



"""simple docstring"""




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_lowerCAmelCase							:   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",
}

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		List[str]=False							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       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__      :		Optional[Any]							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       {}

     _UpperCAmelCase     :      Any														=       R".*sequential.(\d+).*"
     _UpperCAmelCase     :      str														=       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     :      Any														=       key.replace(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

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

               _UpperCAmelCase     :      Any														=       key.replace(f'sequential.{sequential_layer}.'		,    f'layers.{int(SCREAMING_SNAKE_CASE__							)//3}.linear.'							)
          elif re.match(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
               _UpperCAmelCase     :      Dict														=       int(re.match(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							).group(1							)							)

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

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

               _UpperCAmelCase     :      List[Any]														=       mixed_qkv[:qkv_dim]
               _UpperCAmelCase     :      Optional[Any]														=       mixed_qkv[qkv_dim : qkv_dim * 2]
               _UpperCAmelCase     :      Tuple														=       mixed_qkv[qkv_dim * 2 :]

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

     return model_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		List[str]=False							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       init_clap(SCREAMING_SNAKE_CASE__		,    enable_fusion=SCREAMING_SNAKE_CASE__							)

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

     _UpperCAmelCase     :      int														=       ClapConfig()
     _UpperCAmelCase     :      Tuple														=       enable_fusion
     _UpperCAmelCase     :      int														=       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__":
 _lowerCAmelCase							:   Optional[Any]				=				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							:   str				=				parser.parse_args()

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





"""simple docstring"""




from manim import *






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      str														=       Rectangle(height=0.5	,      width=0.5		)
           _UpperCAmelCase     :      List[Any]														=       Rectangle(height=0.25	,      width=0.25		)
           _UpperCAmelCase     :      Tuple														=       Rectangle(height=0.46	,      width=0.46		).set_stroke(width=0		)

           _UpperCAmelCase     :      List[Any]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      int														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Dict														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      int														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      str														=       Text("CPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Union[str, Any]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           cpu.move_to([-2.5, -0.5, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       [mem.copy() for i in range(4		)]
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Any														=       Text("GPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Dict														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           gpu.move_to([-1, -1, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Tuple														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      str														=       VGroup(*A		).arrange(A	,      buff=0		)

           _UpperCAmelCase     :      Any														=       Text("Model"	,      font_size=2_4		)
           _UpperCAmelCase     :      Any														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           model.move_to([3, -1.0, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      List[str]														=       []
           _UpperCAmelCase     :      Optional[int]														=       []
           _UpperCAmelCase     :      str														=       []

           for i, rect in enumerate(A		):
                rect.set_stroke(A		)

                _UpperCAmelCase     :      Dict														=       Rectangle(height=0.46 / 4	,      width=0.46 / 3		).set_stroke(width=0.0		).set_fill(A	,      opacity=0.7		)

                if i == 0:
                     cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)	,      buff=0.02	,      direction=A		)
                     cpu_target.set_x(cpu_target.get_x() + 0.1		)
                elif i == 3:
                     cpu_target.next_to(model_cpu_arr[0]	,      direction=A	,      buff=0.0		)
                else:
                     cpu_target.next_to(model_cpu_arr[i - 1]	,      direction=A	,      buff=0.0		)
                self.add(A		)
                model_cpu_arr.append(A		)

           self.add(*A	,      *A	,      *A		)

           _UpperCAmelCase     :      Dict														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       VGroup(*A		).arrange(A	,      buff=0		)

           _UpperCAmelCase     :      List[str]														=       Text("Loaded Checkpoint"	,      font_size=2_4		)
           _UpperCAmelCase     :      Optional[int]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           checkpoint.move_to([3, 0.5, 0]		)

           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       []
           _UpperCAmelCase     :      Union[str, Any]														=       []

           for i, rect in enumerate(A		):
                _UpperCAmelCase     :      str														=       fill.copy().set_fill(A	,      opacity=0.7		)
                target.move_to(A		)
                ckpt_arr.append(A		)

                _UpperCAmelCase     :      Dict														=       target.copy()
                if i < 5:
                     cpu_target.move_to(cpu_left_col_base[i + 1]		)
                else:
                     cpu_target.move_to(cpu_right_col_base[i - 5]		)
                ckpt_cpu_arr.append(A		)
           self.add(*A	,      *A		)

           _UpperCAmelCase     :      List[str]														=       Square(side_length=2.2		)
           key.move_to([-5, 2, 0]		)

           _UpperCAmelCase     :      List[Any]														=       MarkupText(
               f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'	,      font_size=1_8	,      )

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

           self.add(A	,      A		)

           _UpperCAmelCase     :      Tuple														=       MarkupText(
               f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'	,      font_size=1_8	,      )

           blue_text.next_to(A	,      DOWN * 2.4	,      aligned_edge=key_text.get_left()		)
           self.add(A		)

           _UpperCAmelCase     :      Any														=       MarkupText(
               f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'	,      font_size=2_4	,      )
           step_a.move_to([2, 2, 0]		)

           _UpperCAmelCase     :      int														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      Optional[int]														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      List[Any]														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Union[str, Any]														=       Text("Disk"	,      font_size=2_4		)
           _UpperCAmelCase     :      int														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           disk.move_to([-4.0, -1.25, 0]		)
           self.play(Write(A	,      run_time=3		)	,      Write(A	,      run_time=1		)	,      Create(A	,      run_time=1		)		)

           _UpperCAmelCase     :      List[Any]														=       []
           for i, rect in enumerate(A		):
                _UpperCAmelCase     :      Any														=       rect.copy()
                target.generate_target()
                target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
                animations.append(MoveToTarget(A	,      run_time=1.5		)		)
           self.play(*A		)

           self.play(FadeOut(A		)		)

           _UpperCAmelCase     :      int														=       MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.'	,      font_size=2_4		)
           step_a.move_to([2, 2, 0]		)

           self.play(Write(A	,      run_time=3		)		)

           self.play(
               FadeOut(A	,      A	,      *A	,      *A		)	,      )

           self.wait()
