





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None					)    ->    str:







			"""simple docstring"""
			assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
			lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(UpperCAmelCase					)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
						lowerCamelCase__       :						Dict        =							nn.Parameter(UpperCAmelCase					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Union[str, Any]:







			"""simple docstring"""
			lowerCamelCase__       :						str        =							np.asarray(weights[0]					)
			lowerCamelCase__       :						Optional[Any]        =							np.asarray(weights[1]					)
			lowerCamelCase__       :						str        =							np.asarray(weights[2]					)

			set_param(
			    torch_layer.self_attention.query_key      ,  torch.tensor(UpperCAmelCase					).transpose(1      ,  2					).contiguous().view(-1      ,  UpperCAmelCase					)      ,  )
			set_param(
			    torch_layer.self_attention.value      ,  torch.tensor(UpperCAmelCase					).transpose(1      ,  2					).contiguous().view(-1      ,  UpperCAmelCase					)      ,  )
			set_param(
			    torch_layer.output.dense      ,  torch.tensor(UpperCAmelCase					).view(-1      ,  UpperCAmelCase					).contiguous().transpose(0      ,  1					)      ,  )



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







			"""simple docstring"""
			lowerCamelCase__       :						Optional[Any]        =							np.asarray(weights[0]					)
			lowerCamelCase__       :						Optional[int]        =							np.asarray(weights[1]					)
			lowerCamelCase__       :						str        =							np.asarray(weights[2]					)
			lowerCamelCase__       :						List[str]        =							np.asarray(weights[3]					)

			set_param(
			    torch_layer.self_attention.query      ,  torch.tensor(UpperCAmelCase					).transpose(1      ,  2					).contiguous().view(-1      ,  UpperCAmelCase					)      ,  )
			set_param(
			    torch_layer.self_attention.key      ,  torch.tensor(UpperCAmelCase					).transpose(1      ,  2					).contiguous().view(-1      ,  UpperCAmelCase					)      ,  )
			set_param(
			    torch_layer.self_attention.value      ,  torch.tensor(UpperCAmelCase					).transpose(1      ,  2					).contiguous().view(-1      ,  UpperCAmelCase					)      ,  )
			set_param(
			    torch_layer.output.dense      ,  torch.tensor(UpperCAmelCase					).view(-1      ,  UpperCAmelCase					).contiguous().transpose(0      ,  1					)      ,  )



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







			"""simple docstring"""
			lowerCamelCase__       :						str        =							weights[0][0][0]
			lowerCamelCase__       :						str        =							np.asarray(layer_norm_a[0]					)
			lowerCamelCase__       :						Any        =							np.asarray(layer_norm_a[1]					)
			set_param(
			    torch_block.attention.layer_norm      ,  torch.tensor(UpperCAmelCase					)      ,  torch.tensor(UpperCAmelCase					)      ,  )

			# lsh weights + output
			lowerCamelCase__       :						Optional[int]        =							weights[0][1]
			if len(UpperCAmelCase					) < 4:
						set_layer_weights_in_torch_lsh(UpperCAmelCase      ,  torch_block.attention      ,  UpperCAmelCase					)
			else:
						set_layer_weights_in_torch_local(UpperCAmelCase      ,  torch_block.attention      ,  UpperCAmelCase					)

			# intermediate weighs
			lowerCamelCase__       :						int        =							weights[2][0][1][2]

			# Chunked Feed Forward
			if len(UpperCAmelCase					) == 4:
						lowerCamelCase__       :						str        =							intermediate_weights[2]

			# layernorm 2
			lowerCamelCase__       :						Tuple        =							np.asarray(intermediate_weights[0][0]					)
			lowerCamelCase__       :						Optional[Any]        =							np.asarray(intermediate_weights[0][1]					)
			set_param(
			    torch_block.feed_forward.layer_norm      ,  torch.tensor(UpperCAmelCase					)      ,  torch.tensor(UpperCAmelCase					)      ,  )

			# intermediate dense
			lowerCamelCase__       :						List[str]        =							np.asarray(intermediate_weights[1][0]					)
			lowerCamelCase__       :						List[Any]        =							np.asarray(intermediate_weights[1][1]					)
			set_param(
			    torch_block.feed_forward.dense.dense      ,  torch.tensor(UpperCAmelCase					).transpose(0      ,  1					).contiguous()      ,  torch.tensor(UpperCAmelCase					)      ,  )

			# intermediate out
			lowerCamelCase__       :						Dict        =							np.asarray(intermediate_weights[4][0]					)
			lowerCamelCase__       :						Tuple        =							np.asarray(intermediate_weights[4][1]					)
			set_param(
			    torch_block.feed_forward.output.dense      ,  torch.tensor(UpperCAmelCase					).transpose(0      ,  1					).contiguous()      ,  torch.tensor(UpperCAmelCase					)      ,  )



