          beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule='scaled_linear'      ,  clip_sample=_lowerCamelCase      ,  set_alpha_to_one=_lowerCamelCase      ,  )
      torch.manual_seed(0   )
      UpperCamelCase_     :					Tuple   	=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_     :					Optional[Any]   	=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=1_000      ,  hidden_act='gelu'      ,  projection_dim=512      ,  )
      UpperCamelCase_     :					Any   	=CLIPTextModel(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[int]   	=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 :Any      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :Any=0   ):







      '''simple docstring'''



      if str(_lowerCamelCase   ).startswith('mps'   ):
       UpperCamelCase_     :					int   	=torch.manual_seed(_lowerCamelCase   )
      else:
       UpperCamelCase_     :					Optional[int]   	=torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
      UpperCamelCase_     :					Dict   	={
          '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 :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	='cpu'

      UpperCamelCase_     :					str   	=self.get_dummy_components()
      UpperCamelCase_     :					Dict   	=self.pipeline_class(**_lowerCamelCase   )
      pipe.to(_lowerCamelCase   )
      pipe.set_progress_bar_config(disable=_lowerCamelCase   )

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

      self.assertEqual(image.shape      ,  (1, 64, 64, 3)   )
      UpperCamelCase_     :					Dict   	=np.array(
          [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]   )
      UpperCamelCase_     :					List[str]   	=np.abs(image_slice.flatten() - expected_slice   ).max()
      self.assertLessEqual(_lowerCamelCase      ,  1E-3   )



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



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



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







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



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





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



     @classmethod
     def  lowerCamelCase_						(							cls :int   ):







      '''simple docstring'''



      super().setUpClass()
      torch.use_deterministic_algorithms(_lowerCamelCase   )



     @classmethod
     def  lowerCamelCase_						(							cls :Tuple   ):







      '''simple docstring'''



      super().tearDownClass()
      torch.use_deterministic_algorithms(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=torch.manual_seed(51   )

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

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

      UpperCamelCase_     :					Optional[int]   	=pipe(
          prompt=_lowerCamelCase      ,  token_indices=_lowerCamelCase      ,  guidance_scale=7.5      ,  generator=_lowerCamelCase      ,  num_inference_steps=5      ,  max_iter_to_alter=5      ,  output_type='numpy'      ,  ).images[0]

      UpperCamelCase_     :					Optional[int]   	=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 manim import *





class 				a__					(						A__       ):



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=Rectangle(height=0.5      ,  width=0.5   )
      UpperCamelCase_     :					Any   	=Rectangle(height=0.46      ,  width=0.46   ).set_stroke(width=0   )

      UpperCamelCase_     :					Any   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					Optional[int]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					List[str]   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					Optional[int]   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					Union[str, Any]   	=VGroup(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					str   	=Text('CPU'      ,  font_size=24   )
      UpperCamelCase_     :					str   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      cpu.move_to([-2.5, -0.5, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[mem.copy() for i in range(4   )]
      UpperCamelCase_     :					Any   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					List[Any]   	=Text('GPU'      ,  font_size=24   )
      UpperCamelCase_     :					Any   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      gpu.move_to([-1, -1, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					str   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )

      UpperCamelCase_     :					Optional[int]   	=Text('Model'      ,  font_size=24   )
      UpperCamelCase_     :					List[Any]   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      model.move_to([3, -1.0, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[]
      for i, rect in enumerate(_lowerCamelCase   ):
       rect.set_stroke(_lowerCamelCase   )
       # target = fill.copy().set_fill(YELLOW, opacity=0.7)
       # target.move_to(rect)
       # self.add(target)

       UpperCamelCase_     :					str   	=Rectangle(height=0.46 / 4      ,  width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(_lowerCamelCase      ,  opacity=0.7   )

       if i == 0:
        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )      ,  buff=0.02      ,  direction=_lowerCamelCase   )
        cpu_target.set_x(cpu_target.get_x() + 0.1   )
       elif i == 3:
        cpu_target.next_to(cpu_targs[0]      ,  direction=_lowerCamelCase      ,  buff=0.0   )
       else:
        cpu_target.next_to(cpu_targs[i - 1]      ,  direction=_lowerCamelCase      ,  buff=0.0   )
       self.add(_lowerCamelCase   )
       cpu_targs.append(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					Any   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )

      UpperCamelCase_     :					Optional[int]   	=Text('Loaded Checkpoint'      ,  font_size=24   )
      UpperCamelCase_     :					List[Any]   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  aligned_edge=_lowerCamelCase      ,  buff=0.4   )
      checkpoint.move_to([3, 0.5, 0]   )

      UpperCamelCase_     :					int   	=Square(side_length=2.2   )
      key.move_to([-5, 2, 0]   )

      UpperCamelCase_     :					Optional[Any]   	=MarkupText(
          f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''      ,  font_size=18      ,  )

      key_text.move_to([-5, 2.4, 0]   )

      self.add(_lowerCamelCase      ,  _lowerCamelCase   )

      UpperCamelCase_     :					Any   	=MarkupText(
          f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''      ,  font_size=18      ,  )

      blue_text.next_to(_lowerCamelCase      ,  DOWN * 2.4      ,  aligned_edge=key_text.get_left()   )

      UpperCamelCase_     :					Dict   	=MarkupText(
          f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'''      ,  font_size=24      ,  )
      step_a.move_to([2, 2, 0]   )
      self.play(Write(_lowerCamelCase   )      ,  Write(_lowerCamelCase   )   )

      self.play(Write(_lowerCamelCase      ,  run_time=1   )      ,  Create(_lowerCamelCase      ,  run_time=1   )   )

      UpperCamelCase_     :					Union[str, Any]   	=[]
      UpperCamelCase_     :					List[Any]   	=[]
      for i, rect in enumerate(_lowerCamelCase   ):
       UpperCamelCase_     :					str   	=fill.copy().set_fill(_lowerCamelCase      ,  opacity=0.7   )
       target.move_to(_lowerCamelCase   )
       first_animations.append(GrowFromCenter(_lowerCamelCase      ,  run_time=1   )   )

       UpperCamelCase_     :					Any   	=target.copy()
       cpu_target.generate_target()
       if i < 5:
        cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
       else:
        cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
       second_animations.append(MoveToTarget(_lowerCamelCase      ,  run_time=1.5   )   )

      self.play(*_lowerCamelCase   )
      self.play(*_lowerCamelCase   )
      self.wait()
