





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCamelCase : Optional[Any]											=			{
    "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",
}

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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=False							):

				"""simple docstring"""






				lowercase   ,							lowercase         = create_model(
				    "HTSAT-tiny"		,							"roberta"		,							lowerCAmelCase_		,							precision="fp32"		,							device="cuda:0" if torch.cuda.is_available() else "cpu"		,							enable_fusion=lowerCAmelCase_		,							fusion_type="aff_2d" if enable_fusion else None		,							)
				return model, model_cfg







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}

				lowercase         = R".*sequential.(\d+).*"
				lowercase         = 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:
																lowercase         = key.replace(lowerCAmelCase_		,							lowerCAmelCase_							)

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

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

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

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

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

												lowercase         = query_layer
												lowercase         = key_layer
												lowercase         = value_layer
								else:
												lowercase         = value

				return model_state_dict







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=False							):

				"""simple docstring"""






				lowercase   ,							lowercase         = init_clap(lowerCAmelCase_		,							enable_fusion=lowerCAmelCase_							)

				clap_model.eval()
				lowercase         = clap_model.state_dict()
				lowercase         = rename_state_dict(lowerCAmelCase_							)

				lowercase         = ClapConfig()
				lowercase         = enable_fusion
				lowercase         = ClapModel(lowerCAmelCase_							)

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

				model.save_pretrained(lowerCAmelCase_							)
				transformers_config.save_pretrained(lowerCAmelCase_							)


if __name__ == "__main__":
			__lowerCamelCase : Dict											=			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")
			__lowerCamelCase : List[str]											=			parser.parse_args()

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











'''simple docstring'''

from __future__ import annotations

class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	str							,			A__      :	str				)					-> str:
						lowercase   ,							lowercase         = text, pattern
						lowercase   ,							lowercase         = len(A__				), len(A__				)




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	str				)					-> int:

						for i in range(self.patLen - 1							,			-1							,			-1				):
										if char == self.pattern[i]:
														return i
						return -1




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	int				)					-> int:

						for i in range(self.patLen - 1							,			-1							,			-1				):
										if self.pattern[i] != self.text[current_pos + i]:
														return current_pos + i
						return -1





		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> list[int]:
						# searches pattern in text and returns index positions
						lowercase         = []
						for i in range(self.textLen - self.patLen + 1				):
										lowercase         = self.mismatch_in_text(A__				)
										if mismatch_index == -1:
														positions.append(A__				)
										else:
														lowercase         = self.match_in_pattern(self.text[mismatch_index]				)
														lowercase         = (
														    mismatch_index - match_index
														)  # shifting index lgtm [py/multiple-definition]
						return positions




__lowerCamelCase : List[str]											=			"ABAABA"
__lowerCamelCase : List[str]											=			"AB"
__lowerCamelCase : Union[str, Any]											=			BoyerMooreSearch(text, pattern)
__lowerCamelCase : List[str]											=			bms.bad_character_heuristic()

if len(positions) == 0:
			print("No match found")
else:
			print("Pattern found in following positions: ")
			print(positions)




