


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						Optional[int]					,			lowerCamelCase :						NestedDataStructureLike[PathLike]					,			lowerCamelCase :						Optional[NamedSplit] = None					,			lowerCamelCase :						Optional[Features] = None					,			lowerCamelCase :						str = None					,			lowerCamelCase :						bool = False					,			lowerCamelCase :						bool = False					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Optional[int] = None					,			**lowerCamelCase :						str					,			)			->					Optional[int]:
														super().__init__(
														    lowerCamelCase					,			split=lowerCamelCase					,			features=lowerCamelCase					,			cache_dir=lowerCamelCase					,			keep_in_memory=lowerCamelCase					,			streaming=lowerCamelCase					,			num_proc=lowerCamelCase					,			**lowerCamelCase					,			)
														__snake_case	:    int											=     field
														__snake_case	:    str											=     path_or_paths if isinstance(lowerCamelCase					,			lowerCamelCase       ) else {self.split: path_or_paths}
														__snake_case	:    Dict											=     Json(
														    cache_dir=lowerCamelCase					,			data_files=lowerCamelCase					,			features=lowerCamelCase					,			field=lowerCamelCase					,			**lowerCamelCase					,			)






							def 					__snake_case      (							self :						str       )			->					str:
														# Build iterable dataset
														if self.streaming:
																					__snake_case	:    Dict											=     self.builder.as_streaming_dataset(split=self.split       )
														# Build regular (map-style) dataset
														else:
																					__snake_case	:    Union[str, Any]											=     None
																					__snake_case	:    Optional[int]											=     None
																					__snake_case	:    Optional[int]											=     None
																					__snake_case	:    str											=     None

																					self.builder.download_and_prepare(
																					    download_config=lowerCamelCase					,			download_mode=lowerCamelCase					,			verification_mode=lowerCamelCase					,			base_path=lowerCamelCase					,			num_proc=self.num_proc					,			)
																					__snake_case	:    Dict											=     self.builder.as_dataset(
																					    split=self.split					,			verification_mode=lowerCamelCase					,			in_memory=self.keep_in_memory       )
														return dataset

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			lowerCamelCase :						Dataset					,			lowerCamelCase :						Union[PathLike, BinaryIO]					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Optional[int] = None					,			**lowerCamelCase :						List[str]					,			)			->					List[str]:
														if num_proc is not None and num_proc <= 0:
																					raise ValueError(F'num_proc {num_proc} must be an integer > 0.'       )

														__snake_case	:    Optional[Any]											=     dataset
														__snake_case	:    Tuple											=     path_or_buf
														__snake_case	:    Optional[int]											=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
														__snake_case	:    Optional[int]											=     num_proc
														__snake_case	:    Union[str, Any]											=     "utf-8"
														__snake_case	:    List[str]											=     to_json_kwargs

							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    Any											=     self.to_json_kwargs.pop("path_or_buf"					,			lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     self.to_json_kwargs.pop("orient"					,			"records"       )
														__snake_case	:    Union[str, Any]											=     self.to_json_kwargs.pop("lines"					,			True if orient == "records" else False       )
														__snake_case	:    str											=     self.to_json_kwargs.pop("index"					,			False if orient in ["split", "table"] else True       )
														__snake_case	:    Dict											=     self.to_json_kwargs.pop("compression"					,			lowerCamelCase       )

														if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																					raise NotImplementedError(F'`datasets` currently does not support {compression} compression'       )

														if isinstance(self.path_or_buf					,			(str, bytes, os.PathLike)       ):
																					with fsspec.open(self.path_or_buf					,			"wb"					,			compression=lowerCamelCase       ) as buffer:
																												__snake_case	:    int											=     self._write(file_obj=lowerCamelCase					,			orient=lowerCamelCase					,			lines=lowerCamelCase					,			index=lowerCamelCase					,			**self.to_json_kwargs       )
														else:
																					if compression:
																												raise NotImplementedError(
																												    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																												    " was passed. Please provide a local path instead."       )
																					__snake_case	:    Optional[Any]											=     self._write(
																					    file_obj=self.path_or_buf					,			orient=lowerCamelCase					,			lines=lowerCamelCase					,			index=lowerCamelCase					,			**self.to_json_kwargs       )
														return written

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					List[str]:
														__snake_case				,    __snake_case				,    __snake_case				,    __snake_case				,    __snake_case	:    Dict											=     args

														__snake_case	:    List[str]											=     query_table(
														    table=self.dataset.data					,			key=slice(lowerCamelCase					,			offset + self.batch_size       )					,			indices=self.dataset._indices					,			)
														__snake_case	:    str											=     batch.to_pandas().to_json(
														    path_or_buf=lowerCamelCase					,			orient=lowerCamelCase					,			lines=lowerCamelCase					,			index=lowerCamelCase					,			**lowerCamelCase       )
														if not json_str.endswith("\n"       ):
																					json_str += "\n"
														return json_str.encode(self.encoding       )






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						BinaryIO					,			lowerCamelCase :						str					,			lowerCamelCase :						int					,			lowerCamelCase :						int					,			**lowerCamelCase :						Union[str, Any]					,			)			->					int:
														__snake_case	:    Union[str, Any]											=     0

														if self.num_proc is None or self.num_proc == 1:
																					for offset in logging.tqdm(
																					    range(0					,			len(self.dataset       )					,			self.batch_size       )					,			unit="ba"					,			disable=not logging.is_progress_bar_enabled()					,			desc="Creating json from Arrow format"					,			):
																												__snake_case	:    str											=     self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
																												written += file_obj.write(lowerCamelCase       )
														else:
																					__snake_case				,    __snake_case	:    str											=     len(self.dataset       ), self.batch_size
																					with multiprocessing.Pool(self.num_proc       ) as pool:
																												for json_str in logging.tqdm(
																												    pool.imap(
																												        self._batch_json					,			[(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,			lowerCamelCase					,			lowerCamelCase       )]					,			)					,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,			unit="ba"					,			disable=not logging.is_progress_bar_enabled()					,			desc="Creating json from Arrow format"					,			):
																																			written += file_obj.write(lowerCamelCase       )

														return written





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "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",
}

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    Any											=     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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    Any											=     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	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

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

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

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     1 if projecton_layer == 0 else 2

																					__snake_case	:    Tuple											=     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	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     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__":
			_snake_case  :	Union[str, 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")
			_snake_case  :	Tuple			      =       parser.parse_args()

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

