"""simple docstring"""







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__SCREAMING_SNAKE_CASE             ={
    "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",
}

__SCREAMING_SNAKE_CASE             =AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Tuple	,     __SCREAMING_SNAKE_CASE				:	List[str]=False ):
     lowercase_      :							Tuple						=      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      lowercase__(			__SCREAMING_SNAKE_CASE				:	Dict ):
     lowercase_      :							Optional[int]						=      {}

     lowercase_      :							Optional[Any]						=      r".*sequential.(\d+).*"
     lowercase_      :							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:
                    lowercase_      :							Tuple						=      key.replace(A__	,     A__ )

          if re.match(A__	,     A__ ):
               # replace sequential layers with list
               lowercase_      :							int						=      re.match(A__	,     A__ ).group(1 )

               lowercase_      :							List[str]						=      key.replace(F'''sequential.{sequential_layer}.'''	,     F'''layers.{int(A__ )//3}.linear.''' )
          elif re.match(A__	,     A__ ):
               lowercase_      :							Any						=      int(re.match(A__	,     A__ ).group(1 ) )

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

               lowercase_      :							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
               lowercase_      :							Union[str, Any]						=      value
               lowercase_      :							Tuple						=      mixed_qkv.size(0 ) // 3

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

               lowercase_      :							Any						=      query_layer
               lowercase_      :							Union[str, Any]						=      key_layer
               lowercase_      :							List[Any]						=      value_layer
          else:
               lowercase_      :							List[Any]						=      value

     return model_state_dict



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]	,     __SCREAMING_SNAKE_CASE				:	Tuple	,     __SCREAMING_SNAKE_CASE				:	Tuple=False ):
     lowercase_      :							Optional[Any]						=      init_clap(A__	,     enable_fusion=A__ )

     clap_model.eval()
     lowercase_      :							Tuple						=      clap_model.state_dict()
     lowercase_      :							Optional[int]						=      rename_state_dict(A__ )

     lowercase_      :							Optional[Any]						=      ClapConfig()
     lowercase_      :							str						=      enable_fusion
     lowercase_      :							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__":
       __SCREAMING_SNAKE_CASE             =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")
       __SCREAMING_SNAKE_CASE             =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 List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, bool]] = "mean"      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					str = "gelu"      ,      lowerCAmelCase_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

				# Transformer architecture configuration
				UpperCAmelCase_	:					Optional[Any]     	=   input_size * len(self.lags_sequence		) + self._number_of_features
				UpperCAmelCase_	:					List[Any]     	=   d_model
				UpperCAmelCase_	:					List[str]     	=   encoder_attention_heads
				UpperCAmelCase_	:					List[str]     	=   decoder_attention_heads
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_ffn_dim
				UpperCAmelCase_	:					Union[str, Any]     	=   decoder_ffn_dim
				UpperCAmelCase_	:					Tuple     	=   encoder_layers
				UpperCAmelCase_	:					List[Any]     	=   decoder_layers

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

				super().__init__(is_encoder_decoder=lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				return (
				    sum(self.embedding_dimension		)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)



