""" @generated by mypy-protobuf. Do not edit manually! isort:skip_file """ import builtins import sys import typing import google.protobuf.descriptor import google.protobuf.internal.enum_type_wrapper import google.protobuf.message import tensorflow.core.framework.model_pb2 if sys.version_info >= (3, 10): import typing as typing_extensions else: import typing_extensions DESCRIPTOR: google.protobuf.descriptor.FileDescriptor class _AutoShardPolicy: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _AutoShardPolicyEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_AutoShardPolicy.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor AUTO: _AutoShardPolicy.ValueType # 0 """AUTO: Attempts FILE-based sharding, falling back to DATA-based sharding.""" FILE: _AutoShardPolicy.ValueType # 1 """FILE: Shards by input files (i.e. each worker will get a set of files to process). When this option is selected, make sure that there is at least as many files as workers. If there are fewer input files than workers, a runtime error will be raised. """ DATA: _AutoShardPolicy.ValueType # 2 """DATA: Shards by elements produced by the dataset. Each worker will process the whole dataset and discard the portion that is not for itself. Note that for this mode to correctly partitions the dataset elements, the dataset needs to produce elements in a deterministic order. """ HINT: _AutoShardPolicy.ValueType # 3 """HINT: Looks for the presence of `shard(SHARD_HINT, ...)` which is treated as a placeholder to replace with `shard(num_workers, worker_index)`. """ OFF: _AutoShardPolicy.ValueType # -1 """OFF: No sharding will be performed.""" class AutoShardPolicy(_AutoShardPolicy, metaclass=_AutoShardPolicyEnumTypeWrapper): """Represents the type of auto-sharding we enable.""" AUTO: AutoShardPolicy.ValueType # 0 """AUTO: Attempts FILE-based sharding, falling back to DATA-based sharding.""" FILE: AutoShardPolicy.ValueType # 1 """FILE: Shards by input files (i.e. each worker will get a set of files to process). When this option is selected, make sure that there is at least as many files as workers. If there are fewer input files than workers, a runtime error will be raised. """ DATA: AutoShardPolicy.ValueType # 2 """DATA: Shards by elements produced by the dataset. Each worker will process the whole dataset and discard the portion that is not for itself. Note that for this mode to correctly partitions the dataset elements, the dataset needs to produce elements in a deterministic order. """ HINT: AutoShardPolicy.ValueType # 3 """HINT: Looks for the presence of `shard(SHARD_HINT, ...)` which is treated as a placeholder to replace with `shard(num_workers, worker_index)`. """ OFF: AutoShardPolicy.ValueType # -1 """OFF: No sharding will be performed.""" global___AutoShardPolicy = AutoShardPolicy class _ExternalStatePolicy: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _ExternalStatePolicyEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_ExternalStatePolicy.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor POLICY_WARN: _ExternalStatePolicy.ValueType # 0 POLICY_IGNORE: _ExternalStatePolicy.ValueType # 1 POLICY_FAIL: _ExternalStatePolicy.ValueType # 2 class ExternalStatePolicy(_ExternalStatePolicy, metaclass=_ExternalStatePolicyEnumTypeWrapper): """Represents how to handle external state during serialization.""" POLICY_WARN: ExternalStatePolicy.ValueType # 0 POLICY_IGNORE: ExternalStatePolicy.ValueType # 1 POLICY_FAIL: ExternalStatePolicy.ValueType # 2 global___ExternalStatePolicy = ExternalStatePolicy @typing_extensions.final class AutotuneOptions(google.protobuf.message.Message): """next: 5""" DESCRIPTOR: google.protobuf.descriptor.Descriptor ENABLED_FIELD_NUMBER: builtins.int CPU_BUDGET_FIELD_NUMBER: builtins.int RAM_BUDGET_FIELD_NUMBER: builtins.int AUTOTUNE_ALGORITHM_FIELD_NUMBER: builtins.int enabled: builtins.bool cpu_budget: builtins.int ram_budget: builtins.int autotune_algorithm: tensorflow.core.framework.model_pb2.AutotuneAlgorithm.ValueType def __init__( self, *, enabled: builtins.bool | None = ..., cpu_budget: builtins.int | None = ..., ram_budget: builtins.int | None = ..., autotune_algorithm: tensorflow.core.framework.model_pb2.AutotuneAlgorithm.ValueType | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["autotune_algorithm", b"autotune_algorithm", "cpu_budget", b"cpu_budget", "enabled", b"enabled", "optional_autotune_algorithm", b"optional_autotune_algorithm", "optional_cpu_budget", b"optional_cpu_budget", "optional_enabled", b"optional_enabled", "optional_ram_budget", b"optional_ram_budget", "ram_budget", b"ram_budget"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["autotune_algorithm", b"autotune_algorithm", "cpu_budget", b"cpu_budget", "enabled", b"enabled", "optional_autotune_algorithm", b"optional_autotune_algorithm", "optional_cpu_budget", b"optional_cpu_budget", "optional_enabled", b"optional_enabled", "optional_ram_budget", b"optional_ram_budget", "ram_budget", b"ram_budget"]) -> None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_autotune_algorithm", b"optional_autotune_algorithm"]) -> typing_extensions.Literal["autotune_algorithm"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_cpu_budget", b"optional_cpu_budget"]) -> typing_extensions.Literal["cpu_budget"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_enabled", b"optional_enabled"]) -> typing_extensions.Literal["enabled"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_ram_budget", b"optional_ram_budget"]) -> typing_extensions.Literal["ram_budget"] | None: ... global___AutotuneOptions = AutotuneOptions @typing_extensions.final class CardinalityOptions(google.protobuf.message.Message): """next: 2""" DESCRIPTOR: google.protobuf.descriptor.Descriptor class _ComputeLevel: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _ComputeLevelEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[CardinalityOptions._ComputeLevel.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor CARDINALITY_COMPUTE_UNSPECIFIED: CardinalityOptions._ComputeLevel.ValueType # 0 CARDINALITY_COMPUTE_LOW: CardinalityOptions._ComputeLevel.ValueType # 1 """Cardinality will only be computed if it can be determined in a cheap manner (ie. without reading from file sources). If the cardinality would be nontrivial to compute, Cardinality() will return UNKNOWN_CARDINALITY. """ CARDINALITY_COMPUTE_MODERATE: CardinalityOptions._ComputeLevel.ValueType # 2 """Moderate effort will be made to determine cardinality, such as reading index data from source files. If significant work is needed to compute cardinality (e.g. reading entire source file contents or executing user defined functions), Cardinality() will return UNKNOWN_CARDINALITY. """ class ComputeLevel(_ComputeLevel, metaclass=_ComputeLevelEnumTypeWrapper): ... CARDINALITY_COMPUTE_UNSPECIFIED: CardinalityOptions.ComputeLevel.ValueType # 0 CARDINALITY_COMPUTE_LOW: CardinalityOptions.ComputeLevel.ValueType # 1 """Cardinality will only be computed if it can be determined in a cheap manner (ie. without reading from file sources). If the cardinality would be nontrivial to compute, Cardinality() will return UNKNOWN_CARDINALITY. """ CARDINALITY_COMPUTE_MODERATE: CardinalityOptions.ComputeLevel.ValueType # 2 """Moderate effort will be made to determine cardinality, such as reading index data from source files. If significant work is needed to compute cardinality (e.g. reading entire source file contents or executing user defined functions), Cardinality() will return UNKNOWN_CARDINALITY. """ COMPUTE_LEVEL_FIELD_NUMBER: builtins.int compute_level: global___CardinalityOptions.ComputeLevel.ValueType def __init__( self, *, compute_level: global___CardinalityOptions.ComputeLevel.ValueType | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["compute_level", b"compute_level"]) -> None: ... global___CardinalityOptions = CardinalityOptions @typing_extensions.final class DistributeOptions(google.protobuf.message.Message): """next: 3""" DESCRIPTOR: google.protobuf.descriptor.Descriptor AUTO_SHARD_POLICY_FIELD_NUMBER: builtins.int NUM_DEVICES_FIELD_NUMBER: builtins.int auto_shard_policy: global___AutoShardPolicy.ValueType num_devices: builtins.int def __init__( self, *, auto_shard_policy: global___AutoShardPolicy.ValueType | None = ..., num_devices: builtins.int | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["num_devices", b"num_devices", "optional_num_devices", b"optional_num_devices"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["auto_shard_policy", b"auto_shard_policy", "num_devices", b"num_devices", "optional_num_devices", b"optional_num_devices"]) -> None: ... def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_num_devices", b"optional_num_devices"]) -> typing_extensions.Literal["num_devices"] | None: ... global___DistributeOptions = DistributeOptions @typing_extensions.final class OptimizationOptions(google.protobuf.message.Message): """next: 20""" DESCRIPTOR: google.protobuf.descriptor.Descriptor APPLY_DEFAULT_OPTIMIZATIONS_FIELD_NUMBER: builtins.int FILTER_FUSION_FIELD_NUMBER: builtins.int MAP_AND_BATCH_FUSION_FIELD_NUMBER: builtins.int MAP_AND_FILTER_FUSION_FIELD_NUMBER: builtins.int MAP_FUSION_FIELD_NUMBER: builtins.int MAP_PARALLELIZATION_FIELD_NUMBER: builtins.int NOOP_ELIMINATION_FIELD_NUMBER: builtins.int PARALLEL_BATCH_FIELD_NUMBER: builtins.int SHUFFLE_AND_REPEAT_FUSION_FIELD_NUMBER: builtins.int FILTER_PARALLELIZATION_FIELD_NUMBER: builtins.int INJECT_PREFETCH_FIELD_NUMBER: builtins.int apply_default_optimizations: builtins.bool filter_fusion: builtins.bool map_and_batch_fusion: builtins.bool map_and_filter_fusion: builtins.bool map_fusion: builtins.bool map_parallelization: builtins.bool noop_elimination: builtins.bool parallel_batch: builtins.bool shuffle_and_repeat_fusion: builtins.bool filter_parallelization: builtins.bool inject_prefetch: builtins.bool