"""simple docstring"""




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



def 				__a       (			_lowercase							, _lowercase = True						):







					"""simple docstring"""





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

										lowerCamelCase__		:   Union[str, Any]  				=					LoRAAttnProcessor(hidden_size=_lowercase							, cross_attention_dim=_lowercase						)
										lowerCamelCase__		:   Tuple  				=					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        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      UNetaDConditionModel
				UpperCAmelCase	=      '''sample'''







				@property
				def   lowercase_		(    self    :Optional[Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					4
									lowerCamelCase__		:   Optional[int]  				=					4
									lowerCamelCase__		:   Tuple  				=					4
									lowerCamelCase__		:   Union[str, Any]  				=					(32, 32)

									lowerCamelCase__		:   List[str]  				=					floats_tensor((batch_size, num_channels, num_frames) + sizes  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					torch.tensor([10]  ).to(__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					floats_tensor((batch_size, 4, 32)  ).to(__UpperCAmelCase  )

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







				@property
				def   lowercase_		(    self    :Optional[Any]  )      ->					List[Any]:




									"""simple docstring"""



									return (4, 4, 32, 32)







				@property
				def   lowercase_		(    self    :Tuple  )      ->					Union[str, Any]:




									"""simple docstring"""



									return (4, 4, 32, 32)







				def   lowercase_		(    self    :List[Any]  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   Tuple  				=					{
									    '''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,
									}
									lowerCamelCase__		:   Dict  				=					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   lowercase_		(    self    :List[str]  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					self.prepare_init_args_and_inputs_for_common()
									lowerCamelCase__		:   List[str]  				=					self.model_class(**__UpperCAmelCase  )

									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   lowercase_		(    self    :Union[str, Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   str  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   Optional[int]  				=					32

									lowerCamelCase__		:   Optional[int]  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  )

														if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
																			lowerCamelCase__		:   Any  				=					output.sample

									self.assertIsNotNone(__UpperCAmelCase  )
									lowerCamelCase__		:   Tuple  				=					inputs_dict['''sample'''].shape
									self.assertEqual(output.shape      ,__UpperCAmelCase      ,'''Input and output shapes do not match'''  )







				def   lowercase_		(    self    :Optional[Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   List[Any]  				=					self.prepare_init_args_and_inputs_for_common()
									lowerCamelCase__		:   Tuple  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()

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

														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  )
														if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
																			lowerCamelCase__		:   Union[str, Any]  				=					first.sample

														lowerCamelCase__		:   str  				=					model(**__UpperCAmelCase  )
														if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
																			lowerCamelCase__		:   Union[str, Any]  				=					second.sample

									lowerCamelCase__		:   Dict  				=					first.cpu().numpy()
									lowerCamelCase__		:   List[Any]  				=					second.cpu().numpy()
									lowerCamelCase__		:   Optional[int]  				=					out_a[~np.isnan(__UpperCAmelCase  )]
									lowerCamelCase__		:   List[str]  				=					out_a[~np.isnan(__UpperCAmelCase  )]
									lowerCamelCase__		:   str  				=					np.amax(np.abs(out_a - out_a  )  )
									self.assertLessEqual(__UpperCAmelCase      ,1E-5  )







				def   lowercase_		(    self    :int  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   int  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   int  				=					8

									lowerCamelCase__		:   List[Any]  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()

									model.set_attention_slice('''auto'''  )
									with torch.no_grad():
														lowerCamelCase__		:   int  				=					model(**__UpperCAmelCase  )
									assert output is not None

									model.set_attention_slice('''max'''  )
									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  )
									assert output is not None

									model.set_attention_slice(2  )
									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  )
									assert output is not None







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




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   str  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   Any  				=					8

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

									with torch.no_grad():
														lowerCamelCase__		:   Optional[Any]  				=					model(**__UpperCAmelCase  ).sample

									lowerCamelCase__		:   Union[str, Any]  				=					create_lora_layers(__UpperCAmelCase  )

