




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase_		:				Optional[Any]									=     logging.get_logger(__name__)




class 		__A      (			__UpperCAmelCase			):

						def __init__(		self   :Optional[Any]    , __snake_case   :int    , __snake_case   :int    , __snake_case   :float    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=feature_size
										__magic_name__	:	Optional[Any]								=sampling_rate
										__magic_name__	:	str								=padding_value

										__magic_name__	:	str								=kwargs.pop("""padding_side"""    , """right""" )
										__magic_name__	:	Any								=kwargs.pop("""return_attention_mask"""    , lowerCAmelCase_ )

										super().__init__(**lowerCAmelCase_ )

						def     A__						(		self   :int    , __snake_case   :Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]    , __snake_case   :Union[bool, str, PaddingStrategy] = True    , __snake_case   :Optional[int] = None    , __snake_case   :bool = False    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[Union[str, TensorType]] = None    , ):
										'''simple docstring'''

										if isinstance(lowerCAmelCase_    , (list, tuple) ) and isinstance(processed_features[0]    , (dict, BatchFeature) ):
														__magic_name__	:	Tuple								={
														    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
														}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
														raise ValueError(
														    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
														    f" to this method that includes {self.model_input_names[0]}, but you provided"
														    f" {list(processed_features.keys() )}" )

										__magic_name__	:	Optional[Any]								=processed_features[self.model_input_names[0]]
										__magic_name__	:	Dict								=(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(lowerCAmelCase_ ) == 0:
														if return_attention_mask:
																		__magic_name__	:	Any								=[]
														return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										__magic_name__	:	str								=required_input[0]
										if isinstance(lowerCAmelCase_    , (list, tuple) ):
														# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
														__magic_name__	:	int								=0
														while len(required_input[index] ) == 0:
																		index += 1
														if index < len(lowerCAmelCase_ ):
																		__magic_name__	:	List[Any]								=required_input[index][0]

										if return_tensors is None:
														if is_tf_tensor(lowerCAmelCase_ ):
																		__magic_name__	:	Any								="""tf"""
														elif is_torch_tensor(lowerCAmelCase_ ):
																		__magic_name__	:	List[Any]								="""pt"""
														elif isinstance(lowerCAmelCase_    , (int, float, list, tuple, np.ndarray) ):
																		__magic_name__	:	Tuple								="""np"""
														else:
																		raise ValueError(
																		    f"type of {first_element} unknown: {type(lowerCAmelCase_ )}. "
																		    """Should be one of a python, numpy, pytorch or tensorflow object.""" )

										for key, value in processed_features.items():
														if isinstance(value[0]    , (int, float) ):
																		__magic_name__	:	List[str]								=to_numpy(lowerCAmelCase_ )
														else:
																		__magic_name__	:	List[Any]								=[to_numpy(lowerCAmelCase_ ) for v in value]

														# Convert padding_strategy in PaddingStrategy
										__magic_name__	:	str								=self._get_padding_strategies(padding=lowerCAmelCase_    , max_length=lowerCAmelCase_ )

										__magic_name__	:	Dict								=processed_features[self.model_input_names[0]]

										__magic_name__	:	Tuple								=len(lowerCAmelCase_ )
										if not all(len(lowerCAmelCase_ ) == batch_size for v in processed_features.values() ):
														raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )

										__magic_name__	:	Optional[Any]								=[]
										for i in range(lowerCAmelCase_ ):
														__magic_name__	:	List[str]								={k: v[i] for k, v in processed_features.items()}
														# truncation
														__magic_name__	:	Tuple								=self._truncate(
														    lowerCAmelCase_    , max_length=lowerCAmelCase_    , pad_to_multiple_of=lowerCAmelCase_    , truncation=lowerCAmelCase_    , )
														truncated_inputs.append(lowerCAmelCase_ )

										if padding_strategy == PaddingStrategy.LONGEST:
														# make sure that `max_length` cannot be longer than the longest truncated length
														__magic_name__	:	Optional[int]								=max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
														__magic_name__	:	Optional[Any]								=PaddingStrategy.MAX_LENGTH

										__magic_name__	:	str								={}
										for i in range(lowerCAmelCase_ ):
														# padding
														__magic_name__	:	str								=self._pad(
														    truncated_inputs[i]    , max_length=lowerCAmelCase_    , padding_strategy=lowerCAmelCase_    , pad_to_multiple_of=lowerCAmelCase_    , return_attention_mask=lowerCAmelCase_    , )

														for key, value in outputs.items():
																		if key not in batch_outputs:
																						__magic_name__	:	List[str]								=[]
																		if value.dtype is np.dtype(np.floataa ):
																						__magic_name__	:	List[str]								=value.astype(np.floataa )
																		batch_outputs[key].append(lowerCAmelCase_ )

										return BatchFeature(lowerCAmelCase_    , tensor_type=lowerCAmelCase_ )

						def     A__						(		self   :Dict    , __snake_case   :Union[Dict[str, np.ndarray], BatchFeature]    , __snake_case   :Optional[int] = None    , __snake_case   :PaddingStrategy = PaddingStrategy.DO_NOT_PAD    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , ):
										'''simple docstring'''