def __init__( self, *, apply_default_optimizations: builtins.bool | None = ..., filter_fusion: builtins.bool | None = ..., map_and_batch_fusion: builtins.bool | None = ..., map_and_filter_fusion: builtins.bool | None = ..., map_fusion: builtins.bool | None = ..., map_parallelization: builtins.bool | None = ..., noop_elimination: builtins.bool | None = ..., parallel_batch: builtins.bool | None = ..., shuffle_and_repeat_fusion: builtins.bool | None = ..., filter_parallelization: builtins.bool | None = ..., inject_prefetch: builtins.bool | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["apply_default_optimizations", b"apply_default_optimizations", "filter_fusion", b"filter_fusion", "filter_parallelization", b"filter_parallelization", "inject_prefetch", b"inject_prefetch", "map_and_batch_fusion", b"map_and_batch_fusion", "map_and_filter_fusion", b"map_and_filter_fusion", "map_fusion", b"map_fusion", "map_parallelization", b"map_parallelization", "noop_elimination", b"noop_elimination", "optional_apply_default_optimizations", b"optional_apply_default_optimizations", "optional_filter_fusion", b"optional_filter_fusion", "optional_filter_parallelization", b"optional_filter_parallelization", "optional_inject_prefetch", b"optional_inject_prefetch", "optional_map_and_batch_fusion", b"optional_map_and_batch_fusion", "optional_map_and_filter_fusion", b"optional_map_and_filter_fusion", "optional_map_fusion", b"optional_map_fusion", "optional_map_parallelization", b"optional_map_parallelization", "optional_noop_elimination", b"optional_noop_elimination", "optional_parallel_batch", b"optional_parallel_batch", "optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion", "parallel_batch", b"parallel_batch", "shuffle_and_repeat_fusion", b"shuffle_and_repeat_fusion"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["apply_default_optimizations", b"apply_default_optimizations", "filter_fusion", b"filter_fusion", "filter_parallelization", b"filter_parallelization", "inject_prefetch", b"inject_prefetch", "map_and_batch_fusion", b"map_and_batch_fusion", "map_and_filter_fusion", b"map_and_filter_fusion", "map_fusion", b"map_fusion", "map_parallelization", b"map_parallelization", "noop_elimination", b"noop_elimination", "optional_apply_default_optimizations", b"optional_apply_default_optimizations", "optional_filter_fusion", b"optional_filter_fusion", "optional_filter_parallelization", b"optional_filter_parallelization", "optional_inject_prefetch", b"optional_inject_prefetch", "optional_map_and_batch_fusion", b"optional_map_and_batch_fusion", "optional_map_and_filter_fusion", b"optional_map_and_filter_fusion", "optional_map_fusion", b"optional_map_fusion", "optional_map_parallelization", b"optional_map_parallelization", "optional_noop_elimination", b"optional_noop_elimination", "optional_parallel_batch", b"optional_parallel_batch", "optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion", "parallel_batch", b"parallel_batch", "shuffle_and_repeat_fusion", b"shuffle_and_repeat_fusion"]) -> None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_apply_default_optimizations", b"optional_apply_default_optimizations"]) -> typing_extensions.Literal["apply_default_optimizations"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_filter_fusion", b"optional_filter_fusion"]) -> typing_extensions.Literal["filter_fusion"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_filter_parallelization", b"optional_filter_parallelization"]) -> typing_extensions.Literal["filter_parallelization"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_inject_prefetch", b"optional_inject_prefetch"]) -> typing_extensions.Literal["inject_prefetch"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_and_batch_fusion", b"optional_map_and_batch_fusion"]) -> typing_extensions.Literal["map_and_batch_fusion"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_and_filter_fusion", b"optional_map_and_filter_fusion"]) -> typing_extensions.Literal["map_and_filter_fusion"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_fusion", b"optional_map_fusion"]) -> typing_extensions.Literal["map_fusion"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_map_parallelization", b"optional_map_parallelization"]) -> typing_extensions.Literal["map_parallelization"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_noop_elimination", b"optional_noop_elimination"]) -> typing_extensions.Literal["noop_elimination"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_parallel_batch", b"optional_parallel_batch"]) -> typing_extensions.Literal["parallel_batch"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_shuffle_and_repeat_fusion", b"optional_shuffle_and_repeat_fusion"]) -> typing_extensions.Literal["shuffle_and_repeat_fusion"] | None: ... global___OptimizationOptions = OptimizationOptions @typing_extensions.final class ThreadingOptions(google.protobuf.message.Message): """next: 3""" DESCRIPTOR: