





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_A :   Tuple  			=  'Create a default config file for Accelerate with only a few flags set.'



def 	_a						(   UpperCAmelCase="no"      ,  UpperCAmelCase = default_json_config_file      ,  UpperCAmelCase = False					)    ->    Any:







			"""simple docstring"""
			lowerCamelCase__       :						List[Any]        =							Path(__a					)
			path.parent.mkdir(parents=__a      ,  exist_ok=__a					)
			if path.exists():
						print(
						    f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."					)
						return False
			lowerCamelCase__       :						List[Any]        =							mixed_precision.lower()
			if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
						raise ValueError(
						    f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"					)
			lowerCamelCase__       :						Optional[int]        =							{
			    '''compute_environment''': '''LOCAL_MACHINE''',
			    '''mixed_precision''': mixed_precision,
			}
			if torch.cuda.is_available():
						lowerCamelCase__       :						Any        =							torch.cuda.device_count()
						lowerCamelCase__       :						List[Any]        =							num_gpus
						lowerCamelCase__       :						Dict        =							False
						if num_gpus > 1:
									lowerCamelCase__       :						str        =							'''MULTI_GPU'''
						else:
									lowerCamelCase__       :						Union[str, Any]        =							'''NO'''
			elif is_xpu_available() and use_xpu:
						lowerCamelCase__       :						str        =							torch.xpu.device_count()
						lowerCamelCase__       :						List[str]        =							num_xpus
						lowerCamelCase__       :						Union[str, Any]        =							False
						if num_xpus > 1:
									lowerCamelCase__       :						Tuple        =							'''MULTI_XPU'''
						else:
									lowerCamelCase__       :						Tuple        =							'''NO'''
			elif is_npu_available():
						lowerCamelCase__       :						Dict        =							torch.npu.device_count()
						lowerCamelCase__       :						Dict        =							num_npus
						lowerCamelCase__       :						Optional[int]        =							False
						if num_npus > 1:
									lowerCamelCase__       :						List[str]        =							'''MULTI_NPU'''
						else:
									lowerCamelCase__       :						int        =							'''NO'''
			else:
						lowerCamelCase__       :						str        =							0
						lowerCamelCase__       :						Optional[Any]        =							True
						lowerCamelCase__       :						int        =							1
						lowerCamelCase__       :						int        =							'''NO'''
			lowerCamelCase__       :						Tuple        =							ClusterConfig(**__a					)
			config.to_json_file(__a					)
			return path



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







			"""simple docstring"""
			lowerCamelCase__       :						List[str]        =							parser.add_parser('''default'''      ,  parents=__a      ,  help=__a      ,  formatter_class=__a					)
			parser.add_argument(
			    '''--config_file'''      ,  default=__a      ,  help=(
			        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
			        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
			        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
			        '''with \'huggingface\'.'''
			    )      ,  dest='''save_location'''      ,  )

			parser.add_argument(
			    '''--mixed_precision'''      ,  choices=['''no''', '''fp16''', '''bf16''']      ,  type=__a      ,  help='''Whether or not to use mixed precision training. '''
			    '''Choose between FP16 and BF16 (bfloat16) training. '''
			    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''      ,  default='''no'''      ,  )
			parser.set_defaults(func=__a					)
			return parser




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







			"""simple docstring"""
			lowerCamelCase__       :						List[Any]        =							write_basic_config(args.mixed_precision      ,  args.save_location					)
			if config_file:
						print(f"accelerate configuration saved at {config_file}"					)










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)



