


'''simple docstring'''




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__snake_case    						=[
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
__snake_case    						=[
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

__snake_case    						=(
    INIT_COMMON
    + [
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.out_proj"),
        ("attention.self", "self_attn"),
        ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
        ("attention.encdec_output.dense", "encoder_attn.out_proj"),
        ("attention.encdec", "encoder_attn"),
        ("key", "k_proj"),
        ("value", "v_proj"),
        ("query", "q_proj"),
        ("decoder.LayerNorm", "decoder.layernorm_embedding"),
    ]
    + END_COMMON
)

__snake_case    						=(
    INIT_COMMON
    + [
        ("embeddings.word_embeddings", "shared.weight"),
        ("embeddings.position_embeddings", "embed_positions.weight"),
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.output"),
        ("attention.self", "self_attn.self"),
        ("encoder.LayerNorm", "encoder.layernorm_embedding"),
    ]
    + END_COMMON
)

__snake_case    						=[
    "encdec/key/bias",
    "encdec/query/bias",
    "encdec/value/bias",
    "self/key/bias",
    "self/query/bias",
    "self/value/bias",
    "encdec_output/dense/bias",
    "attention/output/dense/bias",
]





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     List[str]				):





   for tf_name, hf_name in patterns:
      lowerCAmelCase     =			k.replace(_A	, _A				)
   return k





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Any				):
   lowerCAmelCase     =			BigBirdPegasusConfig(**_A				)
   lowerCAmelCase     =			BigBirdPegasusForConditionalGeneration(_A				)
   lowerCAmelCase     =			torch_model.state_dict()
   lowerCAmelCase     =			{}

   # separating decoder weights
   lowerCAmelCase     =			{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'				)}
   lowerCAmelCase     =			{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'				)}

   for k, v in tqdm(decoder_weights.items()	, 'tf -> hf conversion'				):
      lowerCAmelCase     =			[k.endswith(_A				) for ending in KEYS_TO_IGNORE]
      if any(_A				):
         continue
      lowerCAmelCase     =			DECODER_PATTERNS
      lowerCAmelCase     =			rename_state_dict_key(_A	, _A				)
      if new_k not in state_dict:
         raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''				)
      if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']				):
         lowerCAmelCase     =			v.T
      lowerCAmelCase     =			torch.from_numpy(_A				)
      assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

   for k, v in tqdm(remaining_weights.items()	, 'tf -> hf conversion'				):
      lowerCAmelCase     =			[k.endswith(_A				) for ending in KEYS_TO_IGNORE]
      if any(_A				):
         continue
      lowerCAmelCase     =			REMAINING_PATTERNS
      lowerCAmelCase     =			rename_state_dict_key(_A	, _A				)
      if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
         raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''				)
      if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']				):
         lowerCAmelCase     =			v.T
      lowerCAmelCase     =			torch.from_numpy(_A				)
      if k != "pegasus/embeddings/position_embeddings":
         assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

   lowerCAmelCase     =			mapping['model.embed_positions.weight']
   lowerCAmelCase     =			mapping.pop('model.embed_positions.weight'				)
   lowerCAmelCase     ,  lowerCAmelCase     =			torch_model.load_state_dict(_A	, strict=_A				)
   lowerCAmelCase     =			[
       k
       for k in missing
       if k
       not in [
           'final_logits_bias',
           'model.encoder.embed_tokens.weight',
           'model.decoder.embed_tokens.weight',
           'lm_head.weight',
       ]
   ]
   assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
   assert extra == [], f'''no matches found for the following tf keys {extra}'''
   return torch_model





def 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			tf.train.list_variables(_A				)
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			['global_step']
   for name, shape in tqdm(_A	, desc='converting tf checkpoint to dict'				):
      lowerCAmelCase     =			any(pat in name for pat in ignore_name				)
      if skip_key:
         continue
      lowerCAmelCase     =			tf.train.load_variable(_A	, _A				)
      lowerCAmelCase     =			array
   return tf_weights





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     str	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			get_tf_weights_as_numpy(_A				)
   lowerCAmelCase     =			convert_bigbird_pegasus(_A	, _A				)
   torch_model.save_pretrained(_A				)


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()
       parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
       parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
       __snake_case    						=parser.parse_args()
       __snake_case    						={}
       convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)











from __future__ import annotations

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


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	):







	'''simple docstring'''





	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the reference grid
	snake_case_		    =     1
	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the action grid

	snake_case_		    =     init[0]
	snake_case_		    =     init[1]
	snake_case_		    =     0
	snake_case_		    =     g + heuristic[x][y]  # cost from starting cell to destination cell
	snake_case_		    =     [[f, g, x, y]]

	snake_case_		    =     False  # flag that is set when search is complete
	snake_case_		    =     False  # flag set if we can't find expand

	while not found and not resign:
		if len(_A							) == 0:
			raise ValueError("Algorithm is unable to find solution"							)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			snake_case_		    =     cell.pop()
			snake_case_		    =     next_cell[2]
			snake_case_		    =     next_cell[3]
			snake_case_		    =     next_cell[1]

			if x == goal[0] and y == goal[1]:
				snake_case_		    =     True
			else:
				for i in range(len(_A							)							):  # to try out different valid actions
					snake_case_		    =     x + DIRECTIONS[i][0]
					snake_case_		    =     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(_A							) and ya >= 0 and ya < len(grid[0]							):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							snake_case_		    =     g + cost
							snake_case_		    =     ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]							)
							snake_case_		    =     1
							snake_case_		    =     i
	snake_case_		    =     []
	snake_case_		    =     goal[0]
	snake_case_		    =     goal[1]
	invpath.append([x, y]							)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		snake_case_		    =     x - DIRECTIONS[action[x][y]][0]
		snake_case_		    =     y - DIRECTIONS[action[x][y]][1]
		snake_case_		    =     xa
		snake_case_		    =     ya
		invpath.append([x, y]							)

	snake_case_		    =     []
	for i in range(len(_A							)							):
		path.append(invpath[len(_A							) - 1 - i]							)
	return path, action


if __name__ == "__main__":
		lowercase__				:     Union[str, Any] 		=						[
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 1, 0],
		    [0, 0, 0, 0, 1, 0],
		]

		lowercase__				:     Optional[Any] 		=						[0, 0]
		# all coordinates are given in format [y,x]
		lowercase__				:     Tuple 		=						[len(grid) - 1, len(grid[0]) - 1]
		lowercase__				:     Dict 		=						1

		# the cost map which pushes the path closer to the goal
		lowercase__				:     Any 		=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
		for i in range(len(grid)):
				for j in range(len(grid[0])):
						lowercase__				:     Union[str, Any] 		=						abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								lowercase__				:     int 		=						99

		lowercase__		,       lowercase__				:     Tuple 		=						search(grid, init, goal, cost, heuristic)

		print("ACTION MAP")
		for i in range(len(action)):
				print(action[i])

		for i in range(len(path)):
				print(path[i])