google.protobuf.descriptor.Descriptor MAX_INTRA_OP_PARALLELISM_FIELD_NUMBER: builtins.int PRIVATE_THREADPOOL_SIZE_FIELD_NUMBER: builtins.int max_intra_op_parallelism: builtins.int private_threadpool_size: builtins.int def __init__( self, *, max_intra_op_parallelism: builtins.int | None = ..., private_threadpool_size: builtins.int | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["max_intra_op_parallelism", b"max_intra_op_parallelism", "optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism", "optional_private_threadpool_size", b"optional_private_threadpool_size", "private_threadpool_size", b"private_threadpool_size"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["max_intra_op_parallelism", b"max_intra_op_parallelism", "optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism", "optional_private_threadpool_size", b"optional_private_threadpool_size", "private_threadpool_size", b"private_threadpool_size"]) -> None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_max_intra_op_parallelism", b"optional_max_intra_op_parallelism"]) -> typing_extensions.Literal["max_intra_op_parallelism"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_private_threadpool_size", b"optional_private_threadpool_size"]) -> typing_extensions.Literal["private_threadpool_size"] | None: ... global___ThreadingOptions = ThreadingOptions @typing_extensions.final class Options(google.protobuf.message.Message): """Message stored with Dataset objects to control how datasets are processed and optimized. next: 9 """ DESCRIPTOR: google.protobuf.descriptor.Descriptor DETERMINISTIC_FIELD_NUMBER: builtins.int AUTOTUNE_OPTIONS_FIELD_NUMBER: builtins.int DISTRIBUTE_OPTIONS_FIELD_NUMBER: builtins.int OPTIMIZATION_OPTIONS_FIELD_NUMBER: builtins.int SLACK_FIELD_NUMBER: builtins.int THREADING_OPTIONS_FIELD_NUMBER: builtins.int EXTERNAL_STATE_POLICY_FIELD_NUMBER: builtins.int SYMBOLIC_CHECKPOINT_FIELD_NUMBER: builtins.int deterministic: builtins.bool @property def autotune_options(self) -> global___AutotuneOptions: """The distribution strategy options associated with the dataset.""" @property def distribute_options(self) -> global___DistributeOptions: """The distribution strategy options associated with the dataset.""" @property def optimization_options(self) -> global___OptimizationOptions: """The optimization options associated with the dataset.""" slack: builtins.bool @property def threading_options(self) -> global___ThreadingOptions: """The threading options associated with the dataset.""" external_state_policy: global___ExternalStatePolicy.ValueType symbolic_checkpoint: builtins.bool def __init__( self, *, deterministic: builtins.bool | None = ..., autotune_options: global___AutotuneOptions | None = ..., distribute_options: global___DistributeOptions | None = ..., optimization_options: global___OptimizationOptions | None = ..., slack: builtins.bool | None = ..., threading_options: global___ThreadingOptions | None = ..., external_state_policy: global___ExternalStatePolicy.ValueType | None = ..., symbolic_checkpoint: builtins.bool | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["autotune_options", b"autotune_options", "deterministic", b"deterministic", "distribute_options", b"distribute_options", "external_state_policy", b"external_state_policy", "optimization_options", b"optimization_options", "optional_deterministic", b"optional_deterministic", "optional_external_state_policy", b"optional_external_state_policy", "optional_slack", b"optional_slack", "optional_symbolic_checkpoint", b"optional_symbolic_checkpoint", "slack", b"slack", "symbolic_checkpoint", b"symbolic_checkpoint", "threading_options", b"threading_options"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["autotune_options", b"autotune_options", "deterministic", b"deterministic", "distribute_options", b"distribute_options", "external_state_policy", b"external_state_policy", "optimization_options", b"optimization_options", "optional_deterministic", b"optional_deterministic", "optional_external_state_policy", b"optional_external_state_policy", "optional_slack", b"optional_slack", "optional_symbolic_checkpoint", b"optional_symbolic_checkpoint", "slack", b"slack", "symbolic_checkpoint", b"symbolic_checkpoint", "threading_options", b"threading_options"]) -> None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_deterministic", b"optional_deterministic"]) -> typing_extensions.Literal["deterministic"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_external_state_policy", b"optional_external_state_policy"]) -> typing_extensions.Literal["external_state_policy"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_slack", b"optional_slack"]) -> typing_extensions.Literal["slack"] | None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["optional_symbolic_checkpoint", b"optional_symbolic_checkpoint"]) -> typing_extensions.Literal["symbolic_checkpoint"] | None: ... global___Options = Options