
import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__magic_name__  :  Union[str, Any]     			=   logging.get_logger(__name__)





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      List[Any]				,					snake_case__   :      List[Any] )->			List[Any]:
    A_								=					WavLMForSequenceClassification.from_pretrained(snake_case__				,					config=snake_case__ )
    A_								=					downstream_dict["projector.weight"]
    A_								=					downstream_dict["projector.bias"]
    A_								=					downstream_dict["model.post_net.linear.weight"]
    A_								=					downstream_dict["model.post_net.linear.bias"]
    return model





def        lowerCAmelCase       (				snake_case__   :      Tuple				,					snake_case__   :      int				,					snake_case__   :      List[Any] )->			Any:
    A_								=					WavLMForAudioFrameClassification.from_pretrained(snake_case__				,					config=snake_case__ )
    A_								=					downstream_dict["model.linear.weight"]
    A_								=					downstream_dict["model.linear.bias"]
    return model





def        lowerCAmelCase       (				snake_case__   :      int				,					snake_case__   :      Optional[int]				,					snake_case__   :      Union[str, Any] )->			Optional[int]:
    A_								=					WavLMForXVector.from_pretrained(snake_case__				,					config=snake_case__ )
    A_								=					downstream_dict["connector.weight"]
    A_								=					downstream_dict["connector.bias"]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
        A_								=					downstream_dict[
            f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
        ]
        A_								=					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

    A_								=					downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
    A_								=					downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
    A_								=					downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
    A_								=					downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
    A_								=					downstream_dict["objective.W"]
    return model





@torch.no_grad()
def        lowerCAmelCase       (				snake_case__   :      List[Any]				,					snake_case__   :      Union[str, Any]				,					snake_case__   :      int				,					snake_case__   :      int )->			List[str]:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )

    A_								=					checkpoint["Downstream"]

    A_								=					WavLMConfig.from_pretrained(snake_case__ )
    A_								=					WavaVecaFeatureExtractor.from_pretrained(
        snake_case__				,					return_attention_mask=snake_case__				,					do_normalize=snake_case__ )

    A_								=					hf_config.architectures[0]
    if arch.endswith("ForSequenceClassification" ):
        A_								=					convert_classification(snake_case__				,					snake_case__				,					snake_case__ )
    elif arch.endswith("ForAudioFrameClassification" ):
        A_								=					convert_diarization(snake_case__				,					snake_case__				,					snake_case__ )
    elif arch.endswith("ForXVector" ):
        A_								=					convert_xvector(snake_case__				,					snake_case__				,					snake_case__ )
    else:
        raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )

    if hf_config.use_weighted_layer_sum:
        A_								=					checkpoint["Featurizer"]["weights"]

    hf_feature_extractor.save_pretrained(snake_case__ )
    hf_model.save_pretrained(snake_case__ )


if __name__ == "__main__":
     __magic_name__  :  str     			=   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.')
     __magic_name__  :  Any     			=   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





def        lowerCAmelCase       (				)->			int:
    A_								=					argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path"				,					default=snake_case__				,					type=snake_case__				,					required=snake_case__				,					)
    parser.add_argument("--prompt"				,					type=snake_case__				,					default="" )
    parser.add_argument("--length"				,					type=snake_case__				,					default=20 )
    parser.add_argument("--stop_token"				,					type=snake_case__				,					default=snake_case__				,					help="Token at which text generation is stopped" )
    parser.add_argument(
        "--temperature"				,					type=snake_case__				,					default=1.0				,					help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,					)
    parser.add_argument(
        "--repetition_penalty"				,					type=snake_case__				,					default=1.0				,					help="primarily useful for CTRL model; in that case, use 1.2" )
    parser.add_argument("--k"				,					type=snake_case__				,					default=0 )
    parser.add_argument("--penalty_alpha"				,					type=snake_case__				,					default=0.0 )
    parser.add_argument("--p"				,					type=snake_case__				,					default=0.9 )

    parser.add_argument("--prefix"				,					type=snake_case__				,					default=""				,					help="Text added prior to input." )
    parser.add_argument("--padding_text"				,					type=snake_case__				,					default=""				,					help="Deprecated, the use of `--prefix` is preferred." )
    parser.add_argument("--xlm_language"				,					type=snake_case__				,					default=""				,					help="Optional language when used with the XLM model." )

    parser.add_argument("--seed"				,					type=snake_case__				,					default=42				,					help="random seed for initialization" )
    parser.add_argument("--no_cuda"				,					action="store_true"				,					help="Avoid using CUDA when available" )
    parser.add_argument(
        "--fp16"				,					action="store_true"				,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,					)
    A_								=					parser.parse_args()

    A_								=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
    A_								=					0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}' )

    set_seed(snake_case__ )

    # Initialize the model and tokenizer
    A_								=					AutoTokenizer.from_pretrained(args.model_name_or_path )
    A_								=					AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device )

    if args.fpaa:
        model.half()

    logger.info(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

    A_								=					tokenizer(snake_case__				,					return_tensors="pt"				,					add_special_tokens=snake_case__ )
    A_								=					{key: value.to(args.device ) for key, value in inputs.items()}

    A_								=					model.generate(
        **snake_case__				,					max_length=args.length + len(inputs["input_ids"][0] )				,					penalty_alpha=args.penalty_alpha				,					top_k=args.k				,					)

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

        # Decode text
        A_								=					tokenizer.decode(snake_case__				,					clean_up_tokenization_spaces=snake_case__				,					add_special_tokens=snake_case__ )

        # Remove all text after the stop token
        A_								=					text[: text.find(args.stop_token ) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        A_								=					(
            prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,					clean_up_tokenization_spaces=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






