





'''simple docstring'''






import os

import numpy
import onnx






def      __A						(							a_						:  int      ,a_						:  Tuple	):
		lowerCAmelCase     :    Union[str, Any]						=   a.name
		lowerCAmelCase     :    Optional[int]						=   b.name

		lowerCAmelCase     :    Optional[int]						=   ""
		lowerCAmelCase     :    int						=   ""

		lowerCAmelCase     :    List[str]						=   a == b

		lowerCAmelCase     :    Tuple						=   name_a
		lowerCAmelCase     :    int						=   name_b

		return res






def      __A						(							a_						:  Optional[Any]      ,a_						:  Optional[Any]      ,a_						:  Union[str, Any]	):




		for i, input_name in enumerate(node_proto.input	):
				if input_name == name:
						node_proto.input.insert(_lowerCAmelCase      ,_lowerCAmelCase	)
						node_proto.input.pop(i + 1	)

		if node_proto.op_type == "If":
				_graph_replace_input_with(node_proto.attribute[0].g      ,_lowerCAmelCase      ,_lowerCAmelCase	)
				_graph_replace_input_with(node_proto.attribute[1].g      ,_lowerCAmelCase      ,_lowerCAmelCase	)
		if node_proto.op_type == "Loop":
				_graph_replace_input_with(node_proto.attribute[0].g      ,_lowerCAmelCase      ,_lowerCAmelCase	)






def      __A						(							a_						:  Union[str, Any]      ,a_						:  Tuple      ,a_						:  Optional[Any]	):




		for n in graph_proto.node:
				_node_replace_input_with(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase	)






def      __A						(							a_						:  List[str]      ,a_						:  str      ,a_						:  int	):
		lowerCAmelCase     :    List[str]						=   list(model.graph.initializer	)
		lowerCAmelCase     :    Dict						=   list(model_without_ext.graph.initializer	)
		for i, ref_i in ind_to_replace:
				assert inits_with_data[i].name == inits[i].name
				assert inits_with_data[ref_i].name == inits[ref_i].name
				assert i > ref_i

				lowerCAmelCase     :    Optional[Any]						=   inits[i].name
				lowerCAmelCase     :    Any						=   inits[ref_i].name

				model_without_ext.graph.initializer.remove(inits[i]	)

				# for n in model.graph.node:
				_graph_replace_input_with(model_without_ext.graph      ,_lowerCAmelCase      ,_lowerCAmelCase	)






def      __A						(							a_						:  Any	):
		lowerCAmelCase     :    Optional[Any]						=   os.path.dirname(_lowerCAmelCase	)
		lowerCAmelCase     :    str						=   os.path.basename(_lowerCAmelCase	)

		lowerCAmelCase     :    Union[str, Any]						=   onnx.load(os.path.join(_lowerCAmelCase      ,_lowerCAmelCase	)	)

		lowerCAmelCase     :    Optional[Any]						=   list(model.graph.initializer	)

		lowerCAmelCase     :    str						=   set()
		lowerCAmelCase     :    int						=   {}
		lowerCAmelCase     :    Union[str, Any]						=   []

		lowerCAmelCase     :    List[Any]						=   0

		for i in range(len(_lowerCAmelCase	)	):
				if i in dup_set:
						continue

				for j in range(i + 1      ,len(_lowerCAmelCase	)	):
						if j in dup_set:
								continue
						if _is_equal_tensor_proto(inits[i]      ,inits[j]	):
								dup_set.add(_lowerCAmelCase	)
								dup_set.add(_lowerCAmelCase	)

								lowerCAmelCase     :    Optional[Any]						=   inits[j].data_type
								lowerCAmelCase     :    List[str]						=   numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print("unexpected data type: "      ,_lowerCAmelCase	)
								total_reduced_size += mem_size

								lowerCAmelCase     :    Union[str, Any]						=   inits[i].name
								lowerCAmelCase     :    Optional[Any]						=   inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(_lowerCAmelCase	)
								else:
										lowerCAmelCase     :    Any						=   [name_j]
								ind_to_replace.append((j, i)	)

		print("total reduced size: "      ,total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4      ,"GB"	)

		lowerCAmelCase     :    Dict						=   sorted(_lowerCAmelCase	)
		_remove_dup_initializers_from_model(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase	)

		lowerCAmelCase     :    Tuple						=   "optimized_" + model_file_name
		lowerCAmelCase     :    int						=   os.path.join(_lowerCAmelCase      ,_lowerCAmelCase	)
		onnx.save(_lowerCAmelCase      ,_lowerCAmelCase	)

		return new_model










'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  List[Any]      ,a_						:  Optional[int]=1_0_2_4	):
		lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   [], []

		lowerCAmelCase     :    Optional[Any]						=   list(zip(a_      ,a_	)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   sorted_examples[0]

		def is_too_big(a_						:  Any	):
				return tok(a_      ,return_tensors="pt"	).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]	):
				lowerCAmelCase     :    Optional[int]						=   new_src + " " + src
				lowerCAmelCase     :    Union[str, Any]						=   new_tgt + " " + tgt
				if is_too_big(a_	) or is_too_big(a_	):  # cant fit, finalize example
						finished_src.append(a_	)
						finished_tgt.append(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   src, tgt
				else:  # can fit, keep adding
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(a_	)
				finished_tgt.append(a_	)
		return finished_src, finished_tgt






def      __A						(							a_						:  List[str]      ,a_						:  Path      ,a_						:  Any      ,a_						:  int	):
		lowerCAmelCase     :    Optional[int]						=   Path(a_	)
		save_path.mkdir(exist_ok=a_	)
		for split in ["train"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				lowerCAmelCase     :    Tuple						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase     :    str						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   pack_examples(a_      ,a_      ,a_      ,a_	)
				print(f'''packed {split} split from {len(a_	)} examples -> {len(a_	)}.'''	)
				Path(save_path / f'''{split}.source'''	).open("w"	).write("\n".join(a_	)	)
				Path(save_path / f'''{split}.target'''	).open("w"	).write("\n".join(a_	)	)
		for split in ["val", "test"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				shutil.copyfile(a_      ,save_path / f'''{split}.source'''	)
				shutil.copyfile(a_      ,save_path / f'''{split}.target'''	)






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   argparse.ArgumentParser()
		parser.add_argument("--tok_name"      ,type=a_      ,help="like facebook/bart-large-cnn,t5-base, etc."	)
		parser.add_argument("--max_seq_len"      ,type=a_      ,default=1_2_8	)
		parser.add_argument("--data_dir"      ,type=a_	)
		parser.add_argument("--save_path"      ,type=a_	)
		lowerCAmelCase     :    Optional[Any]						=   parser.parse_args()
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained(args.tok_name	)
		return pack_data_dir(a_      ,Path(args.data_dir	)      ,args.max_seq_len      ,args.save_path	)


if __name__ == "__main__":
					packer_cli()



