





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase      :       List[str]									=  {
    """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""",
}

lowercase      :       Union[str, Any]									=  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")



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



def A_	(		A__				)  ->		str:
       a__						:	Dict =				{}

       a__						:	Optional[Any] =				R'.*sequential.(\d+).*'
       a__						:	Optional[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:
                            a__						:	str =				key.replace(A__		,      A__				)

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

                     a__						:	int =				key.replace(F'sequential.{sequential_layer}.'		,      F'layers.{int(A__				)//3}.linear.'				)
              elif re.match(A__		,      A__				):
                     a__						:	str =				int(re.match(A__		,      A__				).group(1				)				)

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

                     a__						:	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
                     a__						:	int =				value
                     a__						:	Tuple =				mixed_qkv.size(0				) // 3

                     a__						:	Union[str, Any] =				mixed_qkv[:qkv_dim]
                     a__						:	Union[str, Any] =				mixed_qkv[qkv_dim : qkv_dim * 2]
                     a__						:	List[Any] =				mixed_qkv[qkv_dim * 2 :]

                     a__						:	str =				query_layer
                     a__						:	Union[str, Any] =				key_layer
                     a__						:	int =				value_layer
              else:
                     a__						:	Tuple =				value

       return model_state_dict



def A_	(		A__		,      A__		,      A__		,      A__=False				)  ->		Dict:
       a__ ,							a__						:	Optional[int] =				init_clap(A__		,      enable_fusion=A__				)

       clap_model.eval()
       a__						:	Optional[int] =				clap_model.state_dict()
       a__						:	Tuple =				rename_state_dict(A__				)

       a__						:	Union[str, Any] =				ClapConfig()
       a__						:	str =				enable_fusion
       a__						:	Tuple =				ClapModel(A__				)

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

       model.save_pretrained(A__				)
       transformers_config.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       int									=  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""")
    lowercase      :       Any									=  parser.parse_args()

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




import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    re.compile(r"\b(a|an|the)\b", re.UNICODE)

SCREAMING_SNAKE_CASE__   :			int												=    None




def  __magic_name__ (   )			->   str:
				__lowerCamelCase    					=	argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''							)
				parser.add_argument('''data_file'''		,  metavar='''data.json'''		,  help='''Input data JSON file.'''							)
				parser.add_argument('''pred_file'''		,  metavar='''pred.json'''		,  help='''Model predictions.'''							)
				parser.add_argument(
				    '''--out-file'''		,  '''-o'''		,  metavar='''eval.json'''		,  help='''Write accuracy metrics to file (default is stdout).'''							)
				parser.add_argument(
				    '''--na-prob-file'''		,  '''-n'''		,  metavar='''na_prob.json'''		,  help='''Model estimates of probability of no answer.'''							)
				parser.add_argument(
				    '''--na-prob-thresh'''		,  '''-t'''		,  type=__lowerCAmelCase		,  default=1.0		,  help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''		,  )
				parser.add_argument(
				    '''--out-image-dir'''		,  '''-p'''		,  metavar='''out_images'''		,  default=__lowerCAmelCase		,  help='''Save precision-recall curves to directory.'''							)
				parser.add_argument('''--verbose'''		,  '''-v'''		,  action='''store_true'''							)
				if len(sys.argv							) == 1:
								parser.print_help()
								sys.exit(1							)
				return parser.parse_args()




def  __magic_name__ (   __lowerCAmelCase	:						List[str]							)			->   Union[str, Any]:
				__lowerCamelCase    					=	{}
				for article in dataset:
								for p in article["paragraphs"]:
												for qa in p["qas"]:
																__lowerCamelCase    					=	bool(qa['''answers''']['''text''']							)
				return qid_to_has_ans




def  __magic_name__ (   __lowerCAmelCase	:						Dict							)			->   Optional[Any]:

				def remove_articles(__lowerCAmelCase	:						Optional[int]							):
								return ARTICLES_REGEX.sub(''' '''		,  __lowerCAmelCase							)

				def white_space_fix(__lowerCAmelCase	:						Optional[int]							):
								return " ".join(text.split()							)

				def remove_punc(__lowerCAmelCase	:						Union[str, Any]							):
								__lowerCamelCase    					=	set(string.punctuation							)
								return "".join(ch for ch in text if ch not in exclude							)

