

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase    				=     {
    '''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    				=     AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False				):

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    create_model(
         "HTSAT-tiny"		,							"roberta"		,							SCREAMING_SNAKE_CASE		,							precision="fp32"		,							device="cuda:0" if torch.cuda.is_available() else "cpu"		,							enable_fusion=SCREAMING_SNAKE_CASE		,							fusion_type="aff_2d" if enable_fusion else None		,							)
     return model, model_cfg







def 	_snake_case		(    SCREAMING_SNAKE_CASE				):

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    {}

     _lowerCAmelCase   :					Tuple        =    r".*sequential.(\d+).*"
     _lowerCAmelCase   :					Dict        =    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   :					Optional[int]        =    key.replace(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

          if re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
               # replace sequential layers with list
               _lowerCAmelCase   :					Union[str, Any]        =    re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				).group(1				)

               _lowerCAmelCase   :					Tuple        =    key.replace(f'''sequential.{sequential_layer}.'''		,							f'''layers.{int(SCREAMING_SNAKE_CASE				)//3}.linear.'''				)
          elif re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
               _lowerCAmelCase   :					int        =    int(re.match(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				).group(1				)				)

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

               _lowerCAmelCase   :					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
               _lowerCAmelCase   :					List[str]        =    value
               _lowerCAmelCase   :					Optional[Any]        =    mixed_qkv.size(0				) // 3

               _lowerCAmelCase   :					Union[str, Any]        =    mixed_qkv[:qkv_dim]
               _lowerCAmelCase   :					int        =    mixed_qkv[qkv_dim : qkv_dim * 2]
               _lowerCAmelCase   :					Optional[Any]        =    mixed_qkv[qkv_dim * 2 :]

               _lowerCAmelCase   :					Tuple        =    query_layer
               _lowerCAmelCase   :					Optional[int]        =    key_layer
               _lowerCAmelCase   :					Dict        =    value_layer
          else:
               _lowerCAmelCase   :					int        =    value

     return model_state_dict







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False				):

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    init_clap(SCREAMING_SNAKE_CASE		,							enable_fusion=SCREAMING_SNAKE_CASE				)

     clap_model.eval()
     _lowerCAmelCase   :					List[Any]        =    clap_model.state_dict()
     _lowerCAmelCase   :					Dict        =    rename_state_dict(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Dict        =    ClapConfig()
     _lowerCAmelCase   :					Tuple        =    enable_fusion
     _lowerCAmelCase   :					List[str]        =    ClapModel(SCREAMING_SNAKE_CASE				)

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

     model.save_pretrained(SCREAMING_SNAKE_CASE				)
     transformers_config.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     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    				=     parser.parse_args()

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









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	List[str]     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					str        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Union[str, Any]        =    do_center_crop
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return resize(
          A_     ,						size=(size["height"], size["width"])     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[str, Any]=None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					int        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					Optional[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					str        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					List[Any]        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					int        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Tuple        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None or resample is None:
           raise ValueError("Size and resample must be specified if do_resize is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Union[str, Any]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					List[str]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Any        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					str        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					int        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )






