





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCamelCase__   : Union[str, Any]					  = 2







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  str ,					*,  # begin keyword-only arguments
					    __lowercase    :  List[str]="<s>" ,					__lowercase    :  List[Any]="<pad>" ,					__lowercase    :  Tuple="</s>" ,					__lowercase    :  Dict="<unk>" ,					__lowercase    :  List[str]=None ,					):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     bos, unk, pad, eos
						UpperCAmelCase_								=     []
						UpperCAmelCase_								=     []
						UpperCAmelCase_								=     {}
						UpperCAmelCase_								=     self.add_symbol(__lowercase		)
						UpperCAmelCase_								=     self.add_symbol(__lowercase		)
						UpperCAmelCase_								=     self.add_symbol(__lowercase		)
						UpperCAmelCase_								=     self.add_symbol(__lowercase		)
						if extra_special_symbols:
							for s in extra_special_symbols:
								self.add_symbol(__lowercase		)
						UpperCAmelCase_								=     len(self.symbols		)





					def __eq__(						self    :  List[Any] ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return self.indices == other.indices





					def __getitem__(						self    :  int ,					__lowercase    :  Dict		):






						'''simple docstring'''







						if idx < len(self.symbols		):
							return self.symbols[idx]
						return self.unk_word





					def __len__(						self    :  Union[str, Any]		):






						'''simple docstring'''







						return len(self.symbols		)





					def __contains__(						self    :  Optional[Any] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						return sym in self.indices





					@classmethod
					def        SCREAMING_SNAKE_CASE							(						cls    :  List[Any] ,					__lowercase    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     cls()
						d.add_from_file(__lowercase		)
						return d





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  int=1 ,					__lowercase    :  Tuple=False		):






						'''simple docstring'''







						if word in self.indices and not overwrite:
							UpperCAmelCase_								=     self.indices[word]
							UpperCAmelCase_								=     self.count[idx] + n
							return idx
						else:
							UpperCAmelCase_								=     len(self.symbols		)
							UpperCAmelCase_								=     idx
							self.symbols.append(__lowercase		)
							self.count.append(__lowercase		)
							return idx





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  str		):






						'''simple docstring'''







						return 0





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						if isinstance(__lowercase ,					__lowercase		):
							try:
								with open(__lowercase ,					"""r""" ,					encoding="""utf-8"""		) as fd:
									self.add_from_file(__lowercase		)
							except FileNotFoundError as fnfe:
								raise fnfe
							except UnicodeError:
								raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(__lowercase		)		)
							return

						UpperCAmelCase_								=     f.readlines()
						UpperCAmelCase_								=     self._load_meta(__lowercase		)

						for line in lines[indices_start_line:]:
							try:
								UpperCAmelCase_						,	UpperCAmelCase_								=     line.rstrip().rsplit(""" """ ,					1		)
								if field == "#fairseq:overwrite":
									UpperCAmelCase_								=     True
									UpperCAmelCase_						,	UpperCAmelCase_								=     line.rsplit(""" """ ,					1		)
								else:
									UpperCAmelCase_								=     False
								UpperCAmelCase_								=     int(__lowercase		)
								UpperCAmelCase_								=     line
								if word in self and not overwrite:
									raise RuntimeError(
									    """Duplicate word found when loading Dictionary: '{}'. """
									    """Duplicate words can overwrite earlier ones by adding the """
									    """#fairseq:overwrite flag at the end of the corresponding row """
									    """in the dictionary file. If using the Camembert model, please """
									    """download an updated copy of the model file.""".format(__lowercase		)		)
								self.add_symbol(__lowercase ,					n=__lowercase ,					overwrite=__lowercase		)
							except ValueError:
								raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""		)




def    A_(			A   ):
	# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
	# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
	UpperCAmelCase_								=     dict((re.sub(R"""@@$"""				,      """"""				,      A   ), v) if k.endswith("""@@"""   ) else (re.sub(R"""$"""				,      """</w>"""				,      A   ), v) for k, v in d.items()   )
	UpperCAmelCase_								=     """<s> <pad> </s> <unk>""".split()
	# restore the special tokens
	for k in keep_keys:
		del da[f"""{k}</w>"""]
		UpperCAmelCase_								=     d[k]  # restore
	return da




def    A_(			A				,      A   ):
	# prep
	if not os.path.exists(A   ):
		raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""   )
	os.makedirs(A				,      exist_ok=A   )
	print(f"""Writing results to {pytorch_dump_folder_path}"""   )

	# handle various types of models

	UpperCAmelCase_								=     os.path.join(A				,      """checkpoint.pt"""   )
	if not os.path.isfile(A   ):
		raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""   )
	UpperCAmelCase_								=     torch.load(A				,      map_location="""cpu"""   )

	UpperCAmelCase_								=     chkpt["""cfg"""]["""model"""]