				def lower(__lowerCAmelCase	:						Dict							):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase							)							)							)							)




def  __magic_name__ (   __lowerCAmelCase	:						List[Any]							)			->   Optional[int]:
				if not s:
								return []
				return normalize_answer(__lowerCAmelCase							).split()




def  __magic_name__ (   __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Tuple							)			->   int:
				return int(normalize_answer(__lowerCAmelCase							) == normalize_answer(__lowerCAmelCase							)							)




def  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Tuple							)			->   str:
				__lowerCamelCase    					=	get_tokens(__lowerCAmelCase							)
				__lowerCamelCase    					=	get_tokens(__lowerCAmelCase							)
				__lowerCamelCase    					=	collections.Counter(__lowerCAmelCase							) & collections.Counter(__lowerCAmelCase							)
				__lowerCamelCase    					=	sum(common.values()							)
				if len(__lowerCAmelCase							) == 0 or len(__lowerCAmelCase							) == 0:
								# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
								return int(gold_toks == pred_toks							)
				if num_same == 0:
								return 0
				__lowerCamelCase    					=	1.0 * num_same / len(__lowerCAmelCase							)
				__lowerCamelCase    					=	1.0 * num_same / len(__lowerCAmelCase							)
				__lowerCamelCase    					=	(2 * precision * recall) / (precision + recall)
				return fa




def  __magic_name__ (   __lowerCAmelCase	:						Dict		,  __lowerCAmelCase	:						List[str]							)			->   Optional[Any]:
				__lowerCamelCase    					=	{}
				__lowerCamelCase    					=	{}
				for article in dataset:
								for p in article["paragraphs"]:
												for qa in p["qas"]:
																__lowerCamelCase    					=	qa['''id''']
																__lowerCamelCase    					=	[t for t in qa['''answers''']['''text'''] if normalize_answer(__lowerCAmelCase							)]
																if not gold_answers:
																				# For unanswerable questions, only correct answer is empty string
																				__lowerCamelCase    					=	['''''']
																if qid not in preds:
																				print(f'''Missing prediction for {qid}'''							)
																				continue
																__lowerCamelCase    					=	preds[qid]
																# Take max over all gold answers
																__lowerCamelCase    					=	max(compute_exact(__lowerCAmelCase		,  __lowerCAmelCase							) for a in gold_answers							)
																__lowerCamelCase    					=	max(compute_fa(__lowerCAmelCase		,  __lowerCAmelCase							) for a in gold_answers							)
				return exact_scores, fa_scores




def  __magic_name__ (   __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						Union[str, Any]							)			->   List[str]:
				__lowerCamelCase    					=	{}
				for qid, s in scores.items():
								__lowerCamelCase    					=	na_probs[qid] > na_prob_thresh
								if pred_na:
												__lowerCamelCase    					=	float(not qid_to_has_ans[qid]							)
								else:
												__lowerCamelCase    					=	s
				return new_scores




def  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						Optional[Any]=None							)			->   Union[str, Any]:
				if not qid_list:
								__lowerCamelCase    					=	len(__lowerCAmelCase							)
								return collections.OrderedDict(
								    [
								        ('''exact''', 100.0 * sum(exact_scores.values()							) / total),
								        ('''f1''', 100.0 * sum(fa_scores.values()							) / total),
								        ('''total''', total),
								    ]							)
				else:
								__lowerCamelCase    					=	len(__lowerCAmelCase							)
								return collections.OrderedDict(
								    [
								        ('''exact''', 100.0 * sum(exact_scores[k] for k in qid_list							) / total),
								        ('''f1''', 100.0 * sum(fa_scores[k] for k in qid_list							) / total),
								        ('''total''', total),
								    ]							)




def  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Optional[Any]							)			->   int:
				for k in new_eval:
								__lowerCamelCase    					=	new_eval[k]




