




import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase					,	unittest.TestCase):
      UpperCAmelCase__ :      str       						=  UNetaDModel
      UpperCAmelCase__ :      str       						=  'sample'
      @property
      def      lowerCAmelCase__		(							self:					Any  ):
             __lowerCamelCase  =		4
             __lowerCamelCase  =		14
             __lowerCamelCase  =		16

             __lowerCamelCase  =		floats_tensor((batch_size, num_features, seq_len)  ).to(UpperCamelCase_  )
             __lowerCamelCase  =		torch.tensor([10] * batch_size  ).to(UpperCamelCase_  )

             return {"sample": noise, "timestep": time_step}
      @property
      def      lowerCAmelCase__		(							self:					Tuple  ):
             return (4, 14, 16)
      @property
      def      lowerCAmelCase__		(							self:					Dict  ):
             return (4, 14, 16)
      def      lowerCAmelCase__		(							self:					List[str]  ):
             pass
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             pass
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					int  ):
             super().test_determinism()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             super().test_outputs_equivalence()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					int  ):
             super().test_from_save_pretrained()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             super().test_from_save_pretrained_variant()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					int  ):
             super().test_model_from_pretrained()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					str  ):
             super().test_output()
      def      lowerCAmelCase__		(							self:					List[str]  ):
             __lowerCamelCase  =		{
                 """block_out_channels""": (32, 64, 1_28, 2_56),
                 """in_channels""": 14,
                 """out_channels""": 14,
                 """time_embedding_type""": """positional""",
                 """use_timestep_embedding""": True,
                 """flip_sin_to_cos""": False,
                 """freq_shift""": 1.0,
                 """out_block_type""": """OutConv1DBlock""",
                 """mid_block_type""": """MidResTemporalBlock1D""",
                 """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
                 """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
                 """act_fn""": """mish""",
             }
             __lowerCamelCase  =		self.dummy_input
             return init_dict, inputs_dict
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase,  __lowerCamelCase  =		UNetaDModel.from_pretrained(
                 """bglick13/hopper-medium-v2-value-function-hor32"""       ,			output_loading_info=UpperCamelCase_       ,			subfolder="""unet"""  )
             self.assertIsNotNone(UpperCamelCase_  )
             self.assertEqual(len(loading_info["""missing_keys"""]  )       ,			0  )

             model.to(UpperCamelCase_  )
             __lowerCamelCase  =		model(**self.dummy_input  )

             assert image is not None, "Make sure output is not None"
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					str  ):
             __lowerCamelCase  =		UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""       ,			subfolder="""unet"""  )
             torch.manual_seed(0  )
             if torch.cuda.is_available():
                    torch.cuda.manual_seed_all(0  )

             __lowerCamelCase  =		model.config.in_channels
             __lowerCamelCase  =		16
             __lowerCamelCase  =		torch.randn((1, seq_len, num_features)  ).permute(
                 0       ,			2       ,			1  )  # match original, we can update values and remove
             __lowerCamelCase  =		torch.full((num_features,)       ,			0  )

             with torch.no_grad():
                    __lowerCamelCase  =		model(UpperCamelCase_       ,			UpperCamelCase_  ).sample.permute(0       ,			2       ,			1  )

             __lowerCamelCase  =		output[0, -3:, -3:].flatten()
             # fmt: off
             __lowerCamelCase  =		torch.tensor([-2.13_7172, 1.142_6016, 0.368_8687, -0.76_6922, 0.730_3146, 0.1103_8864, -0.476_0633, 0.1327_0172, 0.0259_1348]  )
             # fmt: on
             self.assertTrue(torch.allclose(UpperCamelCase_       ,			UpperCamelCase_       ,			rtol=1E-3  )  )
      def      lowerCAmelCase__		(							self:					str  ):
             # Not implemented yet for this UNet
             pass




      @slow
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		"""harmonai/maestro-150k"""
             __lowerCamelCase  =		UNetaDModel.from_pretrained(UpperCamelCase_       ,			subfolder="""unet"""  )
             model.to(UpperCamelCase_  )

             __lowerCamelCase  =		6_55_36
             __lowerCamelCase  =		torch.sin(torch.arange(UpperCamelCase_  )[None, None, :].repeat(1       ,			2       ,			1  )  ).to(UpperCamelCase_  )
             __lowerCamelCase  =		torch.tensor([1]  ).to(UpperCamelCase_  )

             with torch.no_grad():
                    __lowerCamelCase  =		model(UpperCamelCase_       ,			UpperCamelCase_  ).sample