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

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

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase      ,cross_attention_kwargs={'''scale''': 0.0}  ).sample
														lowerCamelCase__		:   List[Any]  				=					model(**__UpperCAmelCase      ,cross_attention_kwargs={'''scale''': 0.5}  ).sample
														lowerCamelCase__		:   List[Any]  				=					model(**__UpperCAmelCase      ,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   lowercase_		(    self    :Tuple  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   str  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   int  				=					8

									torch.manual_seed(0  )
									lowerCamelCase__		:   Optional[int]  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  ).sample

									lowerCamelCase__		:   List[Any]  				=					create_lora_layers(__UpperCAmelCase  )
									model.set_attn_processor(__UpperCAmelCase  )

									with torch.no_grad():
														lowerCamelCase__		:   str  				=					model(**__UpperCAmelCase      ,cross_attention_kwargs={'''scale''': 0.5}  ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_attn_procs(__UpperCAmelCase  )
														self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase      ,'''pytorch_lora_weights.bin'''  )  )  )
														torch.manual_seed(0  )
														lowerCamelCase__		:   Union[str, Any]  				=					self.model_class(**__UpperCAmelCase  )
														new_model.to(__UpperCAmelCase  )
														new_model.load_attn_procs(__UpperCAmelCase  )

									with torch.no_grad():
														lowerCamelCase__		:   str  				=					new_model(**__UpperCAmelCase      ,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   lowercase_		(    self    :Any  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   str  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   Any  				=					8

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

									with torch.no_grad():
														lowerCamelCase__		:   Dict  				=					model(**__UpperCAmelCase  ).sample

									lowerCamelCase__		:   str  				=					create_lora_layers(__UpperCAmelCase  )
									model.set_attn_processor(__UpperCAmelCase  )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_attn_procs(__UpperCAmelCase      ,safe_serialization=__UpperCAmelCase  )
														self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase      ,'''pytorch_lora_weights.safetensors'''  )  )  )
														torch.manual_seed(0  )
														lowerCamelCase__		:   Union[str, Any]  				=					self.model_class(**__UpperCAmelCase  )
														new_model.to(__UpperCAmelCase  )
														new_model.load_attn_procs(__UpperCAmelCase  )

									with torch.no_grad():
														lowerCamelCase__		:   Union[str, Any]  				=					new_model(**__UpperCAmelCase      ,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   lowercase_		(    self    :Tuple  )      ->					int:




									"""simple docstring"""



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

									lowerCamelCase__		:   int  				=					8

									torch.manual_seed(0  )
									lowerCamelCase__		:   int  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )

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







				def   lowercase_		(    self    :Optional[Any]  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   List[str]  				=					8

									torch.manual_seed(0  )
									lowerCamelCase__		:   Optional[int]  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )

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







				def   lowercase_		(    self    :Dict  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   List[str]  				=					8

									torch.manual_seed(0  )
									lowerCamelCase__		:   Any  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )

									with torch.no_grad():
														lowerCamelCase__		:   List[str]  				=					model(**__UpperCAmelCase  ).sample

									lowerCamelCase__		:   List[str]  				=					create_lora_layers(__UpperCAmelCase  )
									model.set_attn_processor(__UpperCAmelCase  )

									with torch.no_grad():
														lowerCamelCase__		:   Optional[int]  				=					model(**__UpperCAmelCase      ,cross_attention_kwargs={'''scale''': 0.0}  ).sample

									model.set_attn_processor(AttnProcessor()  )

									with torch.no_grad():
														lowerCamelCase__		:   Dict  				=					model(**__UpperCAmelCase  ).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   lowercase_		(    self    :int  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   int  				=					self.prepare_init_args_and_inputs_for_common()

									lowerCamelCase__		:   List[Any]  				=					4

									torch.manual_seed(0  )
									lowerCamelCase__		:   Any  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									lowerCamelCase__		:   Any  				=					create_lora_layers(__UpperCAmelCase  )
									model.set_attn_processor(__UpperCAmelCase  )

									# default
									with torch.no_grad():
														lowerCamelCase__		:   List[str]  				=					model(**__UpperCAmelCase  ).sample

														model.enable_xformers_memory_efficient_attention()
														lowerCamelCase__		:   Optional[Any]  				=					model(**__UpperCAmelCase  ).sample

														model.disable_xformers_memory_efficient_attention()
														lowerCamelCase__		:   List[str]  				=					model(**__UpperCAmelCase  ).sample

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







				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__				,						lowerCamelCase__		:   int  				=					self.prepare_init_args_and_inputs_for_common()
									lowerCamelCase__		:   int  				=					32

									lowerCamelCase__		:   int  				=					self.model_class(**__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()

									with torch.no_grad():
														lowerCamelCase__		:   Any  				=					model(**__UpperCAmelCase  )[0]

									model.enable_forward_chunking()
									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(**__UpperCAmelCase  )[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 HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			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)






