
'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							KandinskyVaaImgaImgPipeline
 lowercase_					:    Any				=							["image_embeds", "negative_image_embeds", "image"]
 lowercase_					:    int				=							[
     "image_embeds",
     "negative_image_embeds",
     "image",
 ]
 lowercase_					:    List[str]				=							[
     "generator",
     "height",
     "width",
     "strength",
     "guidance_scale",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_					:    List[Any]				=							False







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return self.time_input_dim







 @property
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   return self.time_input_dim * 4







 @property
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					int				       =							{
       "in_channels": 4,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "image",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   UpperCAmelCase__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }







 @property
 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					int				       =							VQModel(**self.dummy_movq_kwargs )
   return model







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



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.dummy_unet
   UpperCAmelCase__      :					Tuple				       =							self.dummy_movq

   UpperCAmelCase__      :					List[Any]				       =							{
       "num_train_timesteps": 10_00,
       "beta_schedule": "linear",
       "beta_start": 0.00085,
       "beta_end": 0.012,
       "clip_sample": False,
       "set_alpha_to_one": False,
       "steps_offset": 0,
       "prediction_type": "epsilon",
       "thresholding": False,
   }

   UpperCAmelCase__      :					List[str]				       =							DDIMScheduler(**snake_case__ )

   UpperCAmelCase__      :					Dict				       =							{
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[int]			,				snake_case__		:  Tuple			,				snake_case__		:  List[Any]=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(seed + 1 ) ).to(
       snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Dict				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					List[str]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Optional[Any]				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Any				       =							{
       "image": init_image,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 10,
       "guidance_scale": 7.0,
       "strength": 0.2,
       "output_type": "np",
   }
   return inputs







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							"cpu"

   UpperCAmelCase__      :					List[str]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					Optional[Any]				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					Any				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					str				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					str				       =							output.images

   UpperCAmelCase__      :					Optional[int]				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

   UpperCAmelCase__      :					Dict				       =							image[0, -3:, -3:, -1]
   UpperCAmelCase__      :					List[str]				       =							image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   UpperCAmelCase__      :					List[Any]				       =							np.array(
       [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
   assert (
       np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






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

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

   UpperCAmelCase__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							"A red cartoon frog, 4k"

   UpperCAmelCase__      :					Optional[int]				       =							KandinskyVaaPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-2-prior"			,				torch_dtype=torch.floataa )
   pipe_prior.to(snake_case__ )

   UpperCAmelCase__      :					str				       =							KandinskyVaaImgaImgPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-2-decoder"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Any				       =							pipeline.to(snake_case__ )

   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					List[Any]				       =							pipeline(
       image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				strength=0.2			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )





'''simple docstring'''





import functools
import operator

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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {
    """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_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							"wavlm"







 def __init__(					self		:  Optional[int]			,				snake_case__		:  Dict=32			,				snake_case__		:  Optional[Any]=7_68			,				snake_case__		:  Tuple=12			,				snake_case__		:  List[Any]=12			,				snake_case__		:  List[str]=30_72			,				snake_case__		:  Tuple="gelu"			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  Any=0.1			,				snake_case__		:  List[Any]=0.0			,				snake_case__		:  Dict=0.1			,				snake_case__		:  List[Any]=0.1			,				snake_case__		:  Union[str, Any]=0.02			,				snake_case__		:  int=1e-5			,				snake_case__		:  Dict="group"			,				snake_case__		:  Any="gelu"			,				snake_case__		:  List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,				snake_case__		:  int=(5, 2, 2, 2, 2, 2, 2)			,				snake_case__		:  Dict=(10, 3, 3, 3, 3, 2, 2)			,				snake_case__		:  Tuple=False			,				snake_case__		:  int=1_28			,				snake_case__		:  Dict=16			,				snake_case__		:  int=3_20			,				snake_case__		:  Dict=8_00			,				snake_case__		:  List[str]=False			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  str=0.05			,				snake_case__		:  List[Any]=10			,				snake_case__		:  Tuple=2			,				snake_case__		:  Optional[int]=0.0			,				snake_case__		:  int=10			,				snake_case__		:  Any=3_20			,				snake_case__		:  Dict=2			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  Union[str, Any]=1_00			,				snake_case__		:  Dict=2_56			,				snake_case__		:  List[Any]=2_56			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  List[Any]="mean"			,				snake_case__		:  int=False			,				snake_case__		:  Any=False			,				snake_case__		:  Any=2_56			,				snake_case__		:  Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 15_00)			,				snake_case__		:  Union[str, Any]=(5, 3, 3, 1, 1)			,				snake_case__		:  Optional[Any]=(1, 2, 3, 1, 1)			,				snake_case__		:  List[Any]=5_12			,				snake_case__		:  str=80			,				snake_case__		:  Any=0			,				snake_case__		:  str=1			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  int=False			,				snake_case__		:  Union[str, Any]=3			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  List[str]=3			,				snake_case__		:  List[Any]=None			,				**snake_case__		:  int			,				):



