
'''simple docstring'''



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__A          =    {
    "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  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	):
						for attribute in key.split("""."""	):
												lowercase__							 =       getattr(lowercase__		,		lowercase__	)

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

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





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       []
						lowercase__							 =       fairseq_model.state_dict()

						lowercase__							 =       hf_model.feature_extractor

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

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

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





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





def  _A		(							lowercase__	):
						lowercase__							 =       HubertConfig()
						lowercase__							 =       model.config

						lowercase__							 =       fs_config.activation_dropout
						lowercase__							 =       False
						lowercase__							 =       fs_config.attention_dropout
						lowercase__							 =       False
						lowercase__							 =       eval(fs_config.extractor_conv_feature_layers	)
						lowercase__							 =       [x[0] for x in conv_layers]
						lowercase__							 =       [x[1] for x in conv_layers]
						lowercase__							 =       [x[2] for x in conv_layers]
						lowercase__							 =       """gelu"""
						lowercase__							 =       """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
						lowercase__							 =       False
						lowercase__							 =       0.0
						lowercase__							 =       0.0
						lowercase__							 =       fs_config.activation_fn
						lowercase__							 =       fs_config.dropout
						lowercase__							 =       fs_config.encoder_embed_dim
						lowercase__							 =       0.0_2
						lowercase__							 =       fs_config.encoder_ffn_embed_dim
						lowercase__							 =       1e-5
						lowercase__							 =       0.0
						lowercase__							 =       fs_config.encoder_attention_heads
						lowercase__							 =       fs_config.conv_pos_groups
						lowercase__							 =       fs_config.conv_pos
						lowercase__							 =       len(lowercase__	)
						lowercase__							 =       fs_config.encoder_layers

						return config





@torch.no_grad()
def  _A		(							lowercase__		,		lowercase__=None	):
						lowercase__							 =       distilhubert().model.model

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

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

						recursively_load_weights(lowercase__		,		lowercase__	)

						feature_extractor.save_pretrained(lowercase__	)
						hf_model.save_pretrained(lowercase__	)


if __name__ == "__main__":
				__A          =    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")
				__A          =    parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








'''simple docstring'''



import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
				import torch

				from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
				import pyanvml.pyanvml as nvml


__A          =    logging.get_logger(__name__)





class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							PyTorchBenchmarkArguments
			lowerCamelCase							:							PretrainedConfig
			lowerCamelCase							:							str       		=  "PyTorch"



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




									'''simple docstring'''



									return torch.__version__



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									if self.args.torchscript:
															lowercase__							 =       True

									lowercase__							 =       (
									    hasattr(lowerCamelCase__       ,     """architectures"""       )
									    and isinstance(config.architectures       ,     lowerCamelCase__       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					lowercase__							 =       config.architectures[0]
																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
																					lowercase__							 =       model_cls(lowerCamelCase__       )
															except ImportError:
																					raise ImportError(
																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
									else:
															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )

									model.eval()
									model.to(self.args.device       )

									# encoder-decoder has vocab size saved differently
									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""       )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""       )
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
									else:
															lowercase__							 =       model

									def encoder_decoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       )
															return outputs

									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									lowercase__							 =       (
									    hasattr(lowerCamelCase__       ,     """architectures"""       )
									    and isinstance(config.architectures       ,     lowerCamelCase__       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					lowercase__							 =       config.architectures[0]
																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
																					lowercase__							 =       model_cls(lowerCamelCase__       )
															except ImportError:
																					raise ImportError(
																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
									else:
															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )

									if self.args.torchscript:
															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
									else:
															lowercase__							 =       model

									model.train()
									model.to(self.args.device       )

									# encoder-decoder has vocab size saved differently
									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""       )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""       )

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
															loss.backward()
															return loss

									lowercase__							 =       (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train



			def A__ (					self       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
																					timeit.repeat(
																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															lowercase__							 =       timeit.repeat(
															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report()       )

															return min(lowerCamelCase__       ) / 10.0
									except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
															return "N/A"



			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:




									'''simple docstring'''



									try:
															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       start_memory_tracing("""transformers"""       )

															if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																					    """ `--no-memory` or `args.memory=False`"""       )
															elif self.args.is_gpu:
																					if not is_pyanvml_available():
																											logger.warning(
																											    """py3nvml not installed, we won't log GPU memory usage. """
																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
																											lowercase__							 =       """N/A"""
																					else:
																											logger.info(
																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																											    """ on the same GPU."""       )
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
																											lowercase__							 =       meminfo.used
																											lowercase__							 =       Memory(lowerCamelCase__       )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
															else:
																					lowercase__							 =       None

															return memory, summary
									except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
															return "N/A", None






