





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()

UpperCAmelCase						:       Dict        =					logging.get_logger(__name__)







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = True  )				->				int:
					__A     :						List[str]          =    {}
					for name in model.attn_processors.keys():
										__A     :						Any          =    name.endswith('attn2.processor'  ) and not (
										    name.startswith('transformer_in'  ) or 'temp_attentions' in name.split('.'  )
										)
										__A     :						str          =    model.config.cross_attention_dim if has_cross_attention else None
										if name.startswith('mid_block'  ):
															__A     :						Optional[int]          =    model.config.block_out_channels[-1]
										elif name.startswith('up_blocks'  ):
															__A     :						Optional[int]          =    int(name[len('up_blocks.'  )]  )
															__A     :						Tuple          =    list(reversed(model.config.block_out_channels  )  )[block_id]
										elif name.startswith('down_blocks'  ):
															__A     :						Optional[int]          =    int(name[len('down_blocks.'  )]  )
															__A     :						int          =    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
															__A     :						Any          =    8 * model.config.attention_head_dim

										__A     :						Dict          =    LoRAAttnProcessor(hidden_size=a ,						cross_attention_dim=a  )
										__A     :						Union[str, Any]          =    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 						_A( snake_case__    ,			snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Dict    =     UNetaDConditionModel
				UpperCamelCase   :  Tuple    =     '''sample'''



				@property
				def  UpperCAmelCase_		(      self					):
									__A     :						str          =    4
									__A     :						Tuple          =    4
									__A     :						Any          =    4
									__A     :						Dict          =    (32, 32)

									__A     :						Tuple          =    floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(_A					)
									__A     :						List[Any]          =    torch.tensor([10]					).to(_A					)
									__A     :						str          =    floats_tensor((batch_size, 4, 32)					).to(_A					)

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



				@property
				def  UpperCAmelCase_		(      self					):
									return (4, 4, 32, 32)



				@property
				def  UpperCAmelCase_		(      self					):
									return (4, 4, 32, 32)



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    {
									    '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,
									}
									__A     :						Tuple          =    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  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						List[str]          =    self.prepare_init_args_and_inputs_for_common()
									__A     :						str          =    self.model_class(**_A					)

									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  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Union[str, Any]          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						Tuple          =    32

									__A     :						Any          =    self.model_class(**_A					)
									model.to(_A					)
									model.eval()

									with torch.no_grad():
														__A     :						List[str]          =    model(**_A					)

														if isinstance(_A					, _A					):
																			__A     :						Dict          =    output.sample

									self.assertIsNotNone(_A					)
									__A     :						Dict          =    inputs_dict['sample'].shape
									self.assertEqual(output.shape					, _A					, 'Input and output shapes do not match'					)



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Optional[Any]          =    self.prepare_init_args_and_inputs_for_common()
									__A     :						Optional[Any]          =    self.model_class(**_A					)
									model.to(_A					)
									model.eval()

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

														__A     :						Optional[Any]          =    model(**_A					)
														if isinstance(_A					, _A					):
																			__A     :						Any          =    first.sample

														__A     :						int          =    model(**_A					)
														if isinstance(_A					, _A					):
																			__A     :						str          =    second.sample

									__A     :						Optional[int]          =    first.cpu().numpy()
									__A     :						Tuple          =    second.cpu().numpy()
									__A     :						str          =    out_a[~np.isnan(_A					)]
									__A     :						str          =    out_a[~np.isnan(_A					)]
									__A     :						Dict          =    np.amax(np.abs(out_a - out_a					)					)
									self.assertLessEqual(_A					, 1e-5					)



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						List[str]          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						Tuple          =    8

									__A     :						Optional[int]          =    self.model_class(**_A					)
									model.to(_A					)
									model.eval()

									model.set_attention_slice('auto'					)
									with torch.no_grad():
														__A     :						Any          =    model(**_A					)
									assert output is not None

									model.set_attention_slice('max'					)
									with torch.no_grad():
														__A     :						Optional[Any]          =    model(**_A					)
									assert output is not None

									model.set_attention_slice(2					)
									with torch.no_grad():
														__A     :						Optional[Any]          =    model(**_A					)
									assert output is not None



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Any          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						str          =    8

									__A     :						Union[str, Any]          =    self.model_class(**_A					)
									model.to(_A					)

									with torch.no_grad():
														__A     :						Optional[int]          =    model(**_A					).sample