             __lowerCamelCase  =		output.abs().sum()
             __lowerCamelCase  =		output.abs().max()

             assert (output_sum - 224.0896).abs() < 0.5
             assert (output_max - 0.0607).abs() < 4E-4





class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase					,	unittest.TestCase):
      UpperCAmelCase__ :      str       						=  UNetaDModel
      UpperCAmelCase__ :      int       						=  'sample'
      @property
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		4
             __lowerCamelCase  =		14
             __lowerCamelCase  =		16

             __lowerCamelCase  =		floats_tensor((batch_size, num_features, seq_len)  ).to(UpperCamelCase_  )
             __lowerCamelCase  =		torch.tensor([10] * batch_size  ).to(UpperCamelCase_  )

             return {"sample": noise, "timestep": time_step}
      @property
      def      lowerCAmelCase__		(							self:					Dict  ):
             return (4, 14, 16)
      @property
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             return (4, 14, 1)
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             super().test_determinism()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             super().test_outputs_equivalence()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					str  ):
             super().test_from_save_pretrained()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             super().test_from_save_pretrained_variant()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					List[str]  ):
             super().test_model_from_pretrained()
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					List[str]  ):
             # UNetRL is a value-function is different output shape
             __lowerCamelCase,  __lowerCamelCase  =		self.prepare_init_args_and_inputs_for_common()
             __lowerCamelCase  =		self.model_class(**UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             model.eval()

             with torch.no_grad():
                    __lowerCamelCase  =		model(**UpperCamelCase_  )

                    if isinstance(UpperCamelCase_       ,			UpperCamelCase_  ):
                           __lowerCamelCase  =		output.sample

             self.assertIsNotNone(UpperCamelCase_  )
             __lowerCamelCase  =		torch.Size((inputs_dict["""sample"""].shape[0], 1)  )
             self.assertEqual(output.shape       ,			UpperCamelCase_       ,			"""Input and output shapes do not match"""  )
      def      lowerCAmelCase__		(							self:					int  ):
             pass
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             pass
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		{
                 """in_channels""": 14,
                 """out_channels""": 14,
                 """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
                 """up_block_types""": [],
                 """out_block_type""": """ValueFunction""",
                 """mid_block_type""": """ValueFunctionMidBlock1D""",
                 """block_out_channels""": [32, 64, 1_28, 2_56],
                 """layers_per_block""": 1,
                 """downsample_each_block""": True,
                 """use_timestep_embedding""": True,
                 """freq_shift""": 1.0,
                 """flip_sin_to_cos""": False,
                 """time_embedding_type""": """positional""",
                 """act_fn""": """mish""",
             }
             __lowerCamelCase  =		self.dummy_input
             return init_dict, inputs_dict
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             __lowerCamelCase,  __lowerCamelCase  =		UNetaDModel.from_pretrained(
                 """bglick13/hopper-medium-v2-value-function-hor32"""       ,			output_loading_info=UpperCamelCase_       ,			subfolder="""value_function"""  )
             self.assertIsNotNone(UpperCamelCase_  )
             self.assertEqual(len(vf_loading_info["""missing_keys"""]  )       ,			0  )

             value_function.to(UpperCamelCase_  )
             __lowerCamelCase  =		value_function(**self.dummy_input  )

             assert image is not None, "Make sure output is not None"
      @unittest.skipIf(torch_device == """mps"""       ,			"""mish op not supported in MPS"""  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase,  __lowerCamelCase  =		UNetaDModel.from_pretrained(
                 """bglick13/hopper-medium-v2-value-function-hor32"""       ,			output_loading_info=UpperCamelCase_       ,			subfolder="""value_function"""  )
             torch.manual_seed(0  )
             if torch.cuda.is_available():
                    torch.cuda.manual_seed_all(0  )

             __lowerCamelCase  =		value_function.config.in_channels
             __lowerCamelCase  =		14
             __lowerCamelCase  =		torch.randn((1, seq_len, num_features)  ).permute(
                 0       ,			2       ,			1  )  # match original, we can update values and remove
             __lowerCamelCase  =		torch.full((num_features,)       ,			0  )

             with torch.no_grad():
                    __lowerCamelCase  =		value_function(UpperCamelCase_       ,			UpperCamelCase_  ).sample

             # fmt: off
             __lowerCamelCase  =		torch.tensor([165.25] * seq_len  )
             # fmt: on
             self.assertTrue(torch.allclose(UpperCamelCase_       ,			UpperCamelCase_       ,			rtol=1E-3  )  )




      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             # Not implemented yet for this UNet
             pass







import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_   							=      logging.get_logger(__name__)




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



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

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




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

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

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

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      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.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

