



"""simple docstring"""


import os

import numpy
import onnx

def  _lowerCAmelCase      (   lowercase_		,	lowercase_						):
						UpperCAmelCase             = a.name
						UpperCAmelCase             = b.name

						UpperCAmelCase             = ''
						UpperCAmelCase             = ''

						UpperCAmelCase             = a == b

						UpperCAmelCase             = name_a
						UpperCAmelCase             = name_b

						return res

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

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

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

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						UpperCAmelCase             = list(model.graph.initializer						)
						UpperCAmelCase             = 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

												UpperCAmelCase             = inits[i].name
												UpperCAmelCase             = 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		,	lowercase_		,	lowercase_						)






def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = os.path.dirname(lowercase_						)
						UpperCAmelCase             = os.path.basename(lowercase_						)

						UpperCAmelCase             = onnx.load(os.path.join(lowercase_		,	lowercase_						)						)

						UpperCAmelCase             = list(model.graph.initializer						)

						UpperCAmelCase             = set()
						UpperCAmelCase             = {}
						UpperCAmelCase             = []

						UpperCAmelCase             = 0

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

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

																								UpperCAmelCase             = inits[j].data_type
																								UpperCAmelCase             = numpy.prod(inits[j].dims						)
																								if dtype == 1:
																														mem_size *= 4
																								elif dtype == 6:
																														mem_size *= 4
																								elif dtype == 7 or dtype == 11:
																														mem_size *= 8
																								else:
																														print('unexpected data type: '		,	lowercase_						)
																								total_reduced_size += mem_size

																								UpperCAmelCase             = inits[i].name
																								UpperCAmelCase             = inits[j].name

																								if name_i in dup_map:
																														dup_map[name_i].append(lowercase_						)
																								else:
																														UpperCAmelCase             = [name_j]
																								ind_to_replace.append((j, i)						)

						print('total reduced size: '		,	total_reduced_size / 1024 / 1024 / 1024		,	'GB'						)

						UpperCAmelCase             = sorted(lowercase_						)
						_remove_dup_initializers_from_model(lowercase_		,	lowercase_		,	lowercase_						)

						UpperCAmelCase             = 'optimized_' + model_file_name
						UpperCAmelCase             = os.path.join(lowercase_		,	lowercase_						)
						onnx.save(lowercase_		,	lowercase_						)

						return new_model










'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




