
"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class __lowerCAmelCase		(				__snake_case      ,  unittest.TestCase      ):
     lowercase						       =						DiTPipeline
     lowercase						       =						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
     lowercase						       =						PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     lowercase						       =						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	TransformeraDModel(
          sample_size=16     ,					num_layers=2     ,					patch_size=4     ,					attention_head_dim=8     ,					num_attention_heads=2     ,					in_channels=4     ,					out_channels=8     ,					attention_bias=lowerCamelCase_     ,					activation_fn='gelu-approximate'     ,					num_embeds_ada_norm=1000     ,					norm_type='ada_norm_zero'     ,					norm_elementwise_affine=lowerCamelCase_     ,					)
      __UpperCamelCase      =	AutoencoderKL()
      __UpperCamelCase      =	DDIMScheduler()
      __UpperCamelCase      =	{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
      return components





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

      '''simple docstring'''




      if str(lowerCamelCase_				).startswith('mps'				):
       __UpperCamelCase      =	torch.manual_seed(lowerCamelCase_				)
      else:
       __UpperCamelCase      =	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
      __UpperCamelCase      =	{
          """class_labels""": [1],
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	"""cpu"""

      __UpperCamelCase      =	self.get_dummy_components()
      __UpperCamelCase      =	self.pipeline_class(**lowerCamelCase_				)
      pipe.to(lowerCamelCase_				)
      pipe.set_progress_bar_config(disable=lowerCamelCase_				)

      __UpperCamelCase      =	self.get_dummy_inputs(lowerCamelCase_				)
      __UpperCamelCase      =	pipe(**lowerCamelCase_				).images
      __UpperCamelCase      =	image[0, -3:, -3:, -1]

      self.assertEqual(image.shape     ,					(1, 16, 16, 3)				)
      __UpperCamelCase      =	np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]				)
      __UpperCamelCase      =	np.abs(image_slice.flatten() - expected_slice				).max()
      self.assertLessEqual(lowerCamelCase_     ,					1E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase_     ,					expected_max_diff=1E-3				)







     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()     ,					reason='XFormers attention is only available with CUDA and `xformers` installed'     ,					)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)


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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	torch.manual_seed(0				)

      __UpperCamelCase      =	DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'				)
      pipe.to('cuda'				)

      __UpperCamelCase      =	["""vase""", """umbrella""", """white shark""", """white wolf"""]
      __UpperCamelCase      =	pipe.get_label_ids(lowerCamelCase_				)

      __UpperCamelCase      =	pipe(lowerCamelCase_     ,					generator=lowerCamelCase_     ,					num_inference_steps=40     ,					output_type='np'				).images

      for word, image in zip(lowerCamelCase_     ,					lowerCamelCase_				):
       __UpperCamelCase      =	load_numpy(
           F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'				)
       assert np.abs((expected_image - image).max()				) < 1E-2







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'				)
      __UpperCamelCase      =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
      pipe.to('cuda'				)

      __UpperCamelCase      =	["""vase""", """umbrella"""]
      __UpperCamelCase      =	pipe.get_label_ids(lowerCamelCase_				)

      __UpperCamelCase      =	torch.manual_seed(0				)
      __UpperCamelCase      =	pipe(lowerCamelCase_     ,					generator=lowerCamelCase_     ,					num_inference_steps=25     ,					output_type='np'				).images

      for word, image in zip(lowerCamelCase_     ,					lowerCamelCase_				):
       __UpperCamelCase      =	load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           F'/dit/{word}_512.npy'				)

       assert np.abs((expected_image - image).max()				) < 1E-1





"""simple docstring"""

import functools
import operator

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


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			{
    "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		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	feat_extract_norm
      __UpperCamelCase      =	feat_extract_activation
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __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(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	xvector_output_dim







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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



