
"""simple docstring"""


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_lowercase				 =					{
    '''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''',
}

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






def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Dict=False  ):
	A ,   A     = create_model(
	    'HTSAT-tiny'						,   'roberta'						,   snake_case__						,   precision='fp32'						,   device='cuda:0' if torch.cuda.is_available() else 'cpu'						,   enable_fusion=snake_case__						,   fusion_type='aff_2d' if enable_fusion else None						,   )
	return model, model_cfg






def    _snake_case					(		snake_case__ :  Dict  ):
	A     = {}

	A     = r'.*sequential.(\d+).*'
	A     = 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:
				A     = key.replace(snake_case__						,   snake_case__  )

		if re.match(snake_case__						,   snake_case__  ):
			# replace sequential layers with list
			A     = re.match(snake_case__						,   snake_case__  ).group(1  )

			A     = key.replace(F'sequential.{sequential_layer}.'						,   F'layers.{int(snake_case__  )//3}.linear.'  )
		elif re.match(snake_case__						,   snake_case__  ):
			A     = int(re.match(snake_case__						,   snake_case__  ).group(1  )  )

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

			A     = 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
			A     = value
			A     = mixed_qkv.size(0  ) // 3

			A     = mixed_qkv[:qkv_dim]
			A     = mixed_qkv[qkv_dim : qkv_dim * 2]
			A     = mixed_qkv[qkv_dim * 2 :]

			A     = query_layer
			A     = key_layer
			A     = value_layer
		else:
			A     = value

	return model_state_dict






def    _snake_case					(		snake_case__ :  Optional[Any]						,   snake_case__ :  Optional[Any]						,   snake_case__ :  str						,   snake_case__ :  str=False  ):
	A ,   A     = init_clap(snake_case__						,   enable_fusion=snake_case__  )

	clap_model.eval()
	A     = clap_model.state_dict()
	A     = rename_state_dict(snake_case__  )

	A     = ClapConfig()
	A     = enable_fusion
	A     = ClapModel(snake_case__  )

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

	model.save_pretrained(snake_case__  )
	transformers_config.save_pretrained(snake_case__  )


if __name__ == "__main__":
		_lowercase				 =					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''')
		_lowercase				 =					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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase				 =					{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''ElectraTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ElectraForCausalLM''',
		    '''ElectraForMaskedLM''',
		    '''ElectraForMultipleChoice''',
		    '''ElectraForPreTraining''',
		    '''ElectraForQuestionAnswering''',
		    '''ElectraForSequenceClassification''',
		    '''ElectraForTokenClassification''',
		    '''ElectraModel''',
		    '''ElectraPreTrainedModel''',
		    '''load_tf_weights_in_electra''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFElectraForMaskedLM''',
		    '''TFElectraForMultipleChoice''',
		    '''TFElectraForPreTraining''',
		    '''TFElectraForQuestionAnswering''',
		    '''TFElectraForSequenceClassification''',
		    '''TFElectraForTokenClassification''',
		    '''TFElectraModel''',
		    '''TFElectraPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''FlaxElectraForCausalLM''',
		    '''FlaxElectraForMaskedLM''',
		    '''FlaxElectraForMultipleChoice''',
		    '''FlaxElectraForPreTraining''',
		    '''FlaxElectraForQuestionAnswering''',
		    '''FlaxElectraForSequenceClassification''',
		    '''FlaxElectraForTokenClassification''',
		    '''FlaxElectraModel''',
		    '''FlaxElectraPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
		from .tokenization_electra import ElectraTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_electra_fast import ElectraTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_electra import (
				    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				    ElectraPreTrainedModel,
				    load_tf_weights_in_electra,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_electra import (
				    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFElectraForMaskedLM,
				    TFElectraForMultipleChoice,
				    TFElectraForPreTraining,
				    TFElectraForQuestionAnswering,
				    TFElectraForSequenceClassification,
				    TFElectraForTokenClassification,
				    TFElectraModel,
				    TFElectraPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				    FlaxElectraPreTrainedModel,
				)

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)