



'''simple docstring'''
from manim import *






class 							lowercase__	(							lowercase   ):





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   Rectangle(height=0.5 ,width=0.5 )
							_UpperCamelCase					:		Tuple 				=   Rectangle(height=0.4_6 ,width=0.4_6 ).set_stroke(width=0 )

							_UpperCamelCase					:		Union[str, Any] 				=   [mem.copy() for i in range(6 )]
							_UpperCamelCase					:		Dict 				=   [mem.copy() for i in range(6 )]
							_UpperCamelCase					:		Any 				=   VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0 )
							_UpperCamelCase					:		Tuple 				=   VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0 )
							_UpperCamelCase					:		List[Any] 				=   VGroup(lowerCamelCase__ ,lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0 )
							_UpperCamelCase					:		Tuple 				=   Text('CPU' ,font_size=24 )
							_UpperCamelCase					:		Optional[Any] 				=   Group(lowerCamelCase__ ,lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0.5 ,aligned_edge=lowerCamelCase__ )
							cpu.move_to([-2.5, -0.5, 0] )
							self.add(lowerCamelCase__ )

							_UpperCamelCase					:		Optional[Any] 				=   [mem.copy() for i in range(1 )]
							_UpperCamelCase					:		int 				=   VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0 )
							_UpperCamelCase					:		Any 				=   Text('GPU' ,font_size=24 )
							_UpperCamelCase					:		Dict 				=   Group(lowerCamelCase__ ,lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0.5 ,aligned_edge=lowerCamelCase__ )
							gpu.align_to(lowerCamelCase__ ,lowerCamelCase__ )
							gpu.set_x(gpu.get_x() - 1 )

							self.add(lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   [mem.copy() for i in range(6 )]
							_UpperCamelCase					:		Dict 				=   VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0 )

							_UpperCamelCase					:		Optional[Any] 				=   Text('Model' ,font_size=24 )
							_UpperCamelCase					:		int 				=   Group(lowerCamelCase__ ,lowerCamelCase__ ).arrange(lowerCamelCase__ ,buff=0.5 ,aligned_edge=lowerCamelCase__ )
							model.move_to([3, -1.0, 0] )

							self.play(
							    Create(lowerCamelCase__ ,run_time=1 ) ,Create(lowerCamelCase__ ,run_time=1 ) ,Create(lowerCamelCase__ ,run_time=1 ) ,)

							_UpperCamelCase					:		List[Any] 				=   MarkupText(
							    F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' ,font_size=24 ,)

							_UpperCamelCase					:		int 				=   Square(side_length=2.2 )
							key.move_to([-5, 2, 0] )

							_UpperCamelCase					:		Tuple 				=   MarkupText(
							    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' ,font_size=18 ,)

							key_text.move_to([-5, 2.4, 0] )

							step_a.move_to([2, 2, 0] )
							self.play(Write(lowerCamelCase__ ,run_time=2.5 ) ,Write(lowerCamelCase__ ) ,Write(lowerCamelCase__ ) )

							self.add(lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   []
							_UpperCamelCase					:		Optional[Any] 				=   []
							_UpperCamelCase					:		str 				=   []
							for i, rect in enumerate(lowerCamelCase__ ):
													_UpperCamelCase					:		Any 				=   Rectangle(height=0.4_6 ,width=0.4_6 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase__ ,opacity=0.7 )
													cpu_target.move_to(lowerCamelCase__ )
													cpu_target.generate_target()
													_UpperCamelCase					:		List[Any] 				=   0.4_6 / 4
													_UpperCamelCase					:		Optional[Any] 				=   0.4_6 / 3

													if i == 0:
																			cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.0_2 ,direction=lowerCamelCase__ )
																			cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
													elif i == 3:
																			cpu_target.target.next_to(cpu_targs[0].target ,direction=lowerCamelCase__ ,buff=0.0 )
													else:
																			cpu_target.target.next_to(cpu_targs[i - 1].target ,direction=lowerCamelCase__ ,buff=0.0 )
													cpu_targs.append(lowerCamelCase__ )

													first_animations.append(rect.animate(run_time=0.5 ).set_stroke(lowerCamelCase__ ) )
													second_animations.append(MoveToTarget(lowerCamelCase__ ,run_time=1.5 ) )

							self.play(*lowerCamelCase__ )
							self.play(*lowerCamelCase__ )

							self.wait()




'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase			:				Union[str, 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',
}

lowercase			:				Tuple    		=			AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   Tuple					=						create_model(
        '''HTSAT-tiny'''		,				'''roberta'''		,				snake_case__		,				precision='''fp32'''		,				device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''		,				enable_fusion=snake_case__		,				fusion_type='''aff_2d''' if enable_fusion else None		,				)
    return model, model_cfg







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{}

    A       :   str					=						R'''.*sequential.(\d+).*'''
    A       :   Union[str, 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:
                A       :   Any					=						key.replace(snake_case__		,				snake_case__						)

        if re.match(snake_case__		,				snake_case__						):
            # replace sequential layers with list
            A       :   Any					=						re.match(snake_case__		,				snake_case__						).group(1						)

            A       :   List[str]					=						key.replace(F'sequential.{sequential_layer}.'		,				F'layers.{int(snake_case__						)//3}.linear.'						)
        elif re.match(snake_case__		,				snake_case__						):
            A       :   Union[str, Any]					=						int(re.match(snake_case__		,				snake_case__						).group(1						)						)

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

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

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

            A       :   Tuple					=						query_layer
            A       :   Union[str, Any]					=						key_layer
            A       :   Optional[int]					=						value_layer
        else:
            A       :   Dict					=						value

    return model_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   int					=						init_clap(snake_case__		,				enable_fusion=snake_case__						)

    clap_model.eval()
    A       :   str					=						clap_model.state_dict()
    A       :   Union[str, Any]					=						rename_state_dict(snake_case__						)

    A       :   Tuple					=						ClapConfig()
    A       :   str					=						enable_fusion
    A       :   str					=						ClapModel(snake_case__						)

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

    model.save_pretrained(snake_case__						)
    transformers_config.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			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')
      lowercase			:				Tuple    		=			parser.parse_args()

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




