




import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = True   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    {}
    for name in model.attn_processors.keys():
        __UpperCamelCase			=    name.endswith('''attn2.processor'''   ) and not (
            name.startswith('''transformer_in'''   ) or '''temp_attentions''' in name.split('''.'''   )
        )
        __UpperCamelCase			=    model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith('''mid_block'''   ):
            __UpperCamelCase			=    model.config.block_out_channels[-1]
        elif name.startswith('''up_blocks'''   ):
            __UpperCamelCase			=    int(name[len('''up_blocks.'''   )]   )
            __UpperCamelCase			=    list(reversed(model.config.block_out_channels   )   )[block_id]
        elif name.startswith('''down_blocks'''   ):
            __UpperCamelCase			=    int(name[len('''down_blocks.'''   )]   )
            __UpperCamelCase			=    model.config.block_out_channels[block_id]
        elif name.startswith('''transformer_in'''   ):
            # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
            __UpperCamelCase			=    8 * model.config.attention_head_dim

        __UpperCamelCase			=    LoRAAttnProcessor(hidden_size=lowercase_       ,  cross_attention_dim=lowercase_   )
        __UpperCamelCase			=    lora_attn_procs[name].to(model.device   )

        if mock_weights:
            # add 1 to weights to mock trained weights
            with torch.no_grad():
                lora_attn_procs[name].to_q_lora.up.weight += 1
                lora_attn_procs[name].to_k_lora.up.weight += 1
                lora_attn_procs[name].to_v_lora.up.weight += 1
                lora_attn_procs[name].to_out_lora.up.weight += 1

    return lora_attn_procs


@skip_mps
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			Dict     =       UNetaDConditionModel
      lowerCAmelCase__      :			Any     =       "sample"







      @property
      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    4
          __UpperCamelCase			=    4
          __UpperCamelCase			=    4
          __UpperCamelCase			=    (32, 32)

          __UpperCamelCase			=    floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(snake_case					)
          __UpperCamelCase			=    torch.tensor([10]					).to(snake_case					)
          __UpperCamelCase			=    floats_tensor((batch_size, 4, 32)					).to(snake_case					)

          return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}







      @property
      def    snake_case						(  self       :			Tuple					):
          return (4, 4, 32, 32)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return (4, 4, 32, 32)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    {
              '''block_out_channels''': (32, 64),
              '''down_block_types''': (
                  '''CrossAttnDownBlock3D''',
                  '''DownBlock3D''',
              ),
              '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
              '''cross_attention_dim''': 32,
              '''attention_head_dim''': 8,
              '''out_channels''': 4,
              '''in_channels''': 4,
              '''layers_per_block''': 1,
              '''sample_size''': 32,
          }
          __UpperCamelCase			=    self.dummy_input
          return init_dict, inputs_dict







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

          model.enable_xformers_memory_efficient_attention()

          assert (
              model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
              == "XFormersAttnProcessor"
          ), "xformers is not enabled"







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    32

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)

              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    output.sample

          self.assertIsNotNone(snake_case					)
          __UpperCamelCase			=    inputs_dict['''sample'''].shape
          self.assertEqual(output.shape			,							snake_case			,							'''Input and output shapes do not match'''					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              # Warmup pass when using mps (see #372)
              if torch_device == "mps" and isinstance(snake_case			,							snake_case					):
                  model(**self.dummy_input					)

              __UpperCamelCase			=    model(**snake_case					)
              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    first.sample

              __UpperCamelCase			=    model(**snake_case					)
              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    second.sample

          __UpperCamelCase			=    first.cpu().numpy()
          __UpperCamelCase			=    second.cpu().numpy()
          __UpperCamelCase			=    out_a[~np.isnan(snake_case					)]
          __UpperCamelCase			=    out_a[~np.isnan(snake_case					)]
          __UpperCamelCase			=    np.amax(np.abs(out_a - out_a					)					)
          self.assertLessEqual(snake_case			,							1E-5					)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          model.set_attention_slice('''auto'''					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None

          model.set_attention_slice('''max'''					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None

          model.set_attention_slice(2					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)

          # make sure we can set a list of attention processors
          model.set_attn_processor(snake_case					)
          model.to(snake_case					)

          # test that attn processors can be set to itself
          model.set_attn_processor(model.attn_processors					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.0}					).sample
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          assert (samplea - samplea).abs().max() < 3E-3
          assert (samplea - samplea).abs().max() < 3E-3

          # sample 2 and sample 3 should be different
          assert (samplea - samplea).abs().max() > 3E-3







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.bin'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              new_model.load_attn_procs(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    new_model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          assert (sample - new_sample).abs().max() < 1E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case			,							safe_serialization=snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.safetensors'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              new_model.load_attn_procs(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    new_model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

          assert (sample - new_sample).abs().max() < 3E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







      def    snake_case						(  self       :			Optional[int]					):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          __UpperCamelCase			=    create_lora_layers(snake_case			,							mock_weights=snake_case					)
          model.set_attn_processor(snake_case					)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.bin'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              new_model.load_attn_procs(snake_case			,							weight_name='''pytorch_lora_weights.bin'''					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          __UpperCamelCase			=    create_lora_layers(snake_case			,							mock_weights=snake_case					)
          model.set_attn_processor(snake_case					)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case					)
              self.assertTrue(os.path.isfile(os.path.join(snake_case			,							'''pytorch_lora_weights.bin'''					)					)					)
              torch.manual_seed(0					)
              __UpperCamelCase			=    self.model_class(**snake_case					)
              new_model.to(snake_case					)
              with self.assertRaises(snake_case					) as e:
                  new_model.load_attn_procs(snake_case			,							use_safetensors=snake_case					)
              self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''			,							str(e.exception					)					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.0}					).sample

          model.set_attn_processor(AttnProcessor()					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

          assert (sample - new_sample).abs().max() < 1E-4
          assert (sample - old_sample).abs().max() < 3E-3







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()			,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,							)
      def    snake_case						(  self       :			Union[str, Any]					):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    4

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          # default
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

              model.enable_xformers_memory_efficient_attention()
              __UpperCamelCase			=    model(**snake_case					).sample

              model.disable_xformers_memory_efficient_attention()
              __UpperCamelCase			=    model(**snake_case					).sample

          assert (sample - on_sample).abs().max() < 1E-4
          assert (sample - off_sample).abs().max() < 1E-4







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    32

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)[0]

          self.assertEqual(output.shape			,							output_a.shape			,							'''Shape doesn\'t match'''					)
          assert np.abs(output.cpu() - output_a.cpu()					).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.










'''simple docstring'''







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["connector.weight"]
      UpperCamelCase__      =							downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
            UpperCamelCase__      =							downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCamelCase__      =							downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

      UpperCamelCase__      =							hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"       ):
            UpperCamelCase__      =							convert_classification(__A ,     __A ,     __A       )
      elif arch.endswith("ForAudioFrameClassification"       ):
            UpperCamelCase__      =							convert_diarization(__A ,     __A ,     __A       )
      elif arch.endswith("ForXVector"       ):
            UpperCamelCase__      =							convert_xvector(__A ,     __A ,     __A       )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''       )

      if hf_config.use_weighted_layer_sum:
            UpperCamelCase__      =							checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, Any]								=			argparse.ArgumentParser()
  parser.add_argument(
      '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
  )
  parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
  parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
  a__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





