# Path: moonlight/glyphs/base.py
# class GlyphsTensorColumns(enum.IntEnum):
# class BaseGlyphClassifier(object):
#   STAFF_INDEX = 0
#   Y_POSITION = 1
#   X = 2
#   TYPE = 3
#   def __init__(self):
#   def get_detected_glyphs(self):
#   def glyph_predictions_to_page(self, predictions):
#     def create_glyph(glyph):
#     def generate_staff(staff_num):
# 
# Path: moonlight/glyphs/convolutional.py
# DEFAULT_RUN_MIN_LENGTH = 3
# class Convolutional1DGlyphClassifier(base.BaseGlyphClassifier):
#   def __init__(self, run_min_length=DEFAULT_RUN_MIN_LENGTH):
#   def staffline_predictions(self):
#   def _build_detected_glyphs(self, predictions_arr):
#   def _create_glyph_arr(self, staff_index, y_position, x, type_value):
#   def get_detected_glyphs(self):
# 
# Path: moonlight/glyphs/testing.py
# PREDICTIONS = np.asarray(
#     [[[1, 1, 1, 1, 2, 1],
#       [3, 1, 5, 1, 1, 1],
#       [1, 4, 1, 1, 1, 1]],
#      [[1, 1, 3, 1, 1, 1],
#       [1, 1, 5, 1, 1, 1],
#       [1, 1, 1, 1, 3, 5]]])  # pyformat: disable
# GLYPHS_PAGE = musicscore_pb2.Page(system=[
#     musicscore_pb2.StaffSystem(staff=[
#         musicscore_pb2.Staff(glyph=[
#             musicscore_pb2.Glyph(x=0, y_position=0, type=3),
#             musicscore_pb2.Glyph(x=1, y_position=-1, type=4),
#             musicscore_pb2.Glyph(x=2, y_position=0, type=5),
#             musicscore_pb2.Glyph(x=4, y_position=1, type=2),
#         ]),
#         musicscore_pb2.Staff(glyph=[
#             musicscore_pb2.Glyph(x=2, y_position=1, type=3),
#             musicscore_pb2.Glyph(x=2, y_position=0, type=5),
#             musicscore_pb2.Glyph(x=4, y_position=-1, type=3),
#             musicscore_pb2.Glyph(x=5, y_position=-1, type=5),
#         ]),
#     ]),
# ])
# class DummyGlyphClassifier(convolutional.Convolutional1DGlyphClassifier):
#   def __init__(self, predictions):
#   def staffline_predictions(self):


import numpy as np
import pandas as pd
import tensorflow as tf
from moonlight.glyphs import base
from moonlight.glyphs import convolutional
from moonlight.glyphs import testing
from moonlight.protobuf import musicscore_pb2
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://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.
"""Tests for Convolutional1DGlyphClassifier."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function



STAFF_INDEX = base.GlyphsTensorColumns.STAFF_INDEX
Y_POSITION = base.GlyphsTensorColumns.Y_POSITION

# Path: moonlight/glyphs/base.py
# class GlyphsTensorColumns(enum.IntEnum):
# class BaseGlyphClassifier(object):
#   STAFF_INDEX = 0
#   Y_POSITION = 1
#   X = 2
#   TYPE = 3
#   def __init__(self):
#   def get_detected_glyphs(self):
#   def glyph_predictions_to_page(self, predictions):
#     def create_glyph(glyph):
#     def generate_staff(staff_num):
# 
# Path: moonlight/glyphs/convolutional.py
# DEFAULT_RUN_MIN_LENGTH = 3
# class Convolutional1DGlyphClassifier(base.BaseGlyphClassifier):
#   def __init__(self, run_min_length=DEFAULT_RUN_MIN_LENGTH):
#   def staffline_predictions(self):
#   def _build_detected_glyphs(self, predictions_arr):
#   def _create_glyph_arr(self, staff_index, y_position, x, type_value):
#   def get_detected_glyphs(self):
# 
# Path: moonlight/glyphs/testing.py
# PREDICTIONS = np.asarray(
#     [[[1, 1, 1, 1, 2, 1],
#       [3, 1, 5, 1, 1, 1],
#       [1, 4, 1, 1, 1, 1]],
#      [[1, 1, 3, 1, 1, 1],
#       [1, 1, 5, 1, 1, 1],
#       [1, 1, 1, 1, 3, 5]]])  # pyformat: disable
# GLYPHS_PAGE = musicscore_pb2.Page(system=[
#     musicscore_pb2.StaffSystem(staff=[
#         musicscore_pb2.Staff(glyph=[
#             musicscore_pb2.Glyph(x=0, y_position=0, type=3),
#             musicscore_pb2.Glyph(x=1, y_position=-1, type=4),
#             musicscore_pb2.Glyph(x=2, y_position=0, type=5),
#             musicscore_pb2.Glyph(x=4, y_position=1, type=2),
#         ]),
#         musicscore_pb2.Staff(glyph=[
#             musicscore_pb2.Glyph(x=2, y_position=1, type=3),
#             musicscore_pb2.Glyph(x=2, y_position=0, type=5),
#             musicscore_pb2.Glyph(x=4, y_position=-1, type=3),
#             musicscore_pb2.Glyph(x=5, y_position=-1, type=5),
#         ]),
#     ]),
# ])
# class DummyGlyphClassifier(convolutional.Convolutional1DGlyphClassifier):
#   def __init__(self, predictions):
#   def staffline_predictions(self):


# Path: moonlight/glyphs/convolutional_test.py
import numpy as np
import pandas as pd
import tensorflow as tf
from moonlight.glyphs import base
from moonlight.glyphs import convolutional
from moonlight.glyphs import testing
from moonlight.protobuf import musicscore_pb2

# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://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.
"""Tests for Convolutional1DGlyphClassifier."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function



STAFF_INDEX = base.GlyphsTensorColumns.STAFF_INDEX
Y_POSITION = base.GlyphsTensorColumns.Y_POSITION

X = base.GlyphsTensorColumns.X