import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    BertConfig.from_pretrained(
						    "bert-large-cased"					,			vocab_size=__lowercase					,			max_position_embeddings=512					,			is_decoder=__lowercase					,			add_cross_attention=__lowercase					,			)
						_snake_case						:     List[Any]				=    bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						_snake_case						:     Tuple				=    BertGenerationConfig(**__lowercase						)
						if is_encoder:
												_snake_case						:     str				=    BertGenerationEncoder(__lowercase						)
						else:
												_snake_case						:     Optional[int]				=    BertGenerationDecoder(__lowercase						)
						print(F"""Building PyTorch model from configuration: {config}"""						)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase					,			__lowercase					,			model_class="bert"					,			is_encoder_named_decoder=__lowercase					,			is_encoder=__lowercase					,			)

						# Save pytorch-model
						print(F"""Save PyTorch model and config to {pytorch_dump_path}"""						)
						model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    List[str]     = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--is_encoder_named_decoder',
				    action='store_true',
				    help='If decoder has to be renamed to encoder in PyTorch model.',
				)
				parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
				parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
				__SCREAMING_SNAKE_CASE						:    Dict     = parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_hub_path,
				    args.pytorch_dump_path,
				    args.is_encoder_named_decoder,
				    args.vocab_size,
				    is_encoder=args.is_encoder,
				)
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)