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

import typing
from typing import Any, List

from rasa_nlu.components import Component
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.tokenizers import Tokenizer, Token
from rasa_nlu.training_data import Message
from rasa_nlu.training_data import TrainingData

if typing.TYPE_CHECKING:
    from spacy.tokens.doc import Doc


class SpacyTokenizer(Tokenizer, Component):
    name = "tokenizer_spacy"

    provides = ["tokens"]

    def train(self, training_data, config, **kwargs):
        # type: (TrainingData, RasaNLUModelConfig, **Any) -> None

        for example in training_data.training_examples:
            example.set("tokens", self.tokenize(example.get("spacy_doc")))

    def process(self, message, **kwargs):
        # type: (Message, **Any) -> None

        message.set("tokens", self.tokenize(message.get("spacy_doc")))

    def tokenize(self, doc):
        # type: (Doc) -> List[Token]

        return [Token(t.text, t.idx) for t in doc]

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

import typing
from typing import Any, List

from rasa_nlu.components import Component
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.tokenizers import Tokenizer, Token
from rasa_nlu.training_data import Message
from rasa_nlu.training_data import TrainingData

if typing.TYPE_CHECKING:
    from spacy.tokens.doc import Doc


class SpacyTokenizer(Tokenizer, Component):
    name = "tokenizer_spacy"

    provides = ["tokens"]

    requires = ["spacy_doc"]

    def train(self, training_data, config, **kwargs):
        # type: (TrainingData, RasaNLUModelConfig, **Any) -> None

        for example in training_data.training_examples:
            example.set("tokens", self.tokenize(example.get("spacy_doc")))

    def process(self, message, **kwargs):
        # type: (Message, **Any) -> None

        message.set("tokens", self.tokenize(message.get("spacy_doc")))

    def tokenize(self, doc):
        # type: (Doc) -> List[Token]

        return [Token(t.text, t.idx) for t in doc]

Add missing "requires" to spacy tokenizer
Add missing "requires" to spacy tokenizer

  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import typing
  from typing import Any, List
  
  from rasa_nlu.components import Component
  from rasa_nlu.config import RasaNLUModelConfig
  from rasa_nlu.tokenizers import Tokenizer, Token
  from rasa_nlu.training_data import Message
  from rasa_nlu.training_data import TrainingData
  
  if typing.TYPE_CHECKING:
      from spacy.tokens.doc import Doc
  
  
  class SpacyTokenizer(Tokenizer, Component):
      name = "tokenizer_spacy"
  
      provides = ["tokens"]
  
+     requires = ["spacy_doc"]
+ 
      def train(self, training_data, config, **kwargs):
          # type: (TrainingData, RasaNLUModelConfig, **Any) -> None
  
          for example in training_data.training_examples:
              example.set("tokens", self.tokenize(example.get("spacy_doc")))
  
      def process(self, message, **kwargs):
          # type: (Message, **Any) -> None
  
          message.set("tokens", self.tokenize(message.get("spacy_doc")))
  
      def tokenize(self, doc):
          # type: (Doc) -> List[Token]
  
          return [Token(t.text, t.idx) for t in doc]
  
Add missing "requires" to spacy tokenizer
## Code Before:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import typing
from typing import Any, List

from rasa_nlu.components import Component
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.tokenizers import Tokenizer, Token
from rasa_nlu.training_data import Message
from rasa_nlu.training_data import TrainingData

if typing.TYPE_CHECKING:
    from spacy.tokens.doc import Doc


class SpacyTokenizer(Tokenizer, Component):
    name = "tokenizer_spacy"

    provides = ["tokens"]

    def train(self, training_data, config, **kwargs):
        # type: (TrainingData, RasaNLUModelConfig, **Any) -> None

        for example in training_data.training_examples:
            example.set("tokens", self.tokenize(example.get("spacy_doc")))

    def process(self, message, **kwargs):
        # type: (Message, **Any) -> None

        message.set("tokens", self.tokenize(message.get("spacy_doc")))

    def tokenize(self, doc):
        # type: (Doc) -> List[Token]

        return [Token(t.text, t.idx) for t in doc]

## Instruction:
Add missing "requires" to spacy tokenizer
## Code After:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import typing
from typing import Any, List

from rasa_nlu.components import Component
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.tokenizers import Tokenizer, Token
from rasa_nlu.training_data import Message
from rasa_nlu.training_data import TrainingData

if typing.TYPE_CHECKING:
    from spacy.tokens.doc import Doc


class SpacyTokenizer(Tokenizer, Component):
    name = "tokenizer_spacy"

    provides = ["tokens"]

    requires = ["spacy_doc"]

    def train(self, training_data, config, **kwargs):
        # type: (TrainingData, RasaNLUModelConfig, **Any) -> None

        for example in training_data.training_examples:
            example.set("tokens", self.tokenize(example.get("spacy_doc")))

    def process(self, message, **kwargs):
        # type: (Message, **Any) -> None

        message.set("tokens", self.tokenize(message.get("spacy_doc")))

    def tokenize(self, doc):
        # type: (Doc) -> List[Token]

        return [Token(t.text, t.idx) for t in doc]
