


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 			lowerCamelCase_					(    unittest.TestCase    ):
							_lowerCamelCase				: Union[str, Any]       =					(ScoreSdeVeScheduler,)
							_lowerCamelCase				: Optional[int]       =					()





							@property
							def   __magic_name__      (							self    ):
								a_     			=   4
								a_     			=   3
								a_     			=   8
								a_     			=   8

								a_     			=   torch.rand((batch_size, num_channels, height, width)    )

								return sample





							@property
							def   __magic_name__      (							self    ):
								a_     			=   4
								a_     			=   3
								a_     			=   8
								a_     			=   8

								a_     			=   batch_size * num_channels * height * width
								a_     			=   torch.arange(__snake_case    )
								a_     			=   sample.reshape(__snake_case			,       __snake_case			,       __snake_case			,       __snake_case    )
								a_     			=   sample / num_elems
								a_     			=   sample.permute(3			,       0			,       1			,       2    )

								return sample





							def   __magic_name__      (							self    ):
								def model(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       *_SCREAMING_SNAKE_CASE    ):
									return sample * t / (t + 1)

								return model





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   {
								    'num_train_timesteps': 2000,
								    'snr': 0.1_5,
								    'sigma_min': 0.0_1,
								    'sigma_max': 1348,
								    'sampling_eps': 1E-5,
								}

								config.update(**__snake_case    )
								return config





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.dummy_sample
									a_     			=   0.1 * sample

									a_     			=   self.get_scheduler_config(**__snake_case    )
									a_     			=   scheduler_class(**__snake_case    )

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__snake_case    )
										a_     			=   scheduler_class.from_pretrained(__snake_case    )

									a_     			=   scheduler.step_pred(
									    __snake_case			,       __snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample
									a_     			=   new_scheduler.step_pred(
									    __snake_case			,       __snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample

									assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

									a_     			=   scheduler.step_correct(__snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample
									a_     			=   new_scheduler.step_correct(
									    __snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample

									assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler correction are not identical"





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   dict(self.forward_default_kwargs    )
								kwargs.update(__snake_case    )

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.dummy_sample
									a_     			=   0.1 * sample

									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**__snake_case    )

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__snake_case    )
										a_     			=   scheduler_class.from_pretrained(__snake_case    )

									a_     			=   scheduler.step_pred(
									    __snake_case			,       __snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample
									a_     			=   new_scheduler.step_pred(
									    __snake_case			,       __snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample

									assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

									a_     			=   scheduler.step_correct(__snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample
									a_     			=   new_scheduler.step_correct(
									    __snake_case			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample

									assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler correction are not identical"





							def   __magic_name__      (							self    ):
								for timesteps in [10, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__snake_case    )





							def   __magic_name__      (							self    ):
								for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]			,       [1, 100, 1000]    ):
									self.check_over_configs(sigma_min=__snake_case			,       sigma_max=__snake_case    )





							def   __magic_name__      (							self    ):
								for t in [0.1, 0.5, 0.7_5]:
									self.check_over_forward(time_step=__snake_case    )





							def   __magic_name__      (							self    ):
								a_     			=   dict(self.forward_default_kwargs    )

								a_     			=   self.scheduler_classes[0]
								a_     			=   self.get_scheduler_config()
								a_     			=   scheduler_class(**__snake_case    )

								a_     			=   3

								a_     			=   self.dummy_model()
								a_     			=   self.dummy_sample_deter

								scheduler.set_sigmas(__snake_case    )
								scheduler.set_timesteps(__snake_case    )
								a_     			=   torch.manual_seed(0    )

								for i, t in enumerate(scheduler.timesteps    ):
									a_     			=   scheduler.sigmas[i]

									for _ in range(scheduler.config.correct_steps    ):
										with torch.no_grad():
											a_     			=   model(__snake_case			,       __snake_case    )
										a_     			=   scheduler.step_correct(__snake_case			,       __snake_case			,       generator=__snake_case			,       **__snake_case    ).prev_sample

									with torch.no_grad():
										a_     			=   model(__snake_case			,       __snake_case    )

									a_     			=   scheduler.step_pred(__snake_case			,       __snake_case			,       __snake_case			,       generator=__snake_case			,       **__snake_case    )
									a_     			=   output.prev_sample, output.prev_sample_mean

								a_     			=   torch.sum(torch.abs(__snake_case    )    )
								a_     			=   torch.mean(torch.abs(__snake_case    )    )

								assert np.isclose(result_sum.item()			,       143_7275_8528.0    )
								assert np.isclose(result_mean.item()			,       1871_4530.0    )





							def   __magic_name__      (							self    ):
								a_     			=   dict(self.forward_default_kwargs    )

								a_     			=   kwargs.pop("""num_inference_steps"""			,       __snake_case    )

								for scheduler_class in self.scheduler_classes:
									a_     			=   self.get_scheduler_config()
									a_     			=   scheduler_class(**__snake_case    )

									a_     			=   self.dummy_sample
									a_     			=   0.1 * sample

									if num_inference_steps is not None and hasattr(__snake_case			,       """set_timesteps"""    ):
										scheduler.set_timesteps(__snake_case    )
									elif num_inference_steps is not None and not hasattr(__snake_case			,       """set_timesteps"""    ):
										a_     			=   num_inference_steps

									a_     			=   scheduler.step_pred(__snake_case			,       0			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample
									a_     			=   scheduler.step_pred(__snake_case			,       1			,       __snake_case			,       generator=torch.manual_seed(0    )			,       **__snake_case    ).prev_sample

									self.assertEqual(output_a.shape			,       sample.shape    )
									self.assertEqual(output_a.shape			,       output_a.shape    )


