from __future__ import annotations

lowercase__			:				Optional[Any]		=	list[tuple[int, int]]

lowercase__			:				Optional[Any]		=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowercase__			:				List[str]		=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class    lowercase_  :



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			)						->Optional[Any]:
								lowerCAmelCase			=		pos_x
								lowerCAmelCase			=		pos_y
								lowerCAmelCase			=		(pos_y, pos_x)
								lowerCAmelCase			=		goal_x
								lowerCAmelCase			=		goal_y
								lowerCAmelCase			=		g_cost
								lowerCAmelCase			=		parent
								lowerCAmelCase			=		self.calculate_heuristic()


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->float:
								lowerCAmelCase			=		abs(self.pos_x - self.goal_x				)
								lowerCAmelCase			=		abs(self.pos_y - self.goal_y				)
								return dx + dy




	def __lt__(							self		,			__SCREAMING_SNAKE_CASE				)						->bool:
								return self.f_cost < other.f_cost




class    lowercase_  :



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)						->str:
								lowerCAmelCase			=		Node(start[1]		,			start[0]		,			goal[1]		,			goal[0]		,			0		,			__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		Node(goal[1]		,			goal[0]		,			goal[1]		,			goal[0]		,			99999		,			__SCREAMING_SNAKE_CASE				)

								lowerCAmelCase			=		[self.start]
								lowerCAmelCase			=		[]

								lowerCAmelCase			=		False


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Path | None:






								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															lowerCAmelCase			=		self.open_nodes.pop(0				)

															if current_node.pos == self.target.pos:
																						lowerCAmelCase			=		True
																						return self.retrace_path(__SCREAMING_SNAKE_CASE				)

															self.closed_nodes.append(__SCREAMING_SNAKE_CASE				)
															lowerCAmelCase			=		self.get_successors(__SCREAMING_SNAKE_CASE				)

															for child_node in successors:
																						if child_node in self.closed_nodes:
																													continue

																						if child_node not in self.open_nodes:
																													self.open_nodes.append(__SCREAMING_SNAKE_CASE				)
																						else:
																													# retrieve the best current path
																													lowerCAmelCase			=		self.open_nodes.pop(self.open_nodes.index(__SCREAMING_SNAKE_CASE				)				)

																													if child_node.g_cost < better_node.g_cost:
																																				self.open_nodes.append(__SCREAMING_SNAKE_CASE				)
																													else:
																																				self.open_nodes.append(__SCREAMING_SNAKE_CASE				)

								if not self.reached:
															return [self.start.pos]
								return None


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->list[Node]:
								lowerCAmelCase			=		[]
								for action in delta:
															lowerCAmelCase			=		parent.pos_x + action[1]
															lowerCAmelCase			=		parent.pos_y + action[0]

															if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(__SCREAMING_SNAKE_CASE				) - 1):
																						continue

															if grid[pos_y][pos_x] != 0:
																						continue

															successors.append(
															    Node(
															        __SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			self.target.pos_y		,			self.target.pos_x		,			parent.g_cost + 1		,			__SCREAMING_SNAKE_CASE		,			)				)
								return successors




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Path:
								lowerCAmelCase			=		node
								lowerCAmelCase			=		[]
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)				)
															lowerCAmelCase			=		current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
	lowercase__			:				Tuple		=	(0, 0)
	lowercase__			:				Union[str, Any]		=	(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('''------''')

	lowercase__			:				List[str]		=	GreedyBestFirst(init, goal)
	lowercase__			:				Any		=	greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			lowercase__			:				Optional[Any]		=	2

		for elem in grid:
			print(elem)





'''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)




