






'''simple docstring'''
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__magic_name__		:     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''',
}

__magic_name__		:     Optional[Any]					=    AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def     A__						(	A_    ,      A_=False		)     ->				Optional[Any]:
					_lowercase			= 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     A__						(	A_		)     ->				List[str]:
					_lowercase			= {}

					_lowercase			= R'''.*sequential.(\d+).*'''
					_lowercase			= 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:
																				_lowercase			= key.replace(SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__		)

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

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

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

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

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

															_lowercase			= query_layer
															_lowercase			= key_layer
															_lowercase			= value_layer
										else:
															_lowercase			= value

					return model_state_dict



def     A__						(	A_    ,      A_    ,      A_    ,      A_=False		)     ->				Optional[Any]:
					_lowercase			= init_clap(SCREAMING_SNAKE_CASE__    ,      enable_fusion=SCREAMING_SNAKE_CASE__		)

					clap_model.eval()
					_lowercase			= clap_model.state_dict()
					_lowercase			= rename_state_dict(SCREAMING_SNAKE_CASE__		)

					_lowercase			= ClapConfig()
					_lowercase			= enable_fusion
					_lowercase			= 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__":
	__magic_name__		:     Dict					=    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''')
	__magic_name__		:     Tuple					=    parser.parse_args()

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



'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define glider example
lowercase__						       =      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
lowercase__						       =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							list[list[int]]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						[]
		for i in range(len(SCREAMING_SNAKE_CASE__			)			):
				snake_case  :       str     			=						[]
				for j in range(len(cells[i]			)			):
						# Get the number of live neighbours
						snake_case  :       Any     			=						0
						if i > 0 and j > 0:
								neighbour_count += cells[i - 1][j - 1]
						if i > 0:
								neighbour_count += cells[i - 1][j]
						if i > 0 and j < len(cells[i]			) - 1:
								neighbour_count += cells[i - 1][j + 1]
						if j > 0:
								neighbour_count += cells[i][j - 1]
						if j < len(cells[i]			) - 1:
								neighbour_count += cells[i][j + 1]
						if i < len(SCREAMING_SNAKE_CASE__			) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(SCREAMING_SNAKE_CASE__			) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(SCREAMING_SNAKE_CASE__			) - 1 and j < len(cells[i]			) - 1:
								neighbour_count += cells[i + 1][j + 1]

						# Rules of the game of life (excerpt from Wikipedia):
						# 1. Any live cell with two or three live neighbours survives.
						# 2. Any dead cell with three live neighbours becomes a live cell.
						# 3. All other live cells die in the next generation.
						#    Similarly, all other dead cells stay dead.
						snake_case  :       List[Any]     			=						cells[i][j] == 1
						if (
						    (alive and 2 <= neighbour_count <= 3)
						    or not alive
						    and neighbour_count == 3
						):
								next_generation_row.append(1			)
						else:
								next_generation_row.append(0			)

				next_generation.append(SCREAMING_SNAKE_CASE__			)
		return next_generation




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							list[Image.Image]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						[]
		for _ in range(SCREAMING_SNAKE_CASE__			):
				# Create output image
				snake_case  :       List[Any]     			=						Image.new('''RGB''' ,       (len(cells[0]			), len(SCREAMING_SNAKE_CASE__			))			)
				snake_case  :       Optional[Any]     			=						img.load()

				# Save cells to image
				for x in range(len(SCREAMING_SNAKE_CASE__			)			):
						for y in range(len(cells[0]			)			):
								snake_case  :       str     			=						255 - cells[y][x] * 255
								snake_case  :       List[str]     			=						(colour, colour, colour)

        # Save image
				images.append(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Dict     			=						new_generation(SCREAMING_SNAKE_CASE__			)
		return images


if __name__ == "__main__":
						lowercase__						       =      generate_images(GLIDER, 1_6)
						images[0].save("out.gif", save_all=True, append_images=images[1:])


