






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case_   :	str							      =logging.get_logger(__name__)

snake_case_   :	Tuple							      ={
    '''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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		for attribute in key.split("."	):
				__A    =							getattr(lowerCAmelCase__					,     lowerCAmelCase__	)

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

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							[]
		__A    =							fairseq_model.state_dict()

		__A    =							hf_model.feature_extractor

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

								if key in name:
										__A    =							True
										if "*" in mapped_key:
												__A    =							name.split(lowerCAmelCase__	)[0].split("."	)[-2]
												__A    =							mapped_key.replace("*"					,     lowerCAmelCase__	)
										if "weight_g" in name:
												__A    =							"weight_g"
										elif "weight_v" in name:
												__A    =							"weight_v"
										elif "weight" in name:
												__A    =							"weight"
										elif "bias" in name:
												__A    =							"bias"
										else:
												__A    =							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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							HubertConfig()
		__A    =							model.config

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

		return config



@torch.no_grad()
def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=None	):


		'''simple docstring'''

		__A    =							distilhubert().model.model

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

		__A    =							WavaVecaFeatureExtractor(
		    feature_size=1					,     sampling_rate=1_6000					,     padding_value=0					,     do_normalize=lowerCAmelCase__					,     return_attention_mask=lowerCAmelCase__					,     )
		__A    =							HubertModel(lowerCAmelCase__	)

		recursively_load_weights(lowerCAmelCase__					,     lowerCAmelCase__	)

		feature_extractor.save_pretrained(lowerCAmelCase__	)
		hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
					snake_case_   :	str							      =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''')
					snake_case_   :	str							      =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_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	int							      =[
					    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPTJForCausalLM''',
					    '''GPTJForQuestionAnswering''',
					    '''GPTJForSequenceClassification''',
					    '''GPTJModel''',
					    '''GPTJPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''TFGPTJForCausalLM''',
					    '''TFGPTJForQuestionAnswering''',
					    '''TFGPTJForSequenceClassification''',
					    '''TFGPTJModel''',
					    '''TFGPTJPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''FlaxGPTJForCausalLM''',
					    '''FlaxGPTJModel''',
					    '''FlaxGPTJPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

