

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 			lowerCamelCase__			(       _a				,      _a=False):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							create_model(
	    "HTSAT-tiny"				,      "roberta"				,      _a				,      precision="fp32"				,      device="cuda:0" if torch.cuda.is_available() else "cpu"				,      enable_fusion=_a				,      fusion_type="aff_2d" if enable_fusion else None				,      )
	return model, model_cfg

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   int					  =							{}

	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							r".*sequential.(\d+).*"
	SCREAMING_SNAKE_CASE :   int					  =							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(_a				,      _a)

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

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

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

			SCREAMING_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
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							value
			SCREAMING_SNAKE_CASE :   Dict					  =							mixed_qkv.size(0) // 3

			SCREAMING_SNAKE_CASE :   Dict					  =							mixed_qkv[:qkv_dim]
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							mixed_qkv[qkv_dim : qkv_dim * 2]
			SCREAMING_SNAKE_CASE :   str					  =							mixed_qkv[qkv_dim * 2 :]

			SCREAMING_SNAKE_CASE :   Dict					  =							query_layer
			SCREAMING_SNAKE_CASE :   str					  =							key_layer
			SCREAMING_SNAKE_CASE :   List[str]					  =							value_layer
		else:
			SCREAMING_SNAKE_CASE :   Any					  =							value

	return model_state_dict

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a=False):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							init_clap(_a				,      enable_fusion=_a)

	clap_model.eval()
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							clap_model.state_dict()
	SCREAMING_SNAKE_CASE :   Tuple					  =							rename_state_dict(_a)

	SCREAMING_SNAKE_CASE :   Any					  =							ClapConfig()
	SCREAMING_SNAKE_CASE :   Tuple					  =							enable_fusion
	SCREAMING_SNAKE_CASE :   str					  =							ClapModel(_a)

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

	model.save_pretrained(_a)
	transformers_config.save_pretrained(_a)


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)


from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 			lowerCamelCase__			(       _a):
	return getitem, k

def 			lowerCamelCase__			(       _a				,      _a):
	return setitem, k, v

def 			lowerCamelCase__			(       _a):
	return delitem, k

def 			lowerCamelCase__			(       _a				,      _a				,      *_a):
	try:
		return fun(_a				,      *_a), None
	except Exception as e:
		return None, e


a_      =     (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

a_      =     [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]

@pytest.mark.parametrize(
    "operations"				,      (
        pytest.param(_add_items				,      id="add items"),
        pytest.param(_overwrite_items				,      id="overwrite items"),
        pytest.param(_delete_items				,      id="delete items"),
        pytest.param(_access_absent_items				,      id="access absent items"),
        pytest.param(_add_with_resize_up				,      id="add with resize up"),
        pytest.param(_add_with_resize_down				,      id="add with resize down"),
    )				,      )
def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							HashMap(initial_block_size=4)
	SCREAMING_SNAKE_CASE :   List[str]					  =							{}
	for _, (fun, *args) in enumerate(_a):
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							_run_operation(_a				,      _a				,      *_a)
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							_run_operation(_a				,      _a				,      *_a)
		assert my_res == py_res
		assert str(_a) == str(_a)
		assert set(_a) == set(_a)
		assert len(_a) == len(_a)
		assert set(my.items()) == set(py.items())


def 			lowerCamelCase__			(       ):
	def is_public(_a) -> bool:
		return not name.startswith("_")

	SCREAMING_SNAKE_CASE :   List[str]					  =							{name for name in dir({}) if is_public(_a)}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{name for name in dir(HashMap()) if is_public(_a)}

	assert dict_public_names > hash_public_names