
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowercase__    :					List[str]					=						{
    "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__   (		_A					,     _A					,     _A					,     _A					,     _A							):







	'''simple docstring'''





	for attribute in key.split("."							):
		snake_case_		   =					getattr(_lowerCamelCase					,     _lowerCamelCase							)

	if weight_type is not None:
		snake_case_		   =					getattr(_lowerCamelCase					,     _lowerCamelCase							).shape
	else:
		snake_case_		   =					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":
		snake_case_		   =					value
	elif weight_type == "weight_g":
		snake_case_		   =					value
	elif weight_type == "weight_v":
		snake_case_		   =					value
	elif weight_type == "bias":
		snake_case_		   =					value
	else:
		snake_case_		   =					value

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


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					[]
	snake_case_		   =					fairseq_model.state_dict()

	snake_case_		   =					hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case_		   =					False
		if "conv_layers" in name:
			load_conv_layer(
			    _lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase					,     hf_model.config.feat_extract_norm == "group"					,     )
			snake_case_		   =					True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case_		   =					mapped_key

				if key in name:
					snake_case_		   =					True
					if "*" in mapped_key:
						snake_case_		   =					name.split(_lowerCamelCase							)[0].split("."							)[-2]
						snake_case_		   =					mapped_key.replace("*"					,     _lowerCamelCase							)
					if "weight_g" in name:
						snake_case_		   =					"""weight_g"""
					elif "weight_v" in name:
						snake_case_		   =					"""weight_v"""
					elif "weight" in name:
						snake_case_		   =					"""weight"""
					elif "bias" in name:
						snake_case_		   =					"""bias"""
					else:
						snake_case_		   =					None
					set_recursively(_lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase							)
				continue
		if not is_used:
			unused_weights.append(_lowerCamelCase							)

	logger.warning(f"Unused weights: {unused_weights}"							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					full_name.split("conv_layers."							)[-1]
	snake_case_		   =					name.split("."							)
	snake_case_		   =					int(items[0]							)
	snake_case_		   =					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."
			)
			snake_case_		   =					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."
			)
			snake_case_		   =					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."
			)
			snake_case_		   =					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."
			)
			snake_case_		   =					value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."							)
	else:
		unused_weights.append(_lowerCamelCase							)


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					HubertConfig()
	snake_case_		   =					model.config

	snake_case_		   =					fs_config.activation_dropout
	snake_case_		   =					False
	snake_case_		   =					fs_config.attention_dropout
	snake_case_		   =					False
	snake_case_		   =					eval(fs_config.extractor_conv_feature_layers							)
	snake_case_		   =					[x[0] for x in conv_layers]
	snake_case_		   =					[x[1] for x in conv_layers]
	snake_case_		   =					[x[2] for x in conv_layers]
	snake_case_		   =					"""gelu"""
	snake_case_		   =					"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	snake_case_		   =					False
	snake_case_		   =					0.0
	snake_case_		   =					0.0
	snake_case_		   =					fs_config.activation_fn
	snake_case_		   =					fs_config.dropout
	snake_case_		   =					fs_config.encoder_embed_dim
	snake_case_		   =					0.02
	snake_case_		   =					fs_config.encoder_ffn_embed_dim
	snake_case_		   =					1E-5
	snake_case_		   =					0.0
	snake_case_		   =					fs_config.encoder_attention_heads
	snake_case_		   =					fs_config.conv_pos_groups
	snake_case_		   =					fs_config.conv_pos
	snake_case_		   =					len(_lowerCamelCase							)
	snake_case_		   =					fs_config.encoder_layers

	return config


@torch.no_grad()
def 						lowerCamelCase__   (		_A					,     _A=None							):







	'''simple docstring'''





	snake_case_		   =					distilhubert().model.model

	if config_path is not None:
		snake_case_		   =					HubertConfig.from_pretrained(_lowerCamelCase							)
	else:
		snake_case_		   =					convert_config(_lowerCamelCase							)
	snake_case_		   =					model.eval()

	snake_case_		   =					WavaVecaFeatureExtractor(
	    feature_size=1					,     sampling_rate=16000					,     padding_value=0					,     do_normalize=_lowerCamelCase					,     return_attention_mask=_lowerCamelCase					,     )
	snake_case_		   =					HubertModel(_lowerCamelCase							)

	recursively_load_weights(_lowerCamelCase					,     _lowerCamelCase							)

	feature_extractor.save_pretrained(_lowerCamelCase							)
	hf_model.save_pretrained(_lowerCamelCase							)


if __name__ == "__main__":
		lowercase__    :					Union[str, Any]					=						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")
		lowercase__    :					str					=						parser.parse_args()
		convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=__lowercase						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
