





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A_												=     {
    "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",
}

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





def    __UpperCamelCase      (    a,     a=False) ->Tuple:
					lowerCamelCase__    ,		lowerCamelCase__       =       create_model(
					    "HTSAT-tiny",     "roberta",     UpperCamelCase__,     precision="fp32",     device="cuda:0" if torch.cuda.is_available() else "cpu",     enable_fusion=UpperCamelCase__,     fusion_type="aff_2d" if enable_fusion else None,     )
					return model, model_cfg





def    __UpperCamelCase      (    a) ->str:
					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(UpperCamelCase__,     UpperCamelCase__)

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

															lowerCamelCase__       =       key.replace(f"sequential.{sequential_layer}.",     f"layers.{int(UpperCamelCase__)//3}.linear.")
										elif re.match(UpperCamelCase__,     UpperCamelCase__):
															lowerCamelCase__       =       int(re.match(UpperCamelCase__,     UpperCamelCase__).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    __UpperCamelCase      (    a,     a,     a,     a=False) ->List[str]:
					lowerCamelCase__    ,		lowerCamelCase__       =       init_clap(UpperCamelCase__,     enable_fusion=UpperCamelCase__)

					clap_model.eval()
					lowerCamelCase__       =       clap_model.state_dict()
					lowerCamelCase__       =       rename_state_dict(UpperCamelCase__)

					lowerCamelCase__       =       ClapConfig()
					lowerCamelCase__       =       enable_fusion
					lowerCamelCase__       =       ClapModel(UpperCamelCase__)

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

					model.save_pretrained(UpperCamelCase__)
					transformers_config.save_pretrained(UpperCamelCase__)


if __name__ == "__main__":
				A_												=     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")
				A_												=     parser.parse_args()

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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A_												=     {
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "albert"





					def __init__( self	,  _lowerCAmelCase=3_0000	,  _lowerCAmelCase=128	,  _lowerCAmelCase=4096	,  _lowerCAmelCase=12	,  _lowerCAmelCase=1	,  _lowerCAmelCase=64	,  _lowerCAmelCase=1_6384	,  _lowerCAmelCase=1	,  _lowerCAmelCase="gelu_new"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=0	,  _lowerCAmelCase=512	,  _lowerCAmelCase=2	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1E-12	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase="absolute"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=3	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       embedding_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_hidden_groups
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       inner_group_num
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       classifier_dropout_prob
										lowerCamelCase__       =       position_embedding_type

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@property
					def 					__magic_name__ ( self	):
										if self.task == "multiple-choice":
															lowerCamelCase__       =       {0: "batch", 1: "choice", 2: "sequence"}
										else:
															lowerCamelCase__       =       {0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ]	)

