text: # Copyright 2020 IBM Corporation
  #
  # 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
  #
  # 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.
  
  import lale.docstrings
  import lale.operators
  import autoai_libs.transformers.exportable
  
  class CompressStringsImpl():
      def __init__(self, compress_type, dtypes_list, misslist_list, missing_values_reference_list, activate_flag):
          self._hyperparams = {
              'compress_type': compress_type,
              'dtypes_list': dtypes_list,
              'misslist_list': misslist_list,
              'missing_values_reference_list': missing_values_reference_list,
              'activate_flag': activate_flag}
          self._wrapped_model = autoai_libs.transformers.exportable.CompressStrings(**self._hyperparams)
  
      def fit(self, X, y=None):
          self._wrapped_model.fit(X, y)
          return self
  
      def transform(self, X):
          return self._wrapped_model.transform(X)
  
  _hyperparams_schema = {
      'allOf': [{
          'description': 'This first object lists all constructor arguments with their types, but omits constraints for conditional hyperparameters.',
          'type': 'object',
          'additionalProperties': False,
          'required': ['compress_type', 'dtypes_list', 'misslist_list', 'missing_values_reference_list', 'activate_flag'],
          'relevantToOptimizer': ['compress_type', 'activate_flag'],
          'properties': {
              'compress_type': {
                  'description': 'Type of string compression: `string` for removing spaces from a string and `hash` for creating an int hash, used when there are columns with strings and cat_imp_strategy=`most_frequent`.',
                  'enum': ['string', 'hash'],
                  'default': 'string'},
              'dtypes_list': {
                  'anyOf': [
                  {   'description': 'Strings that denote the type of each column of the input numpy array X.',
                      'type': 'array',
                      'items': {
                          'enum': ['char_str', 'int_str', 'float_str', 'float_num', 'float_int_num', 'int_num', 'boolean', 'Unknown']}},
                  {   'description': 'If None, the column types are discovered.',
                      'enum': [None]}],
                  'default': None},
              'misslist_list': {
                  'anyOf': [
                  {   'description': 'List containing lists of missing values of each column of the input numpy array X.',
                      'type': 'array',
                      'items': {'type': 'array', 'items': {'laleType': 'Any'}}},
                  {   'description': 'If None, the missing values of each column are discovered.',
                      'enum': [None]}],
                  'default': None},
              'missing_values_reference_list': {
                  'anyOf': [
                  {   'description': 'Reference list of missing values in the input numpy array X.',
  
                      'type': 'array',
                      'items': {'laleType': 'Any'}},
                  {   'description': 'If None, the missing values of each column are discovered.',
                      'enum': [None]}],
                  'default': None},
              'activate_flag': {
                  'description': 'If False, transform(X) outputs the input numpy array X unmodified.',
                  'type': 'boolean',
                  'default': True}}}]}
  
  _input_fit_schema = {
      'type': 'object',
      'required': ['X'],
      'additionalProperties': False,
      'properties': {
          'X': {#Handles 1-D arrays as well
              'anyOf': [
                  {'type': 'array', 'items': {'laleType': 'Any'}},
                  {'type': 'array', 'items': {'type': 'array', 'items': {'laleType': 'Any'}}}]},
          'y': {
              'laleType': 'Any'}}}
  
  _input_transform_schema = {
      'type': 'object',
      'required': ['X'],
      'additionalProperties': False,
      'properties': {
          'X': {#Handles 1-D arrays as well
              'anyOf': [
                  {'type': 'array', 'items': {'laleType': 'Any'}},
                  {'type': 'array', 'items': {'type': 'array', 'items': {'laleType': 'Any'}}}]}}}
  
  _output_transform_schema = {
      'description': 'Features; the outer array is over samples.',
      'anyOf': [
          {'type': 'array', 'items': {'laleType': 'Any'}},
          {'type': 'array', 'items': {'type': 'array', 'items': {'laleType': 'Any'}}}]}
  
  _combined_schemas = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': """Operator from `autoai_libs`_. Removes spaces and special characters from string columns of a numpy array.
  
  .. _`autoai_libs`: https://pypi.org/project/autoai-libs""",
      'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.compress_strings.html',
      'type': 'object',
      'tags': {
          'pre': [],
          'op': ['transformer'],
          'post': []},
      'properties': {
          'hyperparams': _hyperparams_schema,
          'input_fit': _input_fit_schema,
          'input_transform': _input_transform_schema,
          'output_transform': _output_transform_schema}}
  
  lale.docstrings.set_docstrings(CompressStringsImpl, _combined_schemas)
  
  CompressStrings = lale.operators.make_operator(CompressStringsImpl, _combined_schemas)
  