



"""simple docstring"""
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__a      =				{
    "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      =				AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")
def    A_  (			_lowercase,					_lowercase=False	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :str						=			{}

						snake_case_ :Optional[int]						=			r""".*sequential.(\d+).*"""
						snake_case_ :Union[str, 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_ :Tuple						=			key.replace(_lowercase,					_lowercase	)

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

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

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

																		snake_case_ :Optional[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_ :Dict						=			value
																		snake_case_ :str						=			mixed_qkv.size(0	) // 3

																		snake_case_ :List[Any]						=			mixed_qkv[:qkv_dim]
																		snake_case_ :List[Any]						=			mixed_qkv[qkv_dim : qkv_dim * 2]
																		snake_case_ :Dict						=			mixed_qkv[qkv_dim * 2 :]

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

						return model_state_dict
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase=False	):







						'''simple docstring'''


						snake_case_,       snake_case_ :Union[str, Any]						=			init_clap(_lowercase,					enable_fusion=_lowercase	)

						clap_model.eval()
						snake_case_ :Any						=			clap_model.state_dict()
						snake_case_ :Dict						=			rename_state_dict(_lowercase	)

						snake_case_ :Optional[int]						=			ClapConfig()
						snake_case_ :Optional[int]						=			enable_fusion
						snake_case_ :Dict						=			ClapModel(_lowercase	)

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

						model.save_pretrained(_lowercase	)
						transformers_config.save_pretrained(_lowercase	)


if __name__ == "__main__":
						__a      =				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      =				parser.parse_args()

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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ReformerAttention",
						    "ReformerForMaskedLM",
						    "ReformerForQuestionAnswering",
						    "ReformerForSequenceClassification",
						    "ReformerLayer",
						    "ReformerModel",
						    "ReformerModelWithLMHead",
						    "ReformerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer import ReformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer_fast import ReformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_reformer import (
												    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ReformerAttention,
												    ReformerForMaskedLM,
												    ReformerForQuestionAnswering,
												    ReformerForSequenceClassification,
												    ReformerLayer,
												    ReformerModel,
												    ReformerModelWithLMHead,
												    ReformerPreTrainedModel,
												)

else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
