





'''simple docstring'''







import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase       						=			'.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_lowercase       						=			[
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]

def    __UpperCamelCase	(	a    : List[str]					,   a    : List[str]					,   a    : int     ) ->Optional[int]:
 snake_case				=			SavedModel()
 snake_case				=			[]

 with open(os.path.join(a					,   '''utils'''					,   '''tf_ops'''					,   '''onnx.json'''     )     ) as f:
  snake_case				=			json.load(a     )['''opsets''']

 for i in range(1					,   opset + 1     ):
  onnx_ops.extend(onnx_opsets[str(a     )]     )

 with open(a					,   '''rb'''     ) as f:
  saved_model.ParseFromString(f.read()     )

 snake_case				=			set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node     )

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def     )

    # Convert to list, sorted if you want
 snake_case				=			sorted(a     )
 snake_case				=			[]

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(a     )

 if strict and len(a     ) > 0:
  raise Exception(f"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops     )
 elif len(a     ) > 0:
  print(f"""Found the following incompatible ops for the opset {opset}:"""     )
  print(*a					,   sep='''\n'''     )
 else:
  print(f"""The saved model {saved_model_path} can properly be converted with ONNX."""     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
   parser.add_argument(
       '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
   )
   parser.add_argument(
       '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
   )
   parser.add_argument(
       '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
   )
   _lowercase       						=			parser.parse_args()

   if args.framework == "onnx":
      onnx_compliancy(args.saved_model_path, args.strict, args.opset)








'''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


_lowercase       						=			[
    {'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    __UpperCamelCase	(	a    : Dict=True     ) ->str:
 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=__a					)					)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	str:
      with TemporaryDirectory() as tmp_dir:
       snake_case				=			dataset_module_factory(A__    ,   cache_dir=A__    )

       snake_case				=			import_main_class(dataset_module.module_path    ,   dataset=A__    )

       snake_case				=			builder_cls(
           cache_dir=A__    ,   config_name=A__    ,   hash=dataset_module.hash    ,   )

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




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






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

