
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        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  Union[str, Any]   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


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

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

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






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]   ,							__lowerCAmelCase  :  Any    ):




			"""simple docstring"""


			lowerCAmelCase_      =     []
			lowerCAmelCase_      =     fairseq_model.state_dict()

			lowerCAmelCase_      =     hf_model.feature_extractor

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

												if key in name:
															lowerCAmelCase_      =     True
															if "*" in mapped_key:
																		lowerCAmelCase_      =     name.split(_lowerCamelCase    )[0].split("."    )[-2]
																		lowerCAmelCase_      =     mapped_key.replace("*"   ,							_lowerCamelCase    )
															if "weight_g" in name:
																		lowerCAmelCase_      =     """weight_g"""
															elif "weight_v" in name:
																		lowerCAmelCase_      =     """weight_v"""
															elif "weight" in name:
																		lowerCAmelCase_      =     """weight"""
															elif "bias" in name:
																		lowerCAmelCase_      =     """bias"""
															else:
																		lowerCAmelCase_      =     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__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  List[str]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			lowerCAmelCase_      =     full_name.split("conv_layers."    )[-1]
			lowerCAmelCase_      =     name.split("."    )
			lowerCAmelCase_      =     int(items[0]    )
			lowerCAmelCase_      =     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."""
									)
									lowerCAmelCase_      =     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."""
									)
									lowerCAmelCase_      =     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."
									)
									lowerCAmelCase_      =     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."""
									)
									lowerCAmelCase_      =     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__    (    __lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     HubertConfig()
			lowerCAmelCase_      =     model.config

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

			return config






@torch.no_grad()
def        lowerCamelCase__    (    __lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  int=None    ):




			"""simple docstring"""


			lowerCAmelCase_      =     distilhubert().model.model

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

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

			recursively_load_weights(_lowerCamelCase   ,							_lowerCamelCase    )

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


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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_A       =					{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					["XLMProphetNetTokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLMProphetNetDecoder",
			    "XLMProphetNetEncoder",
			    "XLMProphetNetForCausalLM",
			    "XLMProphetNetForConditionalGeneration",
			    "XLMProphetNetModel",
			    "XLMProphetNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_prophetnet import (
						    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMProphetNetDecoder,
						    XLMProphetNetEncoder,
						    XLMProphetNetForCausalLM,
						    XLMProphetNetForConditionalGeneration,
						    XLMProphetNetModel,
						    XLMProphetNetPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



