
"""simple docstring"""

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_="attention"      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
		lowerCamelCase					:      Dict          =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
		lowerCamelCase					:      Union[str, Any]          =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
		lowerCamelCase					:      Union[str, Any]          =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
		return k, o, q, v
def 		UpperCAmelCase (		a_,						a_,						a_,						a_=False      ):





		'''simple docstring'''


		if split_mlp_wi:
				lowerCamelCase					:      Union[str, Any]          =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
				lowerCamelCase					:      Optional[int]          =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
				lowerCamelCase					:      Union[str, Any]          =   (wi_a, wi_a)
		else:
				lowerCamelCase					:      Optional[Any]          =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

		lowerCamelCase					:      Optional[int]          =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
		return wi, wo
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]
def 		UpperCAmelCase (		a_,						*, a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   traverse_util.flatten_dict(variables['target']      )
		lowerCamelCase					:      Any          =   {'/'.join(a_      ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		lowerCamelCase					:      Union[str, Any]          =   'encoder/layers_0/mlp/wi_0/kernel' in old
		print('Split MLP:',						a_      )

		lowerCamelCase					:      List[Any]          =   collections.OrderedDict()

		# Shared embeddings.
		lowerCamelCase					:      List[str]          =   old['token_embedder/embedding']

		# Encoder.
		for i in range(a_      ):
				# Block i, layer 0 (Self Attention).
				lowerCamelCase					:      Optional[int]          =   tax_layer_norm_lookup(a_,						a_,						'encoder',						'pre_attention_layer_norm'      )
				lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[Any]          =   tax_attention_lookup(a_,						a_,						'encoder',						'attention'      )
				lowerCamelCase					:      Optional[int]          =   layer_norm
				lowerCamelCase					:      Union[str, Any]          =   k.T
				lowerCamelCase					:      Optional[Any]          =   o.T
				lowerCamelCase					:      Optional[Any]          =   q.T
				lowerCamelCase					:      List[Any]          =   v.T

				# Block i, layer 1 (MLP).
				lowerCamelCase					:      Optional[Any]          =   tax_layer_norm_lookup(a_,						a_,						'encoder',						'pre_mlp_layer_norm'      )
				lowerCamelCase     ,   lowerCamelCase					:      Dict          =   tax_mlp_lookup(a_,						a_,						'encoder',						a_      )
				lowerCamelCase					:      Union[str, Any]          =   layer_norm
				if split_mlp_wi:
						lowerCamelCase					:      Dict          =   wi[0].T
						lowerCamelCase					:      Union[str, Any]          =   wi[1].T
				else:
						lowerCamelCase					:      List[Any]          =   wi.T
				lowerCamelCase					:      Dict          =   wo.T

		lowerCamelCase					:      int          =   old[
		    'encoder/relpos_bias/rel_embedding'
		].T
		lowerCamelCase					:      Dict          =   old['encoder/encoder_norm/scale']

		if not is_encoder_only:
				# Decoder.
				for i in range(a_      ):
						# Block i, layer 0 (Self Attention).
						lowerCamelCase					:      Optional[int]          =   tax_layer_norm_lookup(a_,						a_,						'decoder',						'pre_self_attention_layer_norm'      )
						lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Any          =   tax_attention_lookup(a_,						a_,						'decoder',						'self_attention'      )
						lowerCamelCase					:      Optional[int]          =   layer_norm
						lowerCamelCase					:      Tuple          =   k.T
						lowerCamelCase					:      Optional[int]          =   o.T
						lowerCamelCase					:      Tuple          =   q.T
						lowerCamelCase					:      List[str]          =   v.T

						# Block i, layer 1 (Cross Attention).
						lowerCamelCase					:      Dict          =   tax_layer_norm_lookup(a_,						a_,						'decoder',						'pre_cross_attention_layer_norm'      )
						lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      Dict          =   tax_attention_lookup(a_,						a_,						'decoder',						'encoder_decoder_attention'      )
						lowerCamelCase					:      List[str]          =   layer_norm
						lowerCamelCase					:      str          =   k.T
						lowerCamelCase					:      Dict          =   o.T
						lowerCamelCase					:      int          =   q.T
						lowerCamelCase					:      List[Any]          =   v.T

