# Path: tensorflow_transform/tf_metadata/dataset_metadata.py
# class DatasetMetadata:
#   def __init__(self, schema: schema_pb2.Schema):
#   def from_feature_spec(
#       cls: Type[_DatasetMetadataType],
#       feature_spec: Mapping[str, common_types.FeatureSpecType],
#       domains: Optional[Mapping[str, common_types.DomainType]] = None
#   ) -> _DatasetMetadataType:
#   def schema(self) -> schema_pb2.Schema:
#   def __eq__(self, other):
#   def __ne__(self, other):
#   def __repr__(self):


import argparse
import os
import pprint
import tempfile
import apache_beam as beam
import tensorflow as tf
import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam
from tensorflow_transform.tf_metadata import dataset_metadata
from tfx_bsl.coders.example_coder import RecordBatchToExamples
from tfx_bsl.public import tfxio
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of sentiment analysis using IMDB movie review dataset."""

# pylint: disable=g-bad-import-order



VOCAB_SIZE = 20000
TRAIN_BATCH_SIZE = 128
TRAIN_NUM_EPOCHS = 200
NUM_TRAIN_INSTANCES = 25000
NUM_TEST_INSTANCES = 25000

REVIEW_KEY = 'review'
REVIEW_WEIGHT_KEY = 'review_weight'
LABEL_KEY = 'label'

RAW_DATA_FEATURE_SPEC = {
    REVIEW_KEY: tf.io.FixedLenFeature([], tf.string),
    LABEL_KEY: tf.io.FixedLenFeature([], tf.int64)
}


# Path: tensorflow_transform/tf_metadata/dataset_metadata.py
# class DatasetMetadata:
#   def __init__(self, schema: schema_pb2.Schema):
#   def from_feature_spec(
#       cls: Type[_DatasetMetadataType],
#       feature_spec: Mapping[str, common_types.FeatureSpecType],
#       domains: Optional[Mapping[str, common_types.DomainType]] = None
#   ) -> _DatasetMetadataType:
#   def schema(self) -> schema_pb2.Schema:
#   def __eq__(self, other):
#   def __ne__(self, other):
#   def __repr__(self):


# Path: examples/sentiment_example.py
import argparse
import os
import pprint
import tempfile
import apache_beam as beam
import tensorflow as tf
import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam
from tensorflow_transform.tf_metadata import dataset_metadata
from tfx_bsl.coders.example_coder import RecordBatchToExamples
from tfx_bsl.public import tfxio

# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of sentiment analysis using IMDB movie review dataset."""

# pylint: disable=g-bad-import-order



VOCAB_SIZE = 20000
TRAIN_BATCH_SIZE = 128
TRAIN_NUM_EPOCHS = 200
NUM_TRAIN_INSTANCES = 25000
NUM_TEST_INSTANCES = 25000

REVIEW_KEY = 'review'
REVIEW_WEIGHT_KEY = 'review_weight'
LABEL_KEY = 'label'

RAW_DATA_FEATURE_SPEC = {
    REVIEW_KEY: tf.io.FixedLenFeature([], tf.string),
    LABEL_KEY: tf.io.FixedLenFeature([], tf.int64)
}


SCHEMA = dataset_metadata.DatasetMetadata.from_feature_spec(