"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase_     =	logging.getLogger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							return (preds == labels).mean()




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,       )




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
				a			=						field(metadata={"help": "Should contain the data files for the task."} )
				a			=						field(
				    default=1_28   ,       metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Overwrite the cached training and evaluation sets"} )



def      lowerCamelCase_()->    int:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir  )
							    and os.listdir(training_args.output_dir  )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    """ --overwrite_output_dir to overcome."""  )

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     ,	datefmt="""%m/%d/%Y %H:%M:%S"""     ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,	)
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""     ,	training_args.local_rank     ,	training_args.device     ,	training_args.n_gpu     ,	bool(training_args.local_rank != -1  )     ,	training_args.fpaa     ,	)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank  ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""     ,	__SCREAMING_SNAKE_CASE  )

							# Set seed
							set_seed(training_args.seed  )

							try:
														_SCREAMING_SNAKE_CASE		:						Any			  =    processors[data_args.task_name]()
														_SCREAMING_SNAKE_CASE		:						int			  =    processor.get_labels()
														_SCREAMING_SNAKE_CASE		:						str			  =    len(__SCREAMING_SNAKE_CASE  )
							except KeyError:
														raise ValueError("""Task not found: %s""" % (data_args.task_name)  )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,	num_labels=__SCREAMING_SNAKE_CASE     ,	finetuning_task=data_args.task_name     ,	cache_dir=model_args.cache_dir     ,	)
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,	cache_dir=model_args.cache_dir     ,	)
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path     ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )     ,	config=__SCREAMING_SNAKE_CASE     ,	cache_dir=model_args.cache_dir     ,	)

							# Get datasets
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	task=data_args.task_name     ,	max_seq_length=data_args.max_seq_length     ,	overwrite_cache=data_args.overwrite_cache     ,	mode=Split.train     ,	)
							    if training_args.do_train
							    else None
							)
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	task=data_args.task_name     ,	max_seq_length=data_args.max_seq_length     ,	overwrite_cache=data_args.overwrite_cache     ,	mode=Split.dev     ,	)
							    if training_args.do_eval
							    else None
							)

							def compute_metrics(__SCREAMING_SNAKE_CASE  ) -> Dict:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.argmax(p.predictions     ,	axis=1  )
														return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE     ,	p.label_ids  )}

							# Data collator
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataCollatorWithPadding(__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=8  ) if training_args.fpaa else None

							# Initialize our Trainer
							_SCREAMING_SNAKE_CASE		:						Dict			  =    Trainer(
							    model=__SCREAMING_SNAKE_CASE     ,	args=__SCREAMING_SNAKE_CASE     ,	train_dataset=__SCREAMING_SNAKE_CASE     ,	eval_dataset=__SCREAMING_SNAKE_CASE     ,	compute_metrics=__SCREAMING_SNAKE_CASE     ,	data_collator=__SCREAMING_SNAKE_CASE     ,	)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
							_SCREAMING_SNAKE_CASE		:						str			  =    {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    trainer.evaluate()

														_SCREAMING_SNAKE_CASE		:						Dict			  =    os.path.join(training_args.output_dir     ,	"""eval_results.txt"""  )
														if trainer.is_world_master():
																					with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as writer:
																												logger.info("""***** Eval results *****"""  )
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			writer.write("""%s = %s\n""" % (key, value)  )

																												results.update(__SCREAMING_SNAKE_CASE  )

							return results



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
			main()



"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase_     =	{
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

lowerCAmelCase_     =	AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    str:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    create_model(
							    """HTSAT-tiny"""     ,	"""roberta"""     ,	__SCREAMING_SNAKE_CASE     ,	precision="""fp32"""     ,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""     ,	enable_fusion=__SCREAMING_SNAKE_CASE     ,	fusion_type="""aff_2d""" if enable_fusion else None     ,	)
							return model, model_cfg



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R""".*sequential.(\d+).*"""
							_SCREAMING_SNAKE_CASE		:						Any			  =    R""".*_projection.(\d+).*"""

							for key, value in state_dict.items():
														# check if any key needs to be modified
														for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																					if key_to_modify in key:
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    key.replace(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														if re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					# replace sequential layers with list
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.replace(F"""sequential.{sequential_layer}."""     ,	F"""layers.{int(__SCREAMING_SNAKE_CASE  )//3}.linear."""  )
														elif re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    int(re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )  )

																					# Because in CLAP they use `nn.Sequential`...
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    1 if projecton_layer == 0 else 2

																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    key.replace(F"""_projection.{projecton_layer}."""     ,	F"""_projection.linear{transformers_projection_layer}."""  )

														if "audio" and "qkv" in key:
																					# split qkv into query key and value
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mixed_qkv.size(0  ) // 3

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    mixed_qkv[:qkv_dim]
																					_SCREAMING_SNAKE_CASE		:						str			  =    mixed_qkv[qkv_dim : qkv_dim * 2]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    mixed_qkv[qkv_dim * 2 :]

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    query_layer
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_layer
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value_layer
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value

							return model_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    init_clap(__SCREAMING_SNAKE_CASE     ,	enable_fusion=__SCREAMING_SNAKE_CASE  )

							clap_model.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    clap_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    rename_state_dict(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    ClapConfig()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    enable_fusion
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ClapModel(__SCREAMING_SNAKE_CASE  )

							# ignore the spectrogram embedding layer
							model.load_state_dict(__SCREAMING_SNAKE_CASE     ,	strict=__SCREAMING_SNAKE_CASE  )

							model.save_pretrained(__SCREAMING_SNAKE_CASE  )
							transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
			lowerCAmelCase_     =	parser.parse_args()

			convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)


