
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


SCREAMING_SNAKE_CASE__  :     Dict   				=       {
    "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",
}

SCREAMING_SNAKE_CASE__  :     Tuple   				=       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")




def 					__magic_name__					(  __lowerCAmelCase      :		Optional[Any]						,    __lowerCAmelCase      :		Union[str, Any]=False				)     ->  Optional[Any]:
				__lowerCamelCase   ,       __lowerCamelCase          =      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 					__magic_name__					(  __lowerCAmelCase      :		Optional[Any]				)     ->  Dict:
				__lowerCamelCase          =      {}

				__lowerCamelCase          =      R'''.*sequential.(\d+).*'''
				__lowerCamelCase          =      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:
																__lowerCamelCase          =      key.replace(A__						,    A__				)

								if re.match(A__						,    A__				):
												# replace sequential layers with list
												__lowerCamelCase          =      re.match(A__						,    A__				).group(1				)

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

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

												__lowerCamelCase          =      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
												__lowerCamelCase          =      value
												__lowerCamelCase          =      mixed_qkv.size(0				) // 3

												__lowerCamelCase          =      mixed_qkv[:qkv_dim]
												__lowerCamelCase          =      mixed_qkv[qkv_dim : qkv_dim * 2]
												__lowerCamelCase          =      mixed_qkv[qkv_dim * 2 :]

												__lowerCamelCase          =      query_layer
												__lowerCamelCase          =      key_layer
												__lowerCamelCase          =      value_layer
								else:
												__lowerCamelCase          =      value

				return model_state_dict




def 					__magic_name__					(  __lowerCAmelCase      :		List[Any]						,    __lowerCAmelCase      :		Tuple						,    __lowerCAmelCase      :		List[Any]						,    __lowerCAmelCase      :		Tuple=False				)     ->  Dict:
				__lowerCamelCase   ,       __lowerCamelCase          =      init_clap(A__						,    enable_fusion=A__				)

				clap_model.eval()
				__lowerCamelCase          =      clap_model.state_dict()
				__lowerCamelCase          =      rename_state_dict(A__				)

				__lowerCamelCase          =      ClapConfig()
				__lowerCamelCase          =      enable_fusion
				__lowerCamelCase          =      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__":
					SCREAMING_SNAKE_CASE__  :     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")
					SCREAMING_SNAKE_CASE__  :     Union[str, Any]   				=       parser.parse_args()

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





'''simple docstring'''
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowercase__											={
    'cola': 2,
    'mnli': 3,
    'mrpc': 2,
    'sst-2': 2,
    'sts-b': 1,
    'qqp': 2,
    'qnli': 2,
    'rte': 2,
    'wnli': 2,
}


logging.set_verbosity_info()






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=None						):
	# Initialise PyTorch model
	a_      				=						XLNetConfig.from_json_file(A__						)

	a_      				=						finetuning_task.lower() if finetuning_task is not None else """"""
	if finetuning_task in GLUE_TASKS_NUM_LABELS:
		print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
		a_      				=						finetuning_task
		a_      				=						GLUE_TASKS_NUM_LABELS[finetuning_task]
		a_      				=						XLNetForSequenceClassification(A__						)
	elif "squad" in finetuning_task:
		a_      				=						finetuning_task
		a_      				=						XLNetForQuestionAnswering(A__						)
	else:
		a_      				=						XLNetLMHeadModel(A__						)

	# Load weights from tf checkpoint
	load_tf_weights_in_xlnet(A__  ,      A__  ,      A__						)

	# Save pytorch-model
	a_      				=						os.path.join(A__  ,      A__						)
	a_      				=						os.path.join(A__  ,      A__						)
	print(F'''Save PyTorch model to {os.path.abspath(A__						)}'''						)
	torch.save(model.state_dict()  ,      A__						)
	print(F'''Save configuration file to {os.path.abspath(A__						)}'''						)
	with open(A__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		f.write(config.to_json_string()						)


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--xlnet_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained XLNet model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Path to the folder to store the PyTorch model or dataset/vocab.',
		)
		parser.add_argument(
		    '--finetuning_task',
		    default=None,
		    type=str,
		    help='Name of a task on which the XLNet TensorFlow model was fine-tuned',
		)
		lowercase__											=parser.parse_args()
		print(args)

		convert_xlnet_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
		)