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Any:







			"""simple docstring"""
			lowerCamelCase__       :						Union[str, Any]        =							torch_model.reformer

			# word embeds
			lowerCamelCase__       :						int        =							np.asarray(weights[1]					)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings      ,  torch.tensor(UpperCAmelCase					)      ,  )

			if isinstance(weights[3]      ,  UpperCAmelCase					):
						lowerCamelCase__       :						str        =							torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights					)					):
									lowerCamelCase__       :						List[str]        =							np.asarray(weights[3][emb_idx][0]					)
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), f"{position_embeddings[emb_idx]} emb does not match"
									lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.tensor(UpperCAmelCase					)					)

			lowerCamelCase__       :						int        =							weights[5]
			assert len(torch_model_reformer.encoder.layers					) * 4 == len(
			    UpperCAmelCase					), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers					):
						lowerCamelCase__       :						str        =							trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)

			# output layer norm
			lowerCamelCase__       :						Union[str, Any]        =							np.asarray(weights[7][0]					)
			lowerCamelCase__       :						Any        =							np.asarray(weights[7][1]					)
			set_param(
			    torch_model_reformer.encoder.layer_norm      ,  torch.tensor(UpperCAmelCase					)      ,  torch.tensor(UpperCAmelCase					)      ,  )

			# output embeddings
			lowerCamelCase__       :						List[Any]        =							np.asarray(weights[9][0]					)
			lowerCamelCase__       :						Optional[Any]        =							np.asarray(weights[9][1]					)
			set_param(
			    torch_model.lm_head.decoder      ,  torch.tensor(UpperCAmelCase					).transpose(0      ,  1					).contiguous()      ,  torch.tensor(UpperCAmelCase					)      ,  )



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







			"""simple docstring"""
			lowerCamelCase__       :						str        =							ReformerConfig.from_json_file(UpperCAmelCase					)
			print(f"Building PyTorch model from configuration: {config}"					)
			lowerCamelCase__       :						Tuple        =							ReformerModelWithLMHead(UpperCAmelCase					)

			with open(UpperCAmelCase      ,  '''rb'''					) as f:
						lowerCamelCase__       :						Optional[int]        =							pickle.load(UpperCAmelCase					)['''weights''']

			set_model_weights_in_torch(UpperCAmelCase      ,  UpperCAmelCase      ,  config.hidden_size					)

			# Save pytorch-model
			print(f"Save PyTorch model to {pytorch_dump_path}"					)
			torch.save(model.state_dict()      ,  UpperCAmelCase					)


if __name__ == "__main__":
							_A :   Union[str, Any]  			=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained Reformer model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							_A :   str  			=  parser.parse_args()
							convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A :   Optional[Any]  			=  {
    '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 :   Any  			=  AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							create_model(
       '''HTSAT-tiny'''      ,  '''roberta'''      ,  UpperCAmelCase      ,  precision='''fp32'''      ,  device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''      ,  enable_fusion=UpperCAmelCase      ,  fusion_type='''aff_2d''' if enable_fusion else None      ,  )
   return model, model_cfg



def 	_a						(   UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							{}

   lowerCamelCase__       :						int        =							R'''.*sequential.(\d+).*'''
   lowerCamelCase__       :						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:
            lowerCamelCase__       :						List[str]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

      if re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         # replace sequential layers with list
         lowerCamelCase__       :						List[Any]        =							re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)

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

         # Because in CLAP they use `nn.Sequential`...
         lowerCamelCase__       :						str        =							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__       :						Optional[Any]        =							value
         lowerCamelCase__       :						Optional[Any]        =							mixed_qkv.size(0					) // 3

         lowerCamelCase__       :						Tuple        =							mixed_qkv[:qkv_dim]
         lowerCamelCase__       :						Dict        =							mixed_qkv[qkv_dim : qkv_dim * 2]
         lowerCamelCase__       :						int        =							mixed_qkv[qkv_dim * 2 :]

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

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							init_clap(UpperCAmelCase      ,  enable_fusion=UpperCAmelCase					)

   clap_model.eval()
   lowerCamelCase__       :						List[Any]        =							clap_model.state_dict()
   lowerCamelCase__       :						Dict        =							rename_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[int]        =							ClapConfig()
   lowerCamelCase__       :						Optional[int]        =							enable_fusion
   lowerCamelCase__       :						Optional[Any]        =							ClapModel(UpperCAmelCase					)

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

   model.save_pretrained(UpperCAmelCase					)
   transformers_config.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   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')
       _A :   Tuple  			=  parser.parse_args()

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



