


'''simple docstring'''

import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
	import sqlitea

	import sqlalchemy


class       a__				( UpperCamelCase_      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = False       ,			**__lowercase       ,			):
										super().__init__(features=_a       ,			cache_dir=_a       ,			keep_in_memory=_a       ,			**_a		)
										__lowerCAmelCase							       =				Sql(
										    cache_dir=_a       ,			features=_a       ,			sql=_a       ,			con=_a       ,			**_a       ,			)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				None
										__lowerCAmelCase							       =				None
										__lowerCAmelCase							       =				None
										__lowerCAmelCase							       =				None

										self.builder.download_and_prepare(
										    download_config=_a       ,			download_mode=_a       ,			verification_mode=_a       ,			base_path=_a       ,			)

										# Build dataset for splits
										__lowerCAmelCase							       =				self.builder.as_dataset(
										    split='''train'''       ,			verification_mode=_a       ,			in_memory=self.keep_in_memory		)
										return dataset


class       a__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			**__lowercase       ,			):


										if num_proc is not None and num_proc <= 0:
																raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

										__lowerCAmelCase							       =				dataset
										__lowerCAmelCase							       =				name
										__lowerCAmelCase							       =				con
										__lowerCAmelCase							       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										__lowerCAmelCase							       =				num_proc
										__lowerCAmelCase							       =				to_sql_kwargs




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.to_sql_kwargs.pop('''sql'''       ,			_a		)
										__lowerCAmelCase							       =				self.to_sql_kwargs.pop('''con'''       ,			_a		)
										__lowerCAmelCase							       =				self.to_sql_kwargs.pop('''index'''       ,			_a		)

										__lowerCAmelCase							       =				self._write(index=_a       ,			**self.to_sql_kwargs		)
										return written




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				args
										__lowerCAmelCase							       =				{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
										__lowerCAmelCase							       =				query_table(
										    table=self.dataset.data       ,			key=slice(_a       ,			offset + self.batch_size		)       ,			indices=self.dataset._indices       ,			)
										__lowerCAmelCase							       =				batch.to_pandas()
										__lowerCAmelCase							       =				df.to_sql(self.name       ,			self.con       ,			index=_a       ,			**_a		)
										return num_rows or len(_a		)




				def 					_snake_case  (self       ,			__lowercase       ,			**__lowercase		):
										__lowerCAmelCase							       =				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 SQL from Arrow format'''       ,			):
																						written += self._batch_sql((offset, index, to_sql_kwargs)		)
										else:
																__lowerCAmelCase							       =				len(self.dataset		), self.batch_size
																with multiprocessing.Pool(self.num_proc		) as pool:
																						for num_rows in logging.tqdm(
																						    pool.imap(
																						        self._batch_sql       ,			[(offset, index, to_sql_kwargs) for offset in range(0       ,			_a       ,			_a		)]       ,			)       ,			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 SQL from Arrow format'''       ,			):
																												written += num_rows

										return written





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCAmelCase				: 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""",
}

_UpperCAmelCase				: Optional[int]							    =				AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase):
						__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(lowerCamelCase,				lowerCamelCase)

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

																		__lowerCAmelCase							       =				key.replace(F"""sequential.{sequential_layer}.""",				F"""layers.{int(lowerCamelCase)//3}.linear.""")
												elif re.match(lowerCamelCase,				lowerCamelCase):
																		__lowerCAmelCase							       =				int(re.match(lowerCamelCase,				lowerCamelCase).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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				init_clap(lowerCamelCase,				enable_fusion=lowerCamelCase)

						clap_model.eval()
						__lowerCAmelCase							       =				clap_model.state_dict()
						__lowerCAmelCase							       =				rename_state_dict(lowerCamelCase)

						__lowerCAmelCase							       =				ClapConfig()
						__lowerCAmelCase							       =				enable_fusion
						__lowerCAmelCase							       =				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__":
	_UpperCAmelCase				: 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""")
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()

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

