"""simple docstring"""





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
			from transformers import TvltImageProcessor

if is_speech_available():
			from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    """ZinengTang/tvlt-base"""
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tempfile.mkdtemp()





				def 		_lowerCAmelCase		(      self							:							str      ,    **_A							:							Dict):



											"""simple docstring"""







											return TvltImageProcessor.from_pretrained(self.checkpoint      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    **_A							:							str):



											"""simple docstring"""







											return TvltFeatureExtractor.from_pretrained(self.checkpoint      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											shutil.rmtree(self.tmpdirname)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.get_image_processor()
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.get_feature_extractor()

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TvltProcessor(image_processor=_A      ,    feature_extractor=_A)
											processor.save_pretrained(self.tmpdirname)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    TvltProcessor.from_pretrained(self.tmpdirname)

											self.assertIsInstance(processor.feature_extractor      ,    _A)
											self.assertIsInstance(processor.image_processor      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.get_image_processor()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.get_feature_extractor()

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    TvltProcessor(image_processor=_A      ,    feature_extractor=_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    np.ones([1_2_0_0_0])

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    feature_extractor(_A      ,    return_tensors="""np""")
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    processor(audio=_A      ,    return_tensors="""np""")

											for key in audio_dict.keys():
																		self.assertAlmostEqual(audio_dict[key].sum()      ,    input_processor[key].sum()      ,    delta=1e-2)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.get_image_processor()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_feature_extractor()

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TvltProcessor(image_processor=_A      ,    feature_extractor=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.ones([3, 2_2_4, 2_2_4])

											_SCREAMING_SNAKE_CASE		:						int			  =    image_processor(_A      ,    return_tensors="""np""")
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    processor(images=_A      ,    return_tensors="""np""")

											for key in image_dict.keys():
																		self.assertAlmostEqual(image_dict[key].sum()      ,    input_processor[key].sum()      ,    delta=1e-2)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.get_image_processor()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_feature_extractor()

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    TvltProcessor(image_processor=_A      ,    feature_extractor=_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.ones([1_2_0_0_0])
											_SCREAMING_SNAKE_CASE		:						str			  =    np.ones([3, 2_2_4, 2_2_4])

											_SCREAMING_SNAKE_CASE		:						Any			  =    processor(audio=_A      ,    images=_A)

											self.assertListEqual(list(inputs.keys())      ,    ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""])

											# test if it raises when no input is passed
											with pytest.raises(_A):
																		processor()





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.get_image_processor()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_feature_extractor()

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    TvltProcessor(image_processor=_A      ,    feature_extractor=_A)

											self.assertListEqual(
											    processor.model_input_names      ,    image_processor.model_input_names + feature_extractor.model_input_names      ,    msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match"""      ,    )



"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase_     =	{
    '''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_     =	AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    R""".*sequential.(\d+).*"""
							_SCREAMING_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:
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    key.replace(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    key.replace(F"""sequential.{sequential_layer}."""     ,	F"""layers.{int(__SCREAMING_SNAKE_CASE  )//3}.linear."""  )
														elif re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    int(re.match(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).group(1  )  )

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

																					_SCREAMING_SNAKE_CASE		:						Union[str, 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
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mixed_qkv.size(0  ) // 3

																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    mixed_qkv[:qkv_dim]
																					_SCREAMING_SNAKE_CASE		:						str			  =    mixed_qkv[qkv_dim : qkv_dim * 2]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    mixed_qkv[qkv_dim * 2 :]

																					_SCREAMING_SNAKE_CASE		:						Dict			  =    query_layer
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    key_layer
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    value_layer
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value

							return model_state_dict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    List[Any]:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    init_clap(__SCREAMING_SNAKE_CASE     ,	enable_fusion=__SCREAMING_SNAKE_CASE  )

							clap_model.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    clap_model.state_dict()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    rename_state_dict(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						int			  =    ClapConfig()
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    enable_fusion
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ClapModel(__SCREAMING_SNAKE_CASE  )

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

							model.save_pretrained(__SCREAMING_SNAKE_CASE  )
							transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	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_     =	parser.parse_args()

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