	# dicts
	UpperCAmelCase_								=     os.path.join(A				,      """dict.txt"""   )
	if not os.path.isfile(A   ):
		raise ValueError(f"""path to the file {dict_file} does not exist!"""   )
	UpperCAmelCase_								=     Dictionary.load(A   )
	UpperCAmelCase_								=     rewrite_dict_keys(src_dict.indices   )
	UpperCAmelCase_								=     len(A   )
	UpperCAmelCase_								=     os.path.join(A				,      VOCAB_FILES_NAMES["""vocab_file"""]   )
	print(f"""Generating {src_vocab_file} of {src_vocab_size} records"""   )
	with open(A				,      """w"""				,      encoding="""utf-8"""   ) as f:
		f.write(json.dumps(A				,      ensure_ascii=A				,      indent=A   )   )

	# merges_file (bpecodes)
	UpperCAmelCase_								=     os.path.join(A				,      """bpecodes"""   )
	if not os.path.isfile(A   ):
		raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""   )

	UpperCAmelCase_								=     os.path.join(A				,      VOCAB_FILES_NAMES["""merges_file"""]   )
	shutil.copyfile(A				,      A   )

	# model config
	UpperCAmelCase_								=     os.path.join(A				,      """config.json"""   )

	UpperCAmelCase_								=     {
	    """activation_dropout""": args["""activation_dropout"""],
	    """architectures""": ["""BioGptForCausalLM"""],
	    """attention_probs_dropout_prob""": args["""attention_dropout"""],
	    """bos_token_id""": 0,
	    """eos_token_id""": 2,
	    """hidden_act""": args["""activation_fn"""],
	    """hidden_dropout_prob""": args["""dropout"""],
	    """hidden_size""": args["""decoder_embed_dim"""],
	    """initializer_range""": 0.02,
	    """intermediate_size""": args["""decoder_ffn_embed_dim"""],
	    """layer_norm_eps""": 1E-12,
	    """layerdrop""": args["""decoder_layerdrop"""],
	    """max_position_embeddings""": args["""max_target_positions"""],
	    """model_type""": """biogpt""",
	    """num_attention_heads""": args["""decoder_attention_heads"""],
	    """num_hidden_layers""": args["""decoder_layers"""],
	    """pad_token_id""": 1,
	    """scale_embedding""": not args["""no_scale_embedding"""],
	    """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
	    """vocab_size""": src_vocab_size,
	}

	# good hparam defaults to start with

	print(f"""Generating {biogpt_model_config_file}"""   )
	with open(A				,      """w"""				,      encoding="""utf-8"""   ) as f:
		f.write(json.dumps(A				,      ensure_ascii=A				,      indent=A   )   )

	# tokenizer config
	UpperCAmelCase_								=     os.path.join(A				,      A   )

	UpperCAmelCase_								=     {
	    """bos_token""": """<s>""",
	    """eos_token""": """</s>""",
	    """model_max_length""": 1024,
	    """pad_token""": """<pad>""",
	    """special_tokens_map_file""": None,
	    """tokenizer_class""": """BioGptTokenizer""",
	    """unk_token""": """<unk>""",
	}

	print(f"""Generating {biogpt_tokenizer_config_file}"""   )
	with open(A				,      """w"""				,      encoding="""utf-8"""   ) as f:
		f.write(json.dumps(A				,      ensure_ascii=A				,      indent=A   )   )

	# model
	UpperCAmelCase_								=     chkpt["""model"""]

	# remove unneeded keys
	UpperCAmelCase_								=     [
	    """decoder.version""",
	]
	for k in ignore_keys:
		model_state_dict.pop(A				,      A   )

	UpperCAmelCase_								=     list(model_state_dict.keys()   )
	for layer_name in layer_names:
		if layer_name.endswith("""output_projection.weight"""   ):
			UpperCAmelCase_								=     model_state_dict.pop(A   )
		else:
			UpperCAmelCase_								=     model_state_dict.pop(A   )

	UpperCAmelCase_								=     BioGptConfig.from_pretrained(A   )
	UpperCAmelCase_								=     BioGptForCausalLM(A   )

	# check that it loads ok
	model_new.load_state_dict(A   )

	# save
	UpperCAmelCase_								=     os.path.join(A				,      A   )
	print(f"""Generating {pytorch_weights_dump_path}"""   )
	torch.save(A				,      A   )

	print("""Conversion is done!"""   )


if __name__ == "__main__":
				UpperCamelCase__   : int					  = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--biogpt_checkpoint_path""",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
				        """ bpecodes, etc."""
				    ),
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				UpperCamelCase__   : Union[str, Any]					  = parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









from __future__ import annotations

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




def    A_(			A				,      A				,      A				,      A				,      A				,      ):
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the reference grid
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the action grid

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

	UpperCAmelCase_								=     False  # flag that is set when search is complete
	UpperCAmelCase_								=     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()
			UpperCAmelCase_								=     cell.pop()
			UpperCAmelCase_								=     next_cell[2]
			UpperCAmelCase_								=     next_cell[3]
			UpperCAmelCase_								=     next_cell[1]

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

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


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = [
				    [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],
				]

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

				# the cost map which pushes the path closer to the goal
				UpperCamelCase__   : 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])):
												UpperCamelCase__   : Any					  = abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																UpperCamelCase__   : Dict					  = 99

				UpperCamelCase__  ,			UpperCamelCase__   : Dict					  = 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])


