Run3 scala tokenizer implementation guide

Implementing /root/Tokenizer.scala

Prerequisites

• You MUST have already read /root/Tokenizer.py (use read-python-tokenizer-source skill)
• You MUST have already read /root/TokenizerSpec.scala (use read-scala-test-spec skill)

Implementation Strategy

1. File Structure

package tokenizer  // or whatever package the test spec imports from

// All code in a single file /root/Tokenizer.scala

2. TokenType — Use Scala 2.13 Enumeration or sealed trait

If the test uses TokenType.STRING style access, use Enumeration:

object TokenType extends Enumeration {
  type TokenType = Value
  val STRING, NUMERIC, TEMPORAL, WHITESPACE, UNKNOWN = Value
  // Add all values from the Python enum
}

If the test uses pattern matching or case objects:

sealed trait TokenType
object TokenType {
  case object STRING extends TokenType
  case object NUMERIC extends TokenType
  // etc.
}

3. Token — Use case class

case class Token(
  value: String,
  tokenType: TokenType.TokenType,  // or TokenType depending on approach
  position: Int = 0,
  metadata: Map[String, String] = Map.empty
)

• Match field names exactly to what the test spec expects
• Use default values matching the Python defaults

4. BaseTokenizer — Use trait

trait BaseTokenizer {
  def tokenize(text: String): List[Token]  // or Seq — match test expectations
}

5. Concrete Tokenizers

StringTokenizer:

• Copy regex patterns exactly from Python
• Use scala.util.matching.Regex
• Use regex.findAllMatchIn(text) to iterate matches
• Build Token instances with position and metadata

class StringTokenizer extends BaseTokenizer {
  // Python: pattern = r'[a-zA-Z_][a-zA-Z0-9_]*'  (example — use actual pattern from source)
  private val pattern = """[a-zA-Z_][a-zA-Z0-9_]*""".r
  
  override def tokenize(text: String): List[Token] = {
    pattern.findAllMatchIn(text).map { m =>
      Token(
        value = m.matched,
        tokenType = TokenType.STRING,
        position = m.start,
        metadata = Map("length" -> m.matched.length.toString)  // match Python metadata keys exactly
      )
    }.toList
  }
}

NumericTokenizer:

• Translate all numeric regex patterns (integers, floats, scientific notation)
• Preserve the exact regex from Python
• Handle metadata (e.g., "isInteger", "isFloat", "value")

TemporalTokenizer:

• Translate date/time regex patterns exactly
• Handle metadata for format detection

UniversalTokenizer:

• Combines multiple tokenizers
• Implement fallback/priority logic from Python
• Sort by position if Python does that

WhitespaceTokenizer:

• Simple text.split("\\s+") approach
• Track positions

6. TokenizerBuilder — Builder Pattern

class TokenizerBuilder {
  private var tokenizers: List[BaseTokenizer] = List.empty
  // Add builder methods matching Python: addStringTokenizer, addNumericTokenizer, etc.
  // Return `this` for chaining
  def build(): BaseTokenizer = { /* return composed tokenizer */ }
}

7. Free Functions — Package object or companion object

Check where the test imports them from. Likely a package object:

package object tokenizer {
  def tokenize(text: String): List[Token] = { /* use UniversalTokenizer or default */ }
  def tokenizeBatch(texts: Seq[String]): Seq[List[Token]] = texts.map(tokenize)
  def toToken(value: String): Token = { /* create single token with type inference */ }
  def withMetadata(token: Token, metadata: Map[String, String]): Token = token.copy(metadata = token.metadata ++ metadata)
}

OR if they're in an object:

object Tokenizer {
  def tokenize(text: String): List[Token] = ...
  // etc.
}

8. Scala Idioms to Apply

• Pattern matching instead of if-elif chains for type detection
• Option instead of null/None for optional values
• Immutable collections (List, Map) by default
• Case class copy for creating modified tokens
• String interpolation s"..." instead of f-strings
• scala.util.matching.Regex for all regex operations
• No mutable state where possible
• map, flatMap, filter, collect instead of imperative loops
• Method chaining with builder pattern returning this

9. Error Handling

• Empty string input → return empty List
• Invalid input → return UNKNOWN token type or empty result (match Python behavior exactly)
• Use Option where Python uses Optional or None
• Use Try/Either only if Python explicitly raises exceptions that need catching

Compilation and Testing Workflow

Step 1: Write the implementation

cat > /root/Tokenizer.scala << 'SCALA'
// ... your implementation ...
SCALA

Step 2: Compile just the implementation

scalac /root/Tokenizer.scala

Fix any compilation errors before proceeding.

Step 3: Compile with test spec

Check what test framework the spec uses (ScalaTest, MUnit, specs2) and compile both:

# Find available jars
find / -name "*.jar" 2>/dev/null | grep -i scalatest
# Compile together
scalac -cp ".:path/to/scalatest.jar" /root/Tokenizer.scala /root/TokenizerSpec.scala

Step 4: Run tests

# ScalaTest example
scala -cp ".:path/to/scalatest.jar" org.scalatest.run TokenizerSpec

Step 5: Iterate on failures

• Read error messages carefully
• Common issues: wrong collection type (List vs Seq), wrong TokenType representation, missing methods, wrong package
• Fix and recompile

Common Pitfalls

1. Regex escaping: In Scala triple-quoted strings """...""", backslashes are literal. In regular strings, double them: "\\d+".
2. Package mismatch: If test says import tokenizer._, your code must be in package tokenizer (possibly in a package object for free functions).
3. Enumeration import: If using Enumeration, you may need import TokenType._ for the type alias.
4. Position tracking: Python's re.finditer gives .start() — Scala's findAllMatchIn gives .start.
5. Metadata values as String: Python may store int metadata; Scala Map[String, String] needs .toString.
6. If the spec expects package object: You CANNOT have a package object tokenizer inside package tokenizer in the same file easily. Put the package object in its own declaration block.