


"""simple docstring"""






import os

import numpy
import onnx





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 for i, input_name in enumerate(node_proto.input						):
  if input_name == name:
   node_proto.input.insert(__UpperCamelCase      ,   __UpperCamelCase						)
   node_proto.input.pop(i + 1						)

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 for n in graph_proto.node:
  _node_replace_input_with(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Union[str, Any]:
 __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      ,   __UpperCamelCase      ,   __UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      Optional[Any]:
 __magic_name__     =      os.path.dirname(__UpperCamelCase						)
 __magic_name__     =      os.path.basename(__UpperCamelCase						)

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

 __magic_name__     =      list(model.graph.initializer						)

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

 __magic_name__     =      0

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

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

    __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: '''      ,   __UpperCamelCase						)
    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(__UpperCamelCase						)
    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(__UpperCamelCase						)
 _remove_dup_initializers_from_model(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						)

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

 return new_model




"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCamelCase										=		[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def 	lowercase      (   __UpperCamelCase=True						) ->      List[Any]:
 if with_config:
  return [
      {
          "testcase_name": d["dataset"] + "/" + d["config_name"],
          "dataset": d["dataset"],
          "config_name": d["config_name"],
      }
      for d in DATASETS_ON_HF_GCP
  ]
 else:
  return [
      {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
  ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase     )     )
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       None
     _lowerCamelCase     =       None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      with TemporaryDirectory() as tmp_dir:
       __magic_name__     =      dataset_module_factory(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )

       __magic_name__     =      import_main_class(dataset_module.module_path ,						dataset=UpperCamelCase_       )

       __magic_name__     =      builder_cls(
           cache_dir=UpperCamelCase_ ,						config_name=UpperCamelCase_ ,						hash=dataset_module.hash ,						)

       __magic_name__     =      '''/'''.join(
           [
               HF_GCP_BASE_URL,
               builder_instance._relative_data_dir(with_hash=UpperCamelCase_       ).replace(os.sep ,						'''/'''       ),
               config.DATASET_INFO_FILENAME,
           ]       )
       __magic_name__     =      cached_path(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )
       self.assertTrue(os.path.exists(UpperCamelCase_       )       )





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      tmp_path_factory.mktemp('''test_hf_gcp'''						) / '''test_wikipedia_simple'''
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
 __magic_name__     =      None
 builder_instance.download_and_prepare()
 __magic_name__     =      builder_instance.as_dataset()
 assert ds





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path      ,   dataset=__UpperCamelCase						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 __magic_name__     =      builder_instance.as_streaming_dataset()
 assert ds
 assert isinstance(__UpperCamelCase      ,   __UpperCamelCase						)
 assert "train" in ds
 assert isinstance(ds['''train''']      ,   __UpperCamelCase						)
 assert next(iter(ds['''train''']						)						)