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase__             =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	for attribute in key.split('.'						):
		_UpperCamelCase :		Any								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						)

	if weight_type is not None:
		_UpperCamelCase :		List[str]								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						).shape
	else:
		_UpperCamelCase :		Optional[int]								=    hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
	    F''' {value.shape} for {full_name}'''
	)

	if weight_type == "weight":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "weight_g":
		_UpperCamelCase :		Optional[Any]								=    value
	elif weight_type == "weight_v":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "bias":
		_UpperCamelCase :		Any								=    value
	else:
		_UpperCamelCase :		Tuple								=    value

	logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    []
	_UpperCamelCase :		List[Any]								=    fairseq_model.state_dict()

	_UpperCamelCase :		int								=    hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCamelCase :		Dict								=    False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							hf_model.config.feat_extract_norm == 'group'				,							)
			_UpperCamelCase :		Union[str, Any]								=    True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCamelCase :		Tuple								=    mapped_key

				if key in name:
					_UpperCamelCase :		List[Any]								=    True
					if "*" in mapped_key:
						_UpperCamelCase :		List[Any]								=    name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						_UpperCamelCase :		Optional[Any]								=    mapped_key.replace('*'				,							UpperCAmelCase_						)
					if "weight_g" in name:
						_UpperCamelCase :		List[Any]								=    'weight_g'
					elif "weight_v" in name:
						_UpperCamelCase :		Tuple								=    'weight_v'
					elif "weight" in name:
						_UpperCamelCase :		Optional[Any]								=    'weight'
					elif "bias" in name:
						_UpperCamelCase :		List[Any]								=    'bias'
					else:
						_UpperCamelCase :		str								=    None
					set_recursively(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

	logger.warning(F'''Unused weights: {unused_weights}'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    full_name.split('conv_layers.'						)[-1]
	_UpperCamelCase :		str								=    name.split('.'						)
	_UpperCamelCase :		List[Any]								=    int(items[0]						)
	_UpperCamelCase :		Tuple								=    int(items[1]						)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
			)
			_UpperCamelCase :		Any								=    value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
			)
			_UpperCamelCase :		List[str]								=    value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
			    " found."
			)
			_UpperCamelCase :		str								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
			)
			_UpperCamelCase :		Dict								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
	else:
		unused_weights.append(UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    HubertConfig()
	_UpperCamelCase :		List[str]								=    model.config

	_UpperCamelCase :		Any								=    fs_config.activation_dropout
	_UpperCamelCase :		Tuple								=    False
	_UpperCamelCase :		Tuple								=    fs_config.attention_dropout
	_UpperCamelCase :		Dict								=    False
	_UpperCamelCase :		Optional[Any]								=    eval(fs_config.extractor_conv_feature_layers						)
	_UpperCamelCase :		Dict								=    [x[0] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[1] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[2] for x in conv_layers]
	_UpperCamelCase :		Dict								=    'gelu'
	_UpperCamelCase :		List[Any]								=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_UpperCamelCase :		List[Any]								=    False
	_UpperCamelCase :		List[Any]								=    0.0
	_UpperCamelCase :		Optional[int]								=    0.0
	_UpperCamelCase :		Optional[int]								=    fs_config.activation_fn
	_UpperCamelCase :		Optional[int]								=    fs_config.dropout
	_UpperCamelCase :		Union[str, Any]								=    fs_config.encoder_embed_dim
	_UpperCamelCase :		Tuple								=    0.0_2
	_UpperCamelCase :		int								=    fs_config.encoder_ffn_embed_dim
	_UpperCamelCase :		int								=    1e-5
	_UpperCamelCase :		Optional[Any]								=    0.0
	_UpperCamelCase :		List[str]								=    fs_config.encoder_attention_heads
	_UpperCamelCase :		List[str]								=    fs_config.conv_pos_groups
	_UpperCamelCase :		Union[str, Any]								=    fs_config.conv_pos
	_UpperCamelCase :		int								=    len(UpperCAmelCase_						)
	_UpperCamelCase :		Tuple								=    fs_config.encoder_layers

	return config






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict=None						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    distilhubert().model.model

	if config_path is not None:
		_UpperCamelCase :		Optional[Any]								=    HubertConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[int]								=    convert_config(UpperCAmelCase_						)
	_UpperCamelCase :		List[Any]								=    model.eval()

	_UpperCamelCase :		List[Any]								=    WavaVecaFeatureExtractor(
	    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=UpperCAmelCase_				,							return_attention_mask=UpperCAmelCase_				,							)
	_UpperCamelCase :		Optional[Any]								=    HubertModel(UpperCAmelCase_						)

	recursively_load_weights(UpperCAmelCase_				,							UpperCAmelCase_						)

	feature_extractor.save_pretrained(UpperCAmelCase_						)
	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




