



'''simple docstring'''



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__  :	Optional[Any]								=	logging.get_logger(__name__)

a__  :	Optional[Any]								=	{
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class 				UpperCAmelCase__     (			UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE    					= '''wavlm'''



 def __init__(     self						,		lowercase=3_2						,		lowercase=7_6_8						,		lowercase=1_2						,		lowercase=1_2						,		lowercase=3_0_7_2						,		lowercase="gelu"						,		lowercase=0.1						,		lowercase=0.1						,		lowercase=0.1						,		lowercase=0.0						,		lowercase=0.1						,		lowercase=0.1						,		lowercase=0.02						,		lowercase=1E-5						,		lowercase="group"						,		lowercase="gelu"						,		lowercase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)						,		lowercase=(5, 2, 2, 2, 2, 2, 2)						,		lowercase=(1_0, 3, 3, 3, 3, 2, 2)						,		lowercase=False						,		lowercase=1_2_8						,		lowercase=1_6						,		lowercase=3_2_0						,		lowercase=8_0_0						,		lowercase=False						,		lowercase=True						,		lowercase=0.05						,		lowercase=1_0						,		lowercase=2						,		lowercase=0.0						,		lowercase=1_0						,		lowercase=3_2_0						,		lowercase=2						,		lowercase=0.1						,		lowercase=1_0_0						,		lowercase=2_5_6						,		lowercase=2_5_6						,		lowercase=0.1						,		lowercase="mean"						,		lowercase=False						,		lowercase=False						,		lowercase=2_5_6						,		lowercase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)						,		lowercase=(5, 3, 3, 1, 1)						,		lowercase=(1, 2, 3, 1, 1)						,		lowercase=5_1_2						,		lowercase=8_0						,		lowercase=0						,		lowercase=1						,		lowercase=2						,		lowercase=False						,		lowercase=3						,		lowercase=2						,		lowercase=3						,		lowercase=None						,		**lowercase						,		)     ->							Optional[Any]:
  super().__init__(**__SCREAMING_SNAKE_CASE						,		pad_token_id=__SCREAMING_SNAKE_CASE						,		bos_token_id=__SCREAMING_SNAKE_CASE						,		eos_token_id=__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     hidden_size
  __UpperCamelCase 					=     feat_extract_norm
  __UpperCamelCase 					=     feat_extract_activation
  __UpperCamelCase 					=     list(__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     list(__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     list(__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     conv_bias
  __UpperCamelCase 					=     num_buckets
  __UpperCamelCase 					=     max_bucket_distance
  __UpperCamelCase 					=     num_conv_pos_embeddings
  __UpperCamelCase 					=     num_conv_pos_embedding_groups
  __UpperCamelCase 					=     len(self.conv_dim	)
  __UpperCamelCase 					=     num_hidden_layers
  __UpperCamelCase 					=     intermediate_size
  __UpperCamelCase 					=     hidden_act
  __UpperCamelCase 					=     num_attention_heads
  __UpperCamelCase 					=     hidden_dropout
  __UpperCamelCase 					=     attention_dropout
  __UpperCamelCase 					=     activation_dropout
  __UpperCamelCase 					=     feat_proj_dropout
  __UpperCamelCase 					=     final_dropout
  __UpperCamelCase 					=     layerdrop
  __UpperCamelCase 					=     layer_norm_eps
  __UpperCamelCase 					=     initializer_range
  __UpperCamelCase 					=     num_ctc_classes
  __UpperCamelCase 					=     vocab_size
  __UpperCamelCase 					=     do_stable_layer_norm
  __UpperCamelCase 					=     use_weighted_layer_sum
  __UpperCamelCase 					=     classifier_proj_size

  if (
      (len(self.conv_stride	) != self.num_feat_extract_layers)
      or (len(self.conv_kernel	) != self.num_feat_extract_layers)
      or (len(self.conv_dim	) != self.num_feat_extract_layers)
  ):
   raise ValueError(
       """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
       """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
       f" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"
       f" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."	)

  # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
  __UpperCamelCase 					=     apply_spec_augment
  __UpperCamelCase 					=     mask_time_prob
  __UpperCamelCase 					=     mask_time_length
  __UpperCamelCase 					=     mask_time_min_masks
  __UpperCamelCase 					=     mask_feature_prob
  __UpperCamelCase 					=     mask_feature_length

  # parameters for pretraining with codevector quantized representations
  __UpperCamelCase 					=     num_codevectors_per_group
  __UpperCamelCase 					=     num_codevector_groups
  __UpperCamelCase 					=     contrastive_logits_temperature
  __UpperCamelCase 					=     num_negatives
  __UpperCamelCase 					=     codevector_dim
  __UpperCamelCase 					=     proj_codevector_dim
  __UpperCamelCase 					=     diversity_loss_weight

  # ctc loss
  __UpperCamelCase 					=     ctc_loss_reduction
  __UpperCamelCase 					=     ctc_zero_infinity

  # adapter
  __UpperCamelCase 					=     add_adapter
  __UpperCamelCase 					=     adapter_kernel_size
  __UpperCamelCase 					=     adapter_stride
  __UpperCamelCase 					=     num_adapter_layers
  __UpperCamelCase 					=     output_hidden_size or hidden_size

  # SequenceClassification-specific parameter. Feel free to ignore for other classes.
  __UpperCamelCase 					=     classifier_proj_size

  # XVector-specific parameters. Feel free to ignore for other classes.
  __UpperCamelCase 					=     list(__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     list(__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     list(__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     xvector_output_dim




 @property
 def       __lowerCamelCase (     self	)     ->							List[Any]:
  return functools.reduce(operator.mul						,		self.conv_stride						,		1	)








'''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 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      StableDiffusionInpaintPipeline
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__  					=      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__  					=      frozenset([]					)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE			)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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			)
      __SCREAMING_SNAKE_CASE     =      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					,							)
      __SCREAMING_SNAKE_CASE     =      CLIPTextModel(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      __SCREAMING_SNAKE_CASE     =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]=0			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)					,							rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			)[0]
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((64, 64)			)
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
      if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
           __SCREAMING_SNAKE_CASE     =      torch.manual_seed(__SCREAMING_SNAKE_CASE			)
      else:
           __SCREAMING_SNAKE_CASE     =      torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """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       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE     =      self.get_dummy_components()
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe.to(__SCREAMING_SNAKE_CASE			)
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe(**__SCREAMING_SNAKE_CASE			).images
      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

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


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







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



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> str:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE			)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 9E-3
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							safety_checker=__SCREAMING_SNAKE_CASE					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

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


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

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

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,							subfolder="""scheduler"""			)
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing(1			)
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=2					,							output_type="""np"""					,							)

      __SCREAMING_SNAKE_CASE     =      torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






