




'''simple docstring'''







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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





def 		snake_case_							(_a			:							Any					,     _a			:							Any=False  ):
    UpperCAmelCase ,  UpperCAmelCase     =	create_model(
        '''HTSAT-tiny'''					,     '''roberta'''					,     _a					,     precision='''fp32'''					,     device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''					,     enable_fusion=_a					,     fusion_type='''aff_2d''' if enable_fusion else None					,     )
    return model, model_cfg





def 		snake_case_							(_a			:							int  ):
    UpperCAmelCase     =	{}

    UpperCAmelCase     =	R'''.*sequential.(\d+).*'''
    UpperCAmelCase     =	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     =	key.replace(_a					,     _a  )

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

            UpperCAmelCase     =	key.replace(F"sequential.{sequential_layer}."					,     F"layers.{int(_a  )//3}.linear."  )
        elif re.match(_a					,     _a  ):
            UpperCAmelCase     =	int(re.match(_a					,     _a  ).group(1  )  )

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

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

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

            UpperCAmelCase     =	query_layer
            UpperCAmelCase     =	key_layer
            UpperCAmelCase     =	value_layer
        else:
            UpperCAmelCase     =	value

    return model_state_dict





def 		snake_case_							(_a			:							Optional[int]					,     _a			:							Optional[int]					,     _a			:							Optional[Any]					,     _a			:							Union[str, Any]=False  ):
    UpperCAmelCase ,  UpperCAmelCase     =	init_clap(_a					,     enable_fusion=_a  )

    clap_model.eval()
    UpperCAmelCase     =	clap_model.state_dict()
    UpperCAmelCase     =	rename_state_dict(_a  )

    UpperCAmelCase     =	ClapConfig()
    UpperCAmelCase     =	enable_fusion
    UpperCAmelCase     =	ClapModel(_a  )

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

    model.save_pretrained(_a  )
    transformers_config.save_pretrained(_a  )


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

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






'''simple docstring'''







from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               UpperCAmelCase     =	self.model.config.num_labels

           if self.framework == "pt":
               UpperCAmelCase     =	model_outputs.logits.sigmoid()[0]
               UpperCAmelCase ,  UpperCAmelCase     =	probs.topk(lowercase       )
           else:
               raise ValueError(f"Unsupported framework: {self.framework}"       )

           UpperCAmelCase     =	scores.tolist()
           UpperCAmelCase     =	ids.tolist()
           return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase     ,       lowercase       )]




