"""simple docstring"""






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict



@pytest.mark.parametrize(
    'files'					,			[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,			)
def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							tmp_path_factory.mktemp('dset_infos_dir'       )
				if "full:README.md" in files:
								with open(dataset_infos_dir / 'README.md'					,			'w'       ) as f:
												f.write('---\ndataset_info:\n  dataset_size: 42\n---'       )
				if "empty:README.md" in files:
								with open(dataset_infos_dir / 'README.md'					,			'w'       ) as f:
												f.write(''       )
    # we want to support dataset_infos.json for backward compatibility
				if "dataset_infos.json" in files:
								with open(dataset_infos_dir / 'dataset_infos.json'					,			'w'       ) as f:
												f.write('{"default": {"dataset_size": 42}}'       )
				UpperCAmelCase__             =							DatasetInfosDict.from_directory(lowerCamelCase       )
				assert dataset_infos
				assert dataset_infos["default"].dataset_size == 4_2



@pytest.mark.parametrize(
    'dataset_info'					,			[
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,			features=Features({'a': Value('int32'       )}       )					,			builder_name='builder'					,			config_name='config'					,			version='1.0.0'					,			splits=[{'name': 'train'}]					,			download_size=4_2					,			),
    ]					,			)
def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							str(lowerCamelCase       )
				dataset_info.write_to_directory(lowerCamelCase       )
				UpperCAmelCase__             =							DatasetInfo.from_directory(lowerCamelCase       )
				assert dataset_info == reloaded
				assert os.path.exists(os.path.join(lowerCamelCase					,			'dataset_info.json'       )       )



def   a_						(					):
				UpperCAmelCase__             =							DatasetInfo(
				    description='foo'					,			citation='bar'					,			homepage='https://foo.bar'					,			license='CC0'					,			features=Features({'a': Value('int32'       )}       )					,			post_processed={}					,			supervised_keys=()					,			task_templates=[]					,			builder_name='builder'					,			config_name='config'					,			version='1.0.0'					,			splits=[{'name': 'train', 'num_examples': 4_2}]					,			download_checksums={}					,			download_size=1_3_3_7					,			post_processing_size=4_4_2					,			dataset_size=1_2_3_4					,			size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4					,			)
				UpperCAmelCase__             =							dataset_info._to_yaml_dict()
				assert sorted(lowerCamelCase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								assert key in dataset_info_yaml_dict
								assert isinstance(dataset_info_yaml_dict[key]					,			(list, dict, int, str)       )
				UpperCAmelCase__             =							yaml.safe_dump(lowerCamelCase       )
				UpperCAmelCase__             =							yaml.safe_load(lowerCamelCase       )
				assert dataset_info_yaml_dict == reloaded



def   a_						(					):
				UpperCAmelCase__             =							DatasetInfo()
				UpperCAmelCase__             =							dataset_info._to_yaml_dict()
				assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    'dataset_infos_dict'					,			[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}       ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}       ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,			features=Features({'a': Value('int32'       )}       )					,			builder_name='builder'					,			config_name='config'					,			version='1.0.0'					,			splits=[{'name': 'train'}]					,			download_size=4_2					,			)
            }       ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=4_2       ),
                'v2': DatasetInfo(dataset_size=1_3_3_7       ),
            }       ),
    ]					,			)
def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							str(lowerCamelCase       )
				dataset_infos_dict.write_to_directory(lowerCamelCase       )
				UpperCAmelCase__             =							DatasetInfosDict.from_directory(lowerCamelCase       )

				# the config_name of the dataset_infos_dict take over the attribute
				for config_name, dataset_info in dataset_infos_dict.items():
								UpperCAmelCase__             =							config_name
								# the yaml representation doesn't include fields like description or citation
								# so we just test that we can recover what we can from the yaml
								UpperCAmelCase__             =							DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
				assert dataset_infos_dict == reloaded

				if dataset_infos_dict:
								assert os.path.exists(os.path.join(lowerCamelCase					,			'README.md'       )       )




"""simple docstring"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				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_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				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					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

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

				UpperCAmelCase__             =							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       )

																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 == 1_1:
																				mem_size *= 8
																else:
																				print('unexpected data type: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

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

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[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'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model



