
"""simple docstring"""






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 							snake_case       (     A__							,       A__							,       unittest.TestCase		):
   a_					:		List[str]     					=     UNetaDModel
   a_					:		Union[str, Any]     					=     """sample"""







   @property
   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     4
      a_						   =     14
      a_						   =     16

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

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







   @property
   def    UpperCAmelCase__	( self)   ->Optional[int]:
      return (4, 14, 16)







   @property
   def    UpperCAmelCase__	( self)   ->List[Any]:
      return (4, 14, 16)







   def    UpperCAmelCase__	( self)   ->Dict:
      pass







   def    UpperCAmelCase__	( self)   ->str:
      pass







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->Optional[int]:
      super().test_determinism()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->str:
      super().test_outputs_equivalence()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->str:
      super().test_from_save_pretrained()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->Tuple:
      super().test_from_save_pretrained_variant()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->int:
      super().test_model_from_pretrained()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->Dict:
      super().test_output()







   def    UpperCAmelCase__	( self)   ->Any:
      a_						   =     {
          "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",
      }
      a_						   =     self.dummy_input
      return init_dict, inputs_dict







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->int:
      a_   ,			a_						   =     UNetaDModel.from_pretrained(
          "bglick13/hopper-medium-v2-value-function-hor32"       ,							output_loading_info=_lowerCAmelCase       ,							subfolder="unet")
      self.assertIsNotNone(_lowerCAmelCase)
      self.assertEqual(len(loading_info["missing_keys"])       ,							0)

      model.to(_lowerCAmelCase)
      a_						   =     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    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     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)

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

      with torch.no_grad():
         a_						   =     model(_lowerCAmelCase       ,							_lowerCAmelCase).sample.permute(0       ,							2       ,							1)

      a_						   =     output[0, -3:, -3:].flatten()
      # fmt: off
      a_						   =     torch.tensor([-2.137_172, 1.1_426_016, 0.3_688_687, -0.766_922, 0.7_303_146, 0.11_038_864, -0.4_760_633, 0.13_270_172, 0.02_591_348])
      # fmt: on
      self.assertTrue(torch.allclose(_lowerCAmelCase       ,							_lowerCAmelCase       ,							rtol=1E-3))







   def    UpperCAmelCase__	( self)   ->List[str]:
      # Not implemented yet for this UNet
      pass







   @slow
   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     "harmonai/maestro-150k"
      a_						   =     UNetaDModel.from_pretrained(_lowerCAmelCase       ,							subfolder="unet")
      model.to(_lowerCAmelCase)

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

      with torch.no_grad():
         a_						   =     model(_lowerCAmelCase       ,							_lowerCAmelCase).sample

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

      assert (output_sum - 224.0_896).abs() < 0.5
      assert (output_max - 0.0_607).abs() < 4E-4






class 							snake_case       (     A__							,       A__							,       unittest.TestCase		):
   a_					:		List[Any]     					=     UNetaDModel
   a_					:		Optional[int]     					=     """sample"""







   @property
   def    UpperCAmelCase__	( self)   ->str:
      a_						   =     4
      a_						   =     14
      a_						   =     16

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

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







   @property
   def    UpperCAmelCase__	( self)   ->Dict:
      return (4, 14, 16)







   @property
   def    UpperCAmelCase__	( self)   ->List[Any]:
      return (4, 14, 1)







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->str:
      super().test_determinism()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->List[str]:
      super().test_outputs_equivalence()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->List[str]:
      super().test_from_save_pretrained()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->Any:
      super().test_from_save_pretrained_variant()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->List[Any]:
      super().test_model_from_pretrained()







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->str:
      # UNetRL is a value-function is different output shape
      a_   ,			a_						   =     self.prepare_init_args_and_inputs_for_common()
      a_						   =     self.model_class(**_lowerCAmelCase)
      model.to(_lowerCAmelCase)
      model.eval()

      with torch.no_grad():
         a_						   =     model(**_lowerCAmelCase)

         if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase):
            a_						   =     output.sample

      self.assertIsNotNone(_lowerCAmelCase)
      a_						   =     torch.Size((inputs_dict["sample"].shape[0], 1))
      self.assertEqual(output.shape       ,							_lowerCAmelCase       ,							"Input and output shapes do not match")







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      pass







   def    UpperCAmelCase__	( self)   ->int:
      pass







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     {
          "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",
      }
      a_						   =     self.dummy_input
      return init_dict, inputs_dict







   @unittest.skipIf(torch_device == "mps"       ,							"mish op not supported in MPS")
   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_   ,			a_						   =     UNetaDModel.from_pretrained(
          "bglick13/hopper-medium-v2-value-function-hor32"       ,							output_loading_info=_lowerCAmelCase       ,							subfolder="value_function")
      self.assertIsNotNone(_lowerCAmelCase)
      self.assertEqual(len(vf_loading_info["missing_keys"])       ,							0)

      value_function.to(_lowerCAmelCase)
      a_						   =     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    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_   ,			a_						   =     UNetaDModel.from_pretrained(
          "bglick13/hopper-medium-v2-value-function-hor32"       ,							output_loading_info=_lowerCAmelCase       ,							subfolder="value_function")
      torch.manual_seed(0)
      if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0)

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

      with torch.no_grad():
         a_						   =     value_function(_lowerCAmelCase       ,							_lowerCAmelCase).sample

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







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Not implemented yet for this UNet
      pass






'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							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.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