   '''simple docstring'''




   super().__init__(**snake_case__			,				pad_token_id=snake_case__			,				bos_token_id=snake_case__			,				eos_token_id=snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_size
   UpperCAmelCase__      :					List[str]				       =							feat_extract_norm
   UpperCAmelCase__      :					int				       =							feat_extract_activation
   UpperCAmelCase__      :					Optional[int]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							conv_bias
   UpperCAmelCase__      :					List[Any]				       =							num_buckets
   UpperCAmelCase__      :					Union[str, Any]				       =							max_bucket_distance
   UpperCAmelCase__      :					Tuple				       =							num_conv_pos_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							num_conv_pos_embedding_groups
   UpperCAmelCase__      :					Optional[Any]				       =							len(self.conv_dim )
   UpperCAmelCase__      :					Optional[int]				       =							num_hidden_layers
   UpperCAmelCase__      :					Tuple				       =							intermediate_size
   UpperCAmelCase__      :					List[str]				       =							hidden_act
   UpperCAmelCase__      :					List[str]				       =							num_attention_heads
   UpperCAmelCase__      :					Any				       =							hidden_dropout
   UpperCAmelCase__      :					List[Any]				       =							attention_dropout
   UpperCAmelCase__      :					Optional[int]				       =							activation_dropout
   UpperCAmelCase__      :					Optional[Any]				       =							feat_proj_dropout
   UpperCAmelCase__      :					List[str]				       =							final_dropout
   UpperCAmelCase__      :					int				       =							layerdrop
   UpperCAmelCase__      :					int				       =							layer_norm_eps
   UpperCAmelCase__      :					Tuple				       =							initializer_range
   UpperCAmelCase__      :					Union[str, Any]				       =							num_ctc_classes
   UpperCAmelCase__      :					Any				       =							vocab_size
   UpperCAmelCase__      :					Dict				       =							do_stable_layer_norm
   UpperCAmelCase__      :					List[Any]				       =							use_weighted_layer_sum
   UpperCAmelCase__      :					Optional[Any]				       =							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__      :					List[str]				       =							apply_spec_augment
   UpperCAmelCase__      :					Union[str, Any]				       =							mask_time_prob
   UpperCAmelCase__      :					List[Any]				       =							mask_time_length
   UpperCAmelCase__      :					List[str]				       =							mask_time_min_masks
   UpperCAmelCase__      :					List[str]				       =							mask_feature_prob
   UpperCAmelCase__      :					Dict				       =							mask_feature_length

   # parameters for pretraining with codevector quantized representations
   UpperCAmelCase__      :					Dict				       =							num_codevectors_per_group
   UpperCAmelCase__      :					Union[str, Any]				       =							num_codevector_groups
   UpperCAmelCase__      :					int				       =							contrastive_logits_temperature
   UpperCAmelCase__      :					List[str]				       =							num_negatives
   UpperCAmelCase__      :					List[str]				       =							codevector_dim
   UpperCAmelCase__      :					List[Any]				       =							proj_codevector_dim
   UpperCAmelCase__      :					List[Any]				       =							diversity_loss_weight

   # ctc loss
   UpperCAmelCase__      :					Tuple				       =							ctc_loss_reduction
   UpperCAmelCase__      :					Any				       =							ctc_zero_infinity

   # adapter
   UpperCAmelCase__      :					int				       =							add_adapter
   UpperCAmelCase__      :					List[Any]				       =							adapter_kernel_size
   UpperCAmelCase__      :					Dict				       =							adapter_stride
   UpperCAmelCase__      :					Dict				       =							num_adapter_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							output_hidden_size or hidden_size

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   UpperCAmelCase__      :					str				       =							classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   UpperCAmelCase__      :					Any				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Dict				       =							xvector_output_dim







 @property
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   return functools.reduce(operator.mul			,				self.conv_stride			,				1 )