def  __magic_name__ (   __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Dict		,  __lowerCAmelCase	:						Union[str, Any]							)			->   Optional[Any]:
				plt.step(__lowerCAmelCase		,  __lowerCAmelCase		,  color='''b'''		,  alpha=0.2		,  where='''post'''							)
				plt.fill_between(__lowerCAmelCase		,  __lowerCAmelCase		,  step='''post'''		,  alpha=0.2		,  color='''b'''							)
				plt.xlabel('''Recall'''							)
				plt.ylabel('''Precision'''							)
				plt.xlim([0.0, 1.05]							)
				plt.ylim([0.0, 1.05]							)
				plt.title(__lowerCAmelCase							)
				plt.savefig(__lowerCAmelCase							)
				plt.clf()




def  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Optional[Any]=None		,  __lowerCAmelCase	:						Tuple=None							)			->   int:
				__lowerCamelCase    					=	sorted(__lowerCAmelCase		,  key=lambda __lowerCAmelCase							: na_probs[k]							)
				__lowerCamelCase    					=	0.0
				__lowerCamelCase    					=	1.0
				__lowerCamelCase    					=	0.0
				__lowerCamelCase    					=	[1.0]
				__lowerCamelCase    					=	[0.0]
				__lowerCamelCase    					=	0.0
				for i, qid in enumerate(__lowerCAmelCase							):
								if qid_to_has_ans[qid]:
												true_pos += scores[qid]
								__lowerCamelCase    					=	true_pos / float(i + 1							)
								__lowerCamelCase    					=	true_pos / float(__lowerCAmelCase							)
								if i == len(__lowerCAmelCase							) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
												# i.e., if we can put a threshold after this point
												avg_prec += cur_p * (cur_r - recalls[-1])
												precisions.append(__lowerCAmelCase							)
												recalls.append(__lowerCAmelCase							)
				if out_image:
								plot_pr_curve(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
				return {"ap": 100.0 * avg_prec}




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						List[Any]							)			->   List[Any]:
				if out_image_dir and not os.path.exists(__lowerCAmelCase							):
								os.makedirs(__lowerCAmelCase							)
				__lowerCamelCase    					=	sum(1 for v in qid_to_has_ans.values() if v							)
				if num_true_pos == 0:
								return
				__lowerCamelCase    					=	make_precision_recall_eval(
				    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  out_image=os.path.join(__lowerCAmelCase		,  '''pr_exact.png'''							)		,  title='''Precision-Recall curve for Exact Match score'''		,  )
				__lowerCamelCase    					=	make_precision_recall_eval(
				    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  out_image=os.path.join(__lowerCAmelCase		,  '''pr_f1.png'''							)		,  title='''Precision-Recall curve for F1 score'''		,  )
				__lowerCamelCase    					=	{k: float(__lowerCAmelCase							) for k, v in qid_to_has_ans.items()}
				__lowerCamelCase    					=	make_precision_recall_eval(
				    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  out_image=os.path.join(__lowerCAmelCase		,  '''pr_oracle.png'''							)		,  title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''		,  )
				merge_eval(__lowerCAmelCase		,  __lowerCAmelCase		,  '''pr_exact'''							)
				merge_eval(__lowerCAmelCase		,  __lowerCAmelCase		,  '''pr_f1'''							)
				merge_eval(__lowerCAmelCase		,  __lowerCAmelCase		,  '''pr_oracle'''							)




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						int							)			->   Optional[Any]:
				if not qid_list:
								return
				__lowerCamelCase    					=	[na_probs[k] for k in qid_list]
				__lowerCamelCase    					=	np.ones_like(__lowerCAmelCase							) / float(len(__lowerCAmelCase							)							)
				plt.hist(__lowerCAmelCase		,  weights=__lowerCAmelCase		,  bins=20		,  range=(0.0, 1.0)							)
				plt.xlabel('''Model probability of no-answer'''							)
				plt.ylabel('''Proportion of dataset'''							)
				plt.title(f'''Histogram of no-answer probability: {name}'''							)
				plt.savefig(os.path.join(__lowerCAmelCase		,  f'''na_prob_hist_{name}.png'''							)							)
				plt.clf()




