



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    len(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    []
				for i in range(len(_SCREAMING_SNAKE_CASE				) - pat_len + 1				):
								_UpperCAmelCase					=    True
								for j in range(_SCREAMING_SNAKE_CASE				):
												if s[i + j] != pattern[j]:
																_UpperCAmelCase					=    False
																break
								if match_found:
												position.append(_SCREAMING_SNAKE_CASE				)
				return position


if __name__ == "__main__":
					assert naive_pattern_search("ABCDEFG", "DE") == [3]
					print(naive_pattern_search("ABAAABCDBBABCDDEBCABC", "ABC"))








"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A   :							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",
}

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    r'''.*sequential.(\d+).*'''
				_UpperCAmelCase					=    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:
																_UpperCAmelCase					=    key.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

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

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

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

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

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

												_UpperCAmelCase					=    query_layer
												_UpperCAmelCase					=    key_layer
												_UpperCAmelCase					=    value_layer
								else:
												_UpperCAmelCase					=    value

				return model_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						int=False				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    init_clap(_SCREAMING_SNAKE_CASE					,     enable_fusion=_SCREAMING_SNAKE_CASE				)

				clap_model.eval()
				_UpperCAmelCase					=    clap_model.state_dict()
				_UpperCAmelCase					=    rename_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    ClapConfig()
				_UpperCAmelCase					=    enable_fusion
				_UpperCAmelCase					=    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__":
					__A   :							Any										=   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   :							int										=   parser.parse_args()

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



