

"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCamelCase    : List[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    : int				 =						AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_=False					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      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     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Any    							=      {}

	lowerCAmelCase__      :		List[str]    							=      r'''.*sequential.(\d+).*'''
	lowerCAmelCase__      :		Tuple    							=      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__      :		List[str]    							=      key.replace(A_		,	A_					)

		if re.match(A_		,	A_					):
			# replace sequential layers with list
			lowerCAmelCase__      :		Optional[Any]    							=      re.match(A_		,	A_					).group(1					)

			lowerCAmelCase__      :		Dict    							=      key.replace(f'sequential.{sequential_layer}.'		,	f'layers.{int(A_					)//3}.linear.'					)
		elif re.match(A_		,	A_					):
			lowerCAmelCase__      :		Optional[int]    							=      int(re.match(A_		,	A_					).group(1					)					)

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

			lowerCAmelCase__      :		List[str]    							=      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__      :		List[Any]    							=      value
			lowerCAmelCase__      :		Any    							=      mixed_qkv.size(0					) // 3

			lowerCAmelCase__      :		Any    							=      mixed_qkv[:qkv_dim]
			lowerCAmelCase__      :		List[str]    							=      mixed_qkv[qkv_dim : qkv_dim * 2]
			lowerCAmelCase__      :		Optional[int]    							=      mixed_qkv[qkv_dim * 2 :]

			lowerCAmelCase__      :		Optional[int]    							=      query_layer
			lowerCAmelCase__      :		List[str]    							=      key_layer
			lowerCAmelCase__      :		List[str]    							=      value_layer
		else:
			lowerCAmelCase__      :		List[str]    							=      value

	return model_state_dict

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_		,	A_=False					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      init_clap(A_		,	enable_fusion=A_					)

	clap_model.eval()
	lowerCAmelCase__      :		str    							=      clap_model.state_dict()
	lowerCAmelCase__      :		int    							=      rename_state_dict(A_					)

	lowerCAmelCase__      :		Any    							=      ClapConfig()
	lowerCAmelCase__      :		Union[str, Any]    							=      enable_fusion
	lowerCAmelCase__      :		Union[str, Any]    							=      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__":
							__UpperCamelCase    : Dict				 =						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    : int				 =						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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