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Union[str, Any]							)			->   Optional[int]:
				__lowerCamelCase    					=	sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]							)
				__lowerCamelCase    					=	num_no_ans
				__lowerCamelCase    					=	cur_score
				__lowerCamelCase    					=	0.0
				__lowerCamelCase    					=	sorted(__lowerCAmelCase		,  key=lambda __lowerCAmelCase							: na_probs[k]							)
				for i, qid in enumerate(__lowerCAmelCase							):
								if qid not in scores:
												continue
								if qid_to_has_ans[qid]:
												__lowerCamelCase    					=	scores[qid]
								else:
												if preds[qid]:
																__lowerCamelCase    					=	-1
												else:
																__lowerCamelCase    					=	0
								cur_score += diff
								if cur_score > best_score:
												__lowerCamelCase    					=	cur_score
												__lowerCamelCase    					=	na_probs[qid]
				return 100.0 * best_score / len(__lowerCAmelCase							), best_thresh




def  __magic_name__ (   __lowerCAmelCase	:						Dict		,  __lowerCAmelCase	:						Optional[int]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Union[str, Any]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Optional[Any]							)			->   int:
				__lowerCamelCase					,    __lowerCamelCase    					=	find_best_thresh(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
				__lowerCamelCase					,    __lowerCamelCase    					=	find_best_thresh(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
				__lowerCamelCase    					=	best_exact
				__lowerCamelCase    					=	exact_thresh
				__lowerCamelCase    					=	best_fa
				__lowerCamelCase    					=	fa_thresh




def  __magic_name__ (   )			->   Optional[int]:
				with open(OPTS.data_file							) as f:
								__lowerCamelCase    					=	json.load(__lowerCAmelCase							)
								__lowerCamelCase    					=	dataset_json['''data''']
				with open(OPTS.pred_file							) as f:
								__lowerCamelCase    					=	json.load(__lowerCAmelCase							)
				if OPTS.na_prob_file:
								with open(OPTS.na_prob_file							) as f:
												__lowerCamelCase    					=	json.load(__lowerCAmelCase							)
				else:
								__lowerCamelCase    					=	{k: 0.0 for k in preds}
				__lowerCamelCase    					=	make_qid_to_has_ans(__lowerCAmelCase							)  # maps qid to True/False
				__lowerCamelCase    					=	[k for k, v in qid_to_has_ans.items() if v]
				__lowerCamelCase    					=	[k for k, v in qid_to_has_ans.items() if not v]
				__lowerCamelCase					,    __lowerCamelCase    					=	get_raw_scores(__lowerCAmelCase		,  __lowerCAmelCase							)
				__lowerCamelCase    					=	apply_no_ans_threshold(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  OPTS.na_prob_thresh							)
				__lowerCamelCase    					=	apply_no_ans_threshold(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  OPTS.na_prob_thresh							)
				__lowerCamelCase    					=	make_eval_dict(__lowerCAmelCase		,  __lowerCAmelCase							)
				if has_ans_qids:
								__lowerCamelCase    					=	make_eval_dict(__lowerCAmelCase		,  __lowerCAmelCase		,  qid_list=__lowerCAmelCase							)
								merge_eval(__lowerCAmelCase		,  __lowerCAmelCase		,  '''HasAns'''							)
				if no_ans_qids:
								__lowerCamelCase    					=	make_eval_dict(__lowerCAmelCase		,  __lowerCAmelCase		,  qid_list=__lowerCAmelCase							)
								merge_eval(__lowerCAmelCase		,  __lowerCAmelCase		,  '''NoAns'''							)
				if OPTS.na_prob_file:
								find_all_best_thresh(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase							)
				if OPTS.na_prob_file and OPTS.out_image_dir:
								run_precision_recall_analysis(__lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  OPTS.out_image_dir							)
								histogram_na_prob(__lowerCAmelCase		,  __lowerCAmelCase		,  OPTS.out_image_dir		,  '''hasAns'''							)
								histogram_na_prob(__lowerCAmelCase		,  __lowerCAmelCase		,  OPTS.out_image_dir		,  '''noAns'''							)
				if OPTS.out_file:
								with open(OPTS.out_file		,  '''w'''							) as f:
												json.dump(__lowerCAmelCase		,  __lowerCAmelCase							)
				else:
								print(json.dumps(__lowerCAmelCase		,  indent=2							)							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			Any												=    parse_args()
					if OPTS.out_image_dir:
										import matplotlib

										matplotlib.use("Agg")
										import matplotlib.pyplot as plt
					main()