										__magic_name__	:	str								=processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
														__magic_name__	:	Dict								=len(lowerCAmelCase_ )

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__	:	int								=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__	:	Union[str, Any]								=padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase_ ) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
														__magic_name__	:	int								=np.ones(len(lowerCAmelCase_ )    , dtype=np.intaa )

										if needs_to_be_padded:
														__magic_name__	:	str								=max_length - len(lowerCAmelCase_ )
														if self.padding_side == "right":
																		if return_attention_mask:
																						__magic_name__	:	Any								=np.pad(
																						    processed_features["""attention_mask"""]    , (0, difference) )
																		__magic_name__	:	int								=((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																		__magic_name__	:	Union[str, Any]								=np.pad(
																		    lowerCAmelCase_    , lowerCAmelCase_    , """constant"""    , constant_values=self.padding_value )
														elif self.padding_side == "left":
																		if return_attention_mask:
																						__magic_name__	:	int								=np.pad(
																						    processed_features["""attention_mask"""]    , (difference, 0) )
																		__magic_name__	:	Optional[int]								=((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																		__magic_name__	:	int								=np.pad(
																		    lowerCAmelCase_    , lowerCAmelCase_    , """constant"""    , constant_values=self.padding_value )
														else:
																		raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )

										return processed_features

						def     A__						(		self   :int    , __snake_case   :Union[Dict[str, np.ndarray], BatchFeature]    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , ):
										'''simple docstring'''

										if not truncation:
														return processed_features
										elif truncation and max_length is None:
														raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )

										__magic_name__	:	Optional[int]								=processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__	:	Tuple								=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__	:	Optional[Any]								=len(lowerCAmelCase_ ) > max_length

										if needs_to_be_truncated:
														__magic_name__	:	str								=processed_features[self.model_input_names[0]][:max_length]
														if "attention_mask" in processed_features:
																		__magic_name__	:	List[Any]								=processed_features["""attention_mask"""][:max_length]

										return processed_features


						def     A__						(		self   :Union[str, Any]    , __snake_case   :Optional[Any]=False    , __snake_case   :List[Any]=None ):
										'''simple docstring'''

										if padding is not False:
														if padding is True:
																		__magic_name__	:	List[str]								=PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
														elif not isinstance(lowerCAmelCase_    , lowerCAmelCase_ ):
																		__magic_name__	:	Dict								=PaddingStrategy(lowerCAmelCase_ )
														elif isinstance(lowerCAmelCase_    , lowerCAmelCase_ ):
																		__magic_name__	:	Any								=padding
										else:
														__magic_name__	:	List[Any]								=PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
														if padding_strategy == PaddingStrategy.MAX_LENGTH:
																		raise ValueError(
																		    f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )

														# Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
														raise ValueError(
														    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
														    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )

										return padding_strategy










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_		:				Optional[int]									=     {
    "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_		:				Optional[int]									=     AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Any								=create_model(
				    """HTSAT-tiny"""	,     """roberta"""	,     lowerCamelCase	,     precision="""fp32"""	,     device="""cuda:0""" if torch.cuda.is_available() else """cpu"""	,     enable_fusion=lowerCamelCase	,     fusion_type="""aff_2d""" if enable_fusion else None	,     )
				return model, model_cfg




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								={}

				__magic_name__	:	Union[str, Any]								=R""".*sequential.(\d+).*"""
				__magic_name__	:	Union[str, 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:
																__magic_name__	:	Union[str, Any]								=key.replace(lowerCamelCase	,     lowerCamelCase   )

								if re.match(lowerCamelCase	,     lowerCamelCase   ):
												# replace sequential layers with list
												__magic_name__	:	str								=re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )

												__magic_name__	:	Optional[Any]								=key.replace(F"sequential.{sequential_layer}."	,     F"layers.{int(lowerCamelCase   )//3}.linear."   )
								elif re.match(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Any								=int(re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )   )

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

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

												__magic_name__	:	Optional[Any]								=mixed_qkv[:qkv_dim]
												__magic_name__	:	Any								=mixed_qkv[qkv_dim : qkv_dim * 2]
												__magic_name__	:	Optional[Any]								=mixed_qkv[qkv_dim * 2 :]

												__magic_name__	:	str								=query_layer
												__magic_name__	:	int								=key_layer
												__magic_name__	:	str								=value_layer
								else:
												__magic_name__	:	int								=value

				return model_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Tuple								=init_clap(lowerCamelCase	,     enable_fusion=lowerCamelCase   )

				clap_model.eval()
				__magic_name__	:	int								=clap_model.state_dict()
				__magic_name__	:	Optional[Any]								=rename_state_dict(lowerCamelCase   )

				__magic_name__	:	Any								=ClapConfig()
				__magic_name__	:	Optional[Any]								=enable_fusion
				__magic_name__	:	Tuple								=ClapModel(lowerCamelCase   )

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

				model.save_pretrained(lowerCamelCase   )
				transformers_config.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[int]									=     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_		:				Dict									=     parser.parse_args()

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




