



"""simple docstring"""

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__					:						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__					:						str												=			AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')




def __lowercase    (							_a	,    _a=False					):
    snake_case_,			snake_case_				:  Dict       =			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 __lowercase    (							_a					):
    snake_case_				:  List[Any]       =			{}

    snake_case_				:  Tuple       =			r'''.*sequential.(\d+).*'''
    snake_case_				:  List[str]       =			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:
                snake_case_				:  List[Any]       =			key.replace(_a	,    _a					)

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

            snake_case_				:  str       =			key.replace(f"sequential.{sequential_layer}."	,    f"layers.{int(_a					)//3}.linear."					)
        elif re.match(_a	,    _a					):
            snake_case_				:  List[str]       =			int(re.match(_a	,    _a					).group(1					)					)

            # Because in CLAP they use `nn.Sequential`...
            snake_case_				:  Optional[int]       =			1 if projecton_layer == 0 else 2

            snake_case_				:  List[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
            snake_case_				:  Any       =			value
            snake_case_				:  Tuple       =			mixed_qkv.size(0					) // 3

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

            snake_case_				:  Optional[Any]       =			query_layer
            snake_case_				:  Optional[int]       =			key_layer
            snake_case_				:  int       =			value_layer
        else:
            snake_case_				:  int       =			value

    return model_state_dict




def __lowercase    (							_a	,    _a	,    _a	,    _a=False					):
    snake_case_,			snake_case_				:  Tuple       =			init_clap(_a	,    enable_fusion=_a					)

    clap_model.eval()
    snake_case_				:  Dict       =			clap_model.state_dict()
    snake_case_				:  List[str]       =			rename_state_dict(_a					)

    snake_case_				:  Dict       =			ClapConfig()
    snake_case_				:  Optional[Any]       =			enable_fusion
    snake_case_				:  str       =			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__					:						List[Any]												=			parser.parse_args()

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










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





