"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    int	      =	StableDiffusionInpaintPipeline
     _UpperCamelCase							:    str	      =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     _UpperCamelCase							:    Optional[Any]	      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _UpperCamelCase							:    List[Any]	      =	frozenset(
         []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _UpperCamelCase							:    str	      =	frozenset([]	)






     def 					__A						(	self						):
       torch.manual_seed(0						)
       _lowerCAmelCase		: Union[str, Any]						    =  UNetaDConditionModel(
           block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=9       ,  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__       ,  )
       _lowerCAmelCase		: Optional[int]						    =  PNDMScheduler(skip_prk_steps=a__						)
       torch.manual_seed(0						)
       _lowerCAmelCase		: 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						)
       _lowerCAmelCase		: 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       ,  )
       _lowerCAmelCase		: List[str]						    =  CLIPTextModel(a__						)
       _lowerCAmelCase		: Tuple						    =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

       _lowerCAmelCase		: str						    =  {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """safety_checker""": None,
           """feature_extractor""": None,
       }
       return components






     def 					__A						(	self       ,  a__       ,  a__=0						):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       _lowerCAmelCase		: str						    =  floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(a__						)						).to(a__						)
       _lowerCAmelCase		: List[str]						    =  image.cpu().permute(0       ,  2       ,  3       ,  1						)[0]
       _lowerCAmelCase		: str						    =  Image.fromarray(np.uinta(a__						)						).convert("""RGB"""						).resize((64, 64)						)
       _lowerCAmelCase		: List[Any]						    =  Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((64, 64)						)
       if str(a__						).startswith("""mps"""						):
         _lowerCAmelCase		: Tuple						    =  torch.manual_seed(a__						)
       else:
         _lowerCAmelCase		: List[Any]						    =  torch.Generator(device=a__						).manual_seed(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  {
           """prompt""": """A painting of a squirrel eating a burger""",
           """image""": init_image,
           """mask_image""": mask_image,
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
           """output_type""": """numpy""",
       }
       return inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase		: Dict						    =  self.get_dummy_components()
       _lowerCAmelCase		: Any						    =  StableDiffusionInpaintPipeline(**a__						)
       _lowerCAmelCase		: Any						    =  sd_pipe.to(a__						)
       sd_pipe.set_progress_bar_config(disable=a__						)

       _lowerCAmelCase		: Optional[int]						    =  self.get_dummy_inputs(a__						)
       _lowerCAmelCase		: Optional[int]						    =  sd_pipe(**a__						).images
       _lowerCAmelCase		: Optional[int]						    =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _lowerCAmelCase		: int						    =  np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2






     def 					__A						(	self						):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3						)







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






     def 					__A						(	self						):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png"""						)
       _lowerCAmelCase		: List[Any]						    =  load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""						)
       _lowerCAmelCase		: List[Any]						    =  load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
           """/yellow_cat_sitting_on_a_park_bench.npy"""						)

       _lowerCAmelCase		: str						    =  """stabilityai/stable-diffusion-2-inpainting"""
       _lowerCAmelCase		: Optional[Any]						    =  StableDiffusionInpaintPipeline.from_pretrained(a__       ,  safety_checker=a__						)
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)
       pipe.enable_attention_slicing()

       _lowerCAmelCase		: Tuple						    =  """Face of a yellow cat, high resolution, sitting on a park bench"""

       _lowerCAmelCase		: Tuple						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: Optional[int]						    =  pipe(
           prompt=a__       ,  image=a__       ,  mask_image=a__       ,  generator=a__       ,  output_type="""np"""       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 9e-3






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png"""						)
       _lowerCAmelCase		: Union[str, Any]						    =  load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""						)
       _lowerCAmelCase		: Any						    =  load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
           """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""						)

       _lowerCAmelCase		: Optional[Any]						    =  """stabilityai/stable-diffusion-2-inpainting"""
       _lowerCAmelCase		: str						    =  StableDiffusionInpaintPipeline.from_pretrained(
           a__       ,  torch_dtype=torch.floataa       ,  safety_checker=a__       ,  )
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)
       pipe.enable_attention_slicing()

       _lowerCAmelCase		: int						    =  """Face of a yellow cat, high resolution, sitting on a park bench"""

       _lowerCAmelCase		: List[str]						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: Any						    =  pipe(
           prompt=a__       ,  image=a__       ,  mask_image=a__       ,  generator=a__       ,  output_type="""np"""       ,  )
       _lowerCAmelCase		: List[str]						    =  output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 5e-1






     def 					__A						(	self						):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       _lowerCAmelCase		: List[str]						    =  load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png"""						)
       _lowerCAmelCase		: List[str]						    =  load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""						)

       _lowerCAmelCase		: Optional[Any]						    =  """stabilityai/stable-diffusion-2-inpainting"""
       _lowerCAmelCase		: Optional[int]						    =  PNDMScheduler.from_pretrained(a__       ,  subfolder="""scheduler"""						)
       _lowerCAmelCase		: Optional[int]						    =  StableDiffusionInpaintPipeline.from_pretrained(
           a__       ,  safety_checker=a__       ,  scheduler=a__       ,  torch_dtype=torch.floataa       ,  )
       pipe.to(a__						)
       pipe.set_progress_bar_config(disable=a__						)
       pipe.enable_attention_slicing(1						)
       pipe.enable_sequential_cpu_offload()