						# Block i, layer 2 (MLP).
						lowerCamelCase					:      List[str]          =   tax_layer_norm_lookup(a_,						a_,						'decoder',						'pre_mlp_layer_norm'      )
						lowerCamelCase     ,   lowerCamelCase					:      Any          =   tax_mlp_lookup(a_,						a_,						'decoder',						a_      )
						lowerCamelCase					:      Union[str, Any]          =   layer_norm
						if split_mlp_wi:
								lowerCamelCase					:      Any          =   wi[0].T
								lowerCamelCase					:      List[Any]          =   wi[1].T
						else:
								lowerCamelCase					:      List[str]          =   wi.T
						lowerCamelCase					:      str          =   wo.T

				lowerCamelCase					:      str          =   old['decoder/decoder_norm/scale']
				lowerCamelCase					:      Tuple          =   old[
				    'decoder/relpos_bias/rel_embedding'
				].T

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						lowerCamelCase					:      Tuple          =   old['decoder/logits_dense/kernel'].T

		return new
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				lowerCamelCase					:      Optional[int]          =   state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						lowerCamelCase					:      Any          =   state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'      )
						lowerCamelCase					:      str          =   state_dict['shared.weight']

		return state_dict
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   checkpoints.load_tax_checkpoint(a_      )
		lowerCamelCase					:      Union[str, Any]          =   convert_tax_to_pytorch(a_,						num_layers=config.num_layers,						is_encoder_only=a_      )
		lowerCamelCase					:      List[str]          =   make_state_dict(a_,						a_      )
		model.load_state_dict(a_,						strict=a_      )
def 		UpperCAmelCase (		a_,						a_,						a_,						a_ = False      ):





		'''simple docstring'''


		lowerCamelCase					:      List[str]          =   TaConfig.from_json_file(a_      )
		print(F"""Building PyTorch model from configuration: {config}"""      )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				lowerCamelCase					:      List[Any]          =   TaEncoderModel(a_      )
		else:
				lowerCamelCase					:      Optional[int]          =   TaForConditionalGeneration(a_      )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(a_,						a_,						a_,						a_      )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
		model.save_pretrained(a_      )

		# Verify that we can load the checkpoint.
		model.from_pretrained(a_      )
		print('Done'      )


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
					# Required parameters
					parser.add_argument(
					    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
					)
					_A						=			parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)








"""simple docstring"""

from __future__ import annotations

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

		lowerCamelCase					:      List[str]          =   init[0]
		lowerCamelCase					:      Optional[Any]          =   init[1]
		lowerCamelCase					:      List[Any]          =   0
		lowerCamelCase					:      List[str]          =   g + heuristic[x][y]  # cost from starting cell to destination cell
		lowerCamelCase					:      Union[str, Any]          =   [[f, g, x, y]]

		lowerCamelCase					:      Union[str, Any]          =   False  # flag that is set when search is complete
		lowerCamelCase					:      str          =   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()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

						if x == goal[0] and y == goal[1]:
								lowerCamelCase					:      Any          =   True
						else:
								for i in range(len(a_      )      ):  # to try out different valid actions
										lowerCamelCase					:      Tuple          =   x + DIRECTIONS[i][0]
										lowerCamelCase					:      Union[str, Any]          =   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:
														lowerCamelCase					:      str          =   g + cost
														lowerCamelCase					:      Tuple          =   ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]      )
														lowerCamelCase					:      Union[str, Any]          =   1
														lowerCamelCase					:      Any          =   i
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Optional[int]          =   goal[0]
		lowerCamelCase					:      Dict          =   goal[1]
		invpath.append([x, y]      )  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				lowerCamelCase					:      Dict          =   x - DIRECTIONS[action[x][y]][0]
				lowerCamelCase					:      Dict          =   y - DIRECTIONS[action[x][y]][1]
				lowerCamelCase					:      Optional[Any]          =   xa
				lowerCamelCase					:      Union[str, Any]          =   ya
				invpath.append([x, y]      )

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [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],
					]

					_A						=			[0, 0]
					# all coordinates are given in format [y,x]
					_A						=			[len(grid) - 1, len(grid[0]) - 1]
					_A						=			1

					# the cost map which pushes the path closer to the goal
					_A						=			[[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])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			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])