									__A     :						Optional[Any]          =    create_lora_layers(_A					)

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

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

									with torch.no_grad():
														__A     :						str          =    model(**_A					, cross_attention_kwargs={'scale': 0.0}					).sample
														__A     :						Any          =    model(**_A					, cross_attention_kwargs={'scale': 0.5}					).sample
														__A     :						Any          =    model(**_A					, 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  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Union[str, Any]          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						Any          =    8

									torch.manual_seed(0					)
									__A     :						Optional[Any]          =    self.model_class(**_A					)
									model.to(_A					)

									with torch.no_grad():
														__A     :						str          =    model(**_A					).sample

									__A     :						Optional[int]          =    create_lora_layers(_A					)
									model.set_attn_processor(_A					)

									with torch.no_grad():
														__A     :						List[str]          =    model(**_A					, cross_attention_kwargs={'scale': 0.5}					).sample

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

									with torch.no_grad():
														__A     :						List[str]          =    new_model(**_A					, 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  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						str          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						str          =    8

									torch.manual_seed(0					)
									__A     :						List[Any]          =    self.model_class(**_A					)
									model.to(_A					)

									with torch.no_grad():
														__A     :						Optional[Any]          =    model(**_A					).sample

									__A     :						Any          =    create_lora_layers(_A					)
									model.set_attn_processor(_A					)

									with torch.no_grad():
														__A     :						List[Any]          =    model(**_A					, cross_attention_kwargs={'scale': 0.5}					).sample

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

									with torch.no_grad():
														__A     :						Tuple          =    new_model(**_A					, 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  UpperCAmelCase_		(      self					):
									# enable deterministic behavior for gradient checkpointing
									__A   ,    __A     :						Union[str, Any]          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						int          =    8

									torch.manual_seed(0					)
									__A     :						int          =    self.model_class(**_A					)
									model.to(_A					)

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



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Dict          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						List[str]          =    8

									torch.manual_seed(0					)
									__A     :						Union[str, Any]          =    self.model_class(**_A					)
									model.to(_A					)

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



				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						Optional[int]          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						List[str]          =    8

									torch.manual_seed(0					)
									__A     :						int          =    self.model_class(**_A					)
									model.to(_A					)

									with torch.no_grad():
														__A     :						Optional[int]          =    model(**_A					).sample

									__A     :						List[Any]          =    create_lora_layers(_A					)
									model.set_attn_processor(_A					)

									with torch.no_grad():
														__A     :						Any          =    model(**_A					, cross_attention_kwargs={'scale': 0.0}					).sample

									model.set_attn_processor(AttnProcessor()					)

									with torch.no_grad():
														__A     :						Dict          =    model(**_A					).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  UpperCAmelCase_		(      self					):
									# enable deterministic behavior for gradient checkpointing
									__A   ,    __A     :						Union[str, Any]          =    self.prepare_init_args_and_inputs_for_common()

									__A     :						Dict          =    4

									torch.manual_seed(0					)
									__A     :						Union[str, Any]          =    self.model_class(**_A					)
									model.to(_A					)
									__A     :						Union[str, Any]          =    create_lora_layers(_A					)
									model.set_attn_processor(_A					)

									# default
									with torch.no_grad():
														__A     :						Optional[int]          =    model(**_A					).sample

														model.enable_xformers_memory_efficient_attention()
														__A     :						Tuple          =    model(**_A					).sample

														model.disable_xformers_memory_efficient_attention()
														__A     :						Tuple          =    model(**_A					).sample

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





				def  UpperCAmelCase_		(      self					):
									__A   ,    __A     :						int          =    self.prepare_init_args_and_inputs_for_common()
									__A     :						Optional[int]          =    32

									__A     :						Tuple          =    self.model_class(**_A					)
									model.to(_A					)
									model.eval()

									with torch.no_grad():
														__A     :						str          =    model(**_A					)[0]

									model.enable_forward_chunking()
									with torch.no_grad():
														__A     :						Tuple          =    model(**_A					)[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.











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase						:       Tuple        =					logging.get_logger(__name__)

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







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						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     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

					__A     :						Dict          =    downstream_dict['projector.weight']
					__A     :						int          =    downstream_dict['projector.bias']
					__A     :						List[Any]          =    downstream_dict['model.post_net.linear.weight']
					__A     :						int          =    downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


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