       _lowerCAmelCase		: Tuple						    =  """Face of a yellow cat, high resolution, sitting on a park bench"""

       _lowerCAmelCase		: Optional[Any]						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: str						    =  pipe(
           prompt=a__       ,  image=a__       ,  mask_image=a__       ,  generator=a__       ,  num_inference_steps=2       ,  output_type="""np"""       ,  )

       _lowerCAmelCase		: int						    =  torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.6_5 * 10**9





"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple			) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  int(_lowerCamelCase			)
  assert noofclusters < len(_lowerCamelCase			)

  # Find out the dimensionality
  _lowerCAmelCase		: Any						    =  len(vectors[0]			)

  # Will help select random centroids from among the available vectors
  _lowerCAmelCase		: Any						    =  list(range(len(_lowerCamelCase			)			)			)
  shuffle(_lowerCamelCase			)

  # GRAPH OF COMPUTATION
  # We initialize a new graph and set it as the default during each run
  # of this algorithm. This ensures that as this function is called
  # multiple times, the default graph doesn't keep getting crowded with
  # unused ops and Variables from previous function calls.

  _lowerCAmelCase		: List[Any]						    =  tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    _lowerCAmelCase		: str						    =  tf.Session()

    ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

    ##First lets ensure we have a Variable vector for each centroid,
    ##initialized to one of the vectors from the available data points
    _lowerCAmelCase		: List[str]						    =  [
        tf.Variable(vectors[vector_indices[i]]			) for i in range(_lowerCamelCase			)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float64"""    ,[dim]			)
    _lowerCAmelCase		: Optional[int]						    =  []
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    _lowerCAmelCase		: Dict						    =  [tf.Variable(0			) for i in range(len(_lowerCamelCase			)			)]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    _lowerCAmelCase		: List[Any]						    =  tf.placeholder("""int32"""			)
    _lowerCAmelCase		: Any						    =  []
    for assignment in assignments:
      cluster_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float"""    ,[None, dim]			)
    # The Node/op takes the input and computes a mean along the 0th
    # dimension, i.e. the list of input vectors
    _lowerCAmelCase		: Optional[int]						    =  tf.reduce_mean(_lowerCamelCase    ,0			)

    ##Node for computing Euclidean distances
    # Placeholders for input
    _lowerCAmelCase		: Dict						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: List[Any]						    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase    ,_lowerCamelCase			)    ,2			)			)			)

    ##This node will figure out which cluster to assign a vector to,
    ##based on Euclidean distances of the vector from the centroids.
    # Placeholder for input
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[noofclusters]			)
    _lowerCAmelCase		: str						    =  tf.argmin(_lowerCamelCase    ,0			)

    ##INITIALIZING STATE VARIABLES

    ##This will help initialization of all Variables defined with respect
    ##to the graph. The Variable-initializer should be defined after
    ##all the Variables have been constructed, so that each of them
    ##will be included in the initialization.
    _lowerCAmelCase		: Optional[Any]						    =  tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_lowerCamelCase			)

    ##CLUSTERING ITERATIONS

    # Now perform the Expectation-Maximization steps of K-Means clustering
    # iterations. To keep things simple, we will only do a set number of
    # iterations, instead of using a Stopping Criterion.
    _lowerCAmelCase		: List[str]						    =  100
    for _ in range(_lowerCamelCase			):
      ##EXPECTATION STEP
      ##Based on the centroid locations till last iteration, compute
      ##the _expected_ centroid assignments.
      # Iterate over each vector
      for vector_n in range(len(_lowerCamelCase			)			):
        _lowerCAmelCase		: int						    =  vectors[vector_n]
        # Compute Euclidean distance between this vector and each
        # centroid. Remember that this list cannot be named
        #'centroid_distances', since that is the input to the
        # cluster assignment node.
        _lowerCAmelCase		: Any						    =  [
            sess.run(_lowerCamelCase    ,feed_dict={va: vect, va: sess.run(_lowerCamelCase			)}			)
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        _lowerCAmelCase		: Any						    =  sess.run(
            _lowerCamelCase    ,feed_dict={centroid_distances: distances}			)
        # Now assign the value to the appropriate state variable
        sess.run(
            cluster_assigns[vector_n]    ,feed_dict={assignment_value: assignment}			)

      ##MAXIMIZATION STEP
      # Based on the expected state computed from the Expectation Step,
      # compute the locations of the centroids so as to maximize the
      # overall objective of minimizing within-cluster Sum-of-Squares
      for cluster_n in range(_lowerCamelCase			):
        # Collect all the vectors assigned to this cluster
        _lowerCAmelCase		: List[Any]						    =  [
            vectors[i]
            for i in range(len(_lowerCamelCase			)			)
            if sess.run(assignments[i]			) == cluster_n
        ]
        # Compute new centroid location
        _lowerCAmelCase		: Optional[int]						    =  sess.run(
            _lowerCamelCase    ,feed_dict={mean_input: array(_lowerCamelCase			)}			)
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]    ,feed_dict={centroid_value: new_location}			)

        # Return centroids and assignments
    _lowerCAmelCase		: Optional[int]						    =  sess.run(_lowerCamelCase			)
    _lowerCAmelCase		: List[Any]						    =  sess.run(_lowerCamelCase			)
    return centroids, assignments




