

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCAmelCase							:  Optional[Any]   	=						logging.getLogger()



@unittest.skip('''Temporarily disable the doc tests.'''	)
@require_torch
@require_tf
@slow
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Path   ,						lowerCAmelCase__  :     Union[str, None] = None   ,						lowerCAmelCase__  :     Union[List[str], None] = None   ,						lowerCAmelCase__  :     Union[str, List[str], None] = None   ,						lowerCAmelCase__  :     bool = True   ,						):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [file for file in os.listdir(lowerCAmelCase__) if os.path.isfile(os.path.join(lowerCAmelCase__   ,						lowerCAmelCase__))]

									if identifier is not None:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       [file for file in files if identifier in file]

									if n_identifier is not None:
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					for n_ in n_identifier:
																											SCREAMING_SNAKE_CASE_: Any  		=       [file for file in files if n_ not in file]
															else:
																					SCREAMING_SNAKE_CASE_: Dict  		=       [file for file in files if n_identifier not in file]

									SCREAMING_SNAKE_CASE_: List[Any]  		=       ignore_files or []
									ignore_files.append("__init__.py")
									SCREAMING_SNAKE_CASE_: List[str]  		=       [file for file in files if file not in ignore_files]

									for file in files:
															# Open all files
															print("Testing"   ,						lowerCAmelCase__)

															if only_modules:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       file.split(".")[0]
																					try:
																											SCREAMING_SNAKE_CASE_: Optional[int]  		=       getattr(lowerCAmelCase__   ,						lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_: Optional[Any]  		=       doctest.DocTestSuite(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_: Any  		=       unittest.TextTestRunner().run(lowerCAmelCase__)
																											self.assertIs(len(result.failures)   ,						0)
																					except AttributeError:
																											logger.info(F"{module_identifier} is not a module.")
															else:
																					SCREAMING_SNAKE_CASE_: Tuple  		=       doctest.testfile(str(".." / directory / file)   ,						optionflags=doctest.ELLIPSIS)
																					self.assertIs(result.failed   ,						0)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: str  		=       Path("src/transformers")
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "modeling"
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [
									    "modeling_ctrl.py",
									    "modeling_tf_ctrl.py",
									]
									self.analyze_directory(lowerCAmelCase__   ,						identifier=lowerCAmelCase__   ,						ignore_files=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       Path("src/transformers")
									SCREAMING_SNAKE_CASE_: List[str]  		=       "tokenization"
									self.analyze_directory(lowerCAmelCase__   ,						identifier=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: str  		=       Path("src/transformers")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       "configuration"
									self.analyze_directory(lowerCAmelCase__   ,						identifier=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       Path("src/transformers")
									SCREAMING_SNAKE_CASE_: int  		=       ["configuration", "modeling", "tokenization"]
									self.analyze_directory(lowerCAmelCase__   ,						n_identifier=lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       Path("docs/source")
									SCREAMING_SNAKE_CASE_: str  		=       ["favicon.ico"]
									self.analyze_directory(lowerCAmelCase__   ,						ignore_files=lowerCAmelCase__   ,						only_modules=lowerCAmelCase__)









import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, 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							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

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

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

																		SCREAMING_SNAKE_CASE_: Dict  		=       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
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

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

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  Tuple   	=						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							:  int   	=						parser.parse_args()

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






