



"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class       UpperCamelCase__   (	UpperCAmelCase__  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ["image_processor", "tokenizer"]
      _SCREAMING_SNAKE_CASE  				=     "BlipImageProcessor"
      _SCREAMING_SNAKE_CASE  				=     "AutoTokenizer"



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				Optional[int]								= False
        super().__init__(lowerCamelCase__    ,			lowerCamelCase__       )
        lowerCAmelCase_   :				Tuple								= self.image_processor



      def __call__(      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      ImageInput = None    ,			SCREAMING_SNAKE_CASE_				:      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Union[bool, str, PaddingStrategy] = False    ,			SCREAMING_SNAKE_CASE_				:      Union[bool, str, TruncationStrategy] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, TensorType]] = None    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):







        if images is None and text is None:
          raise ValueError('You have to specify either images or text.'       )

        # Get only text
        if images is None:
          lowerCAmelCase_   :				List[str]								= self.tokenizer
          lowerCAmelCase_   :				int								= self.tokenizer(
              text=lowerCamelCase__    ,			add_special_tokens=lowerCamelCase__    ,			padding=lowerCamelCase__    ,			truncation=lowerCamelCase__    ,			max_length=lowerCamelCase__    ,			stride=lowerCamelCase__    ,			pad_to_multiple_of=lowerCamelCase__    ,			return_attention_mask=lowerCamelCase__    ,			return_overflowing_tokens=lowerCamelCase__    ,			return_special_tokens_mask=lowerCamelCase__    ,			return_offsets_mapping=lowerCamelCase__    ,			return_token_type_ids=lowerCamelCase__    ,			return_length=lowerCamelCase__    ,			verbose=lowerCamelCase__    ,			return_tensors=lowerCamelCase__    ,			**lowerCamelCase__    ,			)
          return text_encoding

        # add pixel_values
        lowerCAmelCase_   :				Union[str, Any]								= self.image_processor(lowerCamelCase__    ,			return_tensors=lowerCamelCase__       )

        if text is not None:
          lowerCAmelCase_   :				int								= self.tokenizer(
              text=lowerCamelCase__    ,			add_special_tokens=lowerCamelCase__    ,			padding=lowerCamelCase__    ,			truncation=lowerCamelCase__    ,			max_length=lowerCamelCase__    ,			stride=lowerCamelCase__    ,			pad_to_multiple_of=lowerCamelCase__    ,			return_attention_mask=lowerCamelCase__    ,			return_overflowing_tokens=lowerCamelCase__    ,			return_special_tokens_mask=lowerCamelCase__    ,			return_offsets_mapping=lowerCamelCase__    ,			return_token_type_ids=lowerCamelCase__    ,			return_length=lowerCamelCase__    ,			verbose=lowerCamelCase__    ,			return_tensors=lowerCamelCase__    ,			**lowerCamelCase__    ,			)
        else:
          lowerCAmelCase_   :				Optional[int]								= None

        if text_encoding is not None:
          encoding_image_processor.update(lowerCamelCase__       )

        return encoding_image_processor



      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        return self.tokenizer.batch_decode(*lowerCamelCase__    ,			**lowerCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      List[str]       ):
        return self.tokenizer.decode(*lowerCamelCase__    ,			**lowerCamelCase__       )





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				List[str]								= self.tokenizer.model_input_names
        lowerCAmelCase_   :				List[Any]								= self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )









'''simple docstring'''


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[Any]	    =					create_model(
		    "HTSAT-tiny"					,       "roberta"					,       __lowerCAmelCase					,       precision="fp32"					,       device="cuda:0" if torch.cuda.is_available() else "cpu"					,       enable_fusion=__lowerCAmelCase					,       fusion_type="aff_2d" if enable_fusion else None					,       )
		return model, model_cfg






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					{}

		_UpperCAmelCase						:				str	    =					R".*sequential.(\d+).*"
		_UpperCAmelCase						:				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:
								_UpperCAmelCase						:				Union[str, Any]	    =					key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

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

						_UpperCAmelCase						:				Optional[int]	    =					key.replace(F"""sequential.{sequential_layer}."""					,       F"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
				elif re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					int(re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )      )

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

						_UpperCAmelCase						:				Tuple	    =					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
						_UpperCAmelCase						:				List[str]	    =					value
						_UpperCAmelCase						:				Tuple	    =					mixed_qkv.size(0      ) // 3

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

						_UpperCAmelCase						:				List[Any]	    =					query_layer
						_UpperCAmelCase						:				int	    =					key_layer
						_UpperCAmelCase						:				Any	    =					value_layer
				else:
						_UpperCAmelCase						:				Dict	    =					value

		return model_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					init_clap(__lowerCAmelCase					,       enable_fusion=__lowerCAmelCase      )

		clap_model.eval()
		_UpperCAmelCase						:				List[str]	    =					clap_model.state_dict()
		_UpperCAmelCase						:				str	    =					rename_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					ClapConfig()
		_UpperCAmelCase						:				str	    =					enable_fusion
		_UpperCAmelCase						:				Union[str, Any]	    =					ClapModel(__lowerCAmelCase      )

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

		model.save_pretrained(__lowerCAmelCase      )
		transformers_config.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
						lowerCamelCase__					=   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__					=   parser.parse_args()

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



