

'''simple docstring'''




import os

import numpy
import onnx




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =a.name
     __magic_name__						       =b.name

     __magic_name__						       =''''''
     __magic_name__						       =''''''

     __magic_name__						       =a == b

     __magic_name__						       =name_a
     __magic_name__						       =name_b

     return res




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Dict:






     '''simple docstring'''
     for i, input_name in enumerate(node_proto.input			):
          if input_name == name:
               node_proto.input.insert(a		,						a			)
               node_proto.input.pop(i + 1			)

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




def 						UpperCamelCase	(			a		,						a		,						a			)				->     List[str]:






     '''simple docstring'''
     for n in graph_proto.node:
          _node_replace_input_with(a		,						a		,						a			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =list(model.graph.initializer			)
     __magic_name__						       =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

          __magic_name__						       =inits[i].name
          __magic_name__						       =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		,						a		,						a			)




def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =os.path.dirname(a			)
     __magic_name__						       =os.path.basename(a			)

     __magic_name__						       =onnx.load(os.path.join(a		,						a			)			)

     __magic_name__						       =list(model.graph.initializer			)

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

     __magic_name__						       =0

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

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

                    __magic_name__						       =inits[j].data_type
                    __magic_name__						       =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: '''		,						a			)
                    total_reduced_size += mem_size

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

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

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

     __magic_name__						       =sorted(a			)
     _remove_dup_initializers_from_model(a		,						a		,						a			)

     __magic_name__						       ='''optimized_''' + model_file_name
     __magic_name__						       =os.path.join(a		,						a			)
     onnx.save(a		,						a			)

     return new_model









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     for split in ds.keys():
          print(F'''Splitting {split} with {ds[split].num_rows} records'''			)

          # to save to val.source, val.target like summary datasets
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    tgt_path.open('''w+'''			)

          # reader is the bottleneck so writing one record at a time doesn't slow things down
          for x in tqdm(ds[split]			):
               __magic_name__						       =    x['''translation''']
               src_fp.write(ex[src_lang] + '''\n'''			)
               tgt_fp.write(ex[tgt_lang] + '''\n'''			)

     print(F'''Saved {dataset} dataset to {save_dir}'''			)


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






