


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  KandinskyVaaPriorPipeline
 __snake_case												=  ["prompt"]
 __snake_case												=  ["prompt", "negative_prompt"]
 __snake_case												=  [
     "num_images_per_prompt",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case												=  False



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       return self.time_input_dim



 @property
 def 						UpperCamelCase__					(     self ):
       return self.time_input_dim * 4



 @property
 def 						UpperCamelCase__					(     self ):
       return 1_00



 @property
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
       return tokenizer



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=self.text_embedder_hidden_size		,				projection_dim=self.text_embedder_hidden_size		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				)
       return CLIPTextModelWithProjection(_UpperCAmelCase )



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )

       snake_case_	=				{
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_	=				PriorTransformer(**_UpperCAmelCase )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case_	=				nn.Parameter(torch.ones(model.clip_std.shape ) )
       return model



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size		,				image_size=2_24		,				projection_dim=self.text_embedder_hidden_size		,				intermediate_size=37		,				num_attention_heads=4		,				num_channels=3		,				num_hidden_layers=5		,				patch_size=14		,				)

       snake_case_	=				CLIPVisionModelWithProjection(_UpperCAmelCase )
       return model



 @property
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				CLIPImageProcessor(
           crop_size=2_24		,				do_center_crop=_UpperCAmelCase		,				do_normalize=_UpperCAmelCase		,				do_resize=_UpperCAmelCase		,				image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,				image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,				resample=3		,				size=2_24		,				)

       return image_processor



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.dummy_prior
       snake_case_	=				self.dummy_image_encoder
       snake_case_	=				self.dummy_text_encoder
       snake_case_	=				self.dummy_tokenizer
       snake_case_	=				self.dummy_image_processor

       snake_case_	=				UnCLIPScheduler(
           variance_type='''fixed_small_log'''		,				prediction_type='''sample'''		,				num_train_timesteps=10_00		,				clip_sample=_UpperCAmelCase		,				clip_sample_range=10.0		,				)

       snake_case_	=				{
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''

       snake_case_	=				self.get_dummy_components()

       snake_case_	=				self.pipeline_class(**_UpperCAmelCase )
       snake_case_	=				pipe.to(_UpperCAmelCase )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
       snake_case_	=				output.image_embeds

       snake_case_	=				pipe(
           **self.get_dummy_inputs(_UpperCAmelCase )		,				return_dict=_UpperCAmelCase		,				)[0]

       snake_case_	=				image[0, -10:]
       snake_case_	=				image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case_	=				np.array(
           [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156] )

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



 @skip_mps
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				torch_device == '''cpu'''
       snake_case_	=				True
       snake_case_	=				False

       self._test_inference_batch_single_identical(
           test_max_difference=_UpperCAmelCase		,				relax_max_difference=_UpperCAmelCase		,				test_mean_pixel_difference=_UpperCAmelCase		,				)





 @skip_mps
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				torch_device == '''cpu'''
       snake_case_	=				False

       self._test_attention_slicing_forward_pass(
           test_max_difference=_UpperCAmelCase		,				test_mean_pixel_difference=_UpperCAmelCase		,				)



import functools
import operator

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


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "wavlm"



 def __init__(     self		,				_UpperCAmelCase=32		,				_UpperCAmelCase=7_68		,				_UpperCAmelCase=12		,				_UpperCAmelCase=12		,				_UpperCAmelCase=30_72		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase="group"		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,				_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,				_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,				_UpperCAmelCase=False		,				_UpperCAmelCase=1_28		,				_UpperCAmelCase=16		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=8_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.05		,				_UpperCAmelCase=10		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=10		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="mean"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,				_UpperCAmelCase=(5, 3, 3, 1, 1)		,				_UpperCAmelCase=(1, 2, 3, 1, 1)		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=80		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=False		,				_UpperCAmelCase=3		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase		,				pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase )
       snake_case_	=				hidden_size
       snake_case_	=				feat_extract_norm
       snake_case_	=				feat_extract_activation
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				conv_bias
       snake_case_	=				num_buckets
       snake_case_	=				max_bucket_distance
       snake_case_	=				num_conv_pos_embeddings
       snake_case_	=				num_conv_pos_embedding_groups
       snake_case_	=				len(self.conv_dim )
       snake_case_	=				num_hidden_layers
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				num_attention_heads
       snake_case_	=				hidden_dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				feat_proj_dropout
       snake_case_	=				final_dropout
       snake_case_	=				layerdrop
       snake_case_	=				layer_norm_eps
       snake_case_	=				initializer_range
       snake_case_	=				num_ctc_classes
       snake_case_	=				vocab_size
       snake_case_	=				do_stable_layer_norm
       snake_case_	=				use_weighted_layer_sum
       snake_case_	=				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
       snake_case_	=				apply_spec_augment
       snake_case_	=				mask_time_prob
       snake_case_	=				mask_time_length
       snake_case_	=				mask_time_min_masks
       snake_case_	=				mask_feature_prob
       snake_case_	=				mask_feature_length

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

       # ctc loss
       snake_case_	=				ctc_loss_reduction
       snake_case_	=				ctc_zero_infinity

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

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

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





 @property
 def 						UpperCamelCase__					(     self ):
       return functools.reduce(operator.mul		,				self.conv_stride		,				1 )