

'''simple docstring'''
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase :      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",
}

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Tuple=False				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={}

     _SCREAMING_SNAKE_CASE							    =r'.*sequential.(\d+).*'
     _SCREAMING_SNAKE_CASE							    =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:
                    _SCREAMING_SNAKE_CASE							    =key.replace(_UpperCamelCase							, _UpperCamelCase				)

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

               _SCREAMING_SNAKE_CASE							    =key.replace(f"sequential.{sequential_layer}."							, f"layers.{int(_UpperCamelCase				)//3}.linear."				)
          elif re.match(_UpperCamelCase							, _UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =int(re.match(_UpperCamelCase							, _UpperCamelCase				).group(1				)				)

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

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

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

               _SCREAMING_SNAKE_CASE							    =query_layer
               _SCREAMING_SNAKE_CASE							    =key_layer
               _SCREAMING_SNAKE_CASE							    =value_layer
          else:
               _SCREAMING_SNAKE_CASE							    =value

     return model_state_dict


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]=False				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =init_clap(_UpperCamelCase							, enable_fusion=_UpperCamelCase				)

     clap_model.eval()
     _SCREAMING_SNAKE_CASE							    =clap_model.state_dict()
     _SCREAMING_SNAKE_CASE							    =rename_state_dict(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =ClapConfig()
     _SCREAMING_SNAKE_CASE							    =enable_fusion
     _SCREAMING_SNAKE_CASE							    =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__":
     lowerCamelCase :      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")
     lowerCamelCase :      Union[str, Any]					 =     parser.parse_args()

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



'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image







class 							A__    (       A__  ):
  A__		 =				(
      'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
      'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
      'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
