refactor: Update generated models and adapt to them (#704)

This PR updates the auto-generated Pydantic models based on OpenAPI specification changes in [apify-docs PR #2398](apify/apify-docs#2398).

## Summary

- Regenerated `_models.py` from updated OpenAPI spec
- Adapted resource clients to renamed/restructured models:
  - `TaskOptions` → `OptionalRunOptions`
  - `ScheduleCreateActions` → discriminated union in `ScheduleCreate`
  - `EnvVar` → `EnvVarRequest` for create/update requests

---------

Co-authored-by: Vlada Dusek <v.dusek96@gmail.com>

Author: apify-service-account

scripts/postprocess_generated_models.py

@@ -4,6 +4,10 @@
 - Discriminator field names: datamodel-codegen sometimes emits the JSON property name (camelCase)
   instead of the Python field name (snake_case) in `Field(discriminator='...')` annotations,
   particularly when the discriminator is on a schema referenced inside array items.
+- Duplicate ErrorType enum: ErrorResponse.yaml inlines the ErrorType enum (due to a Spectral
+  nested-$ref limitation), causing datamodel-codegen to generate a duplicate `Type(StrEnum)`
+  class alongside the canonical `ErrorType(StrEnum)`. This script removes the duplicate and
+  rewires references to use `ErrorType`.
 """
 
 from __future__ import annotations
@@ -31,15 +35,32 @@ def fix_discriminators(content: str) -> str:
     return content
 
 
+def deduplicate_error_type_enum(content: str) -> str:
+    """Remove the duplicate `Type` enum and rewire references to `ErrorType`."""
+    # Remove the entire `class Type(StrEnum): ...` block up to the next class definition.
+    content = re.sub(
+        r'\nclass Type\(StrEnum\):.*?(?=\nclass )',
+        '\n',
+        content,
+        flags=re.DOTALL,
+    )
+    # Replace standalone `Type` references in type annotations with `ErrorType`.
+    # Only target annotation contexts (`: Type`, `| Type`, `[Type`).
+    content = re.sub(r'(?<=: )Type\b|(?<=\| )Type\b|(?<=\[)Type\b', 'ErrorType', content)
+    # Collapse triple+ blank lines left by the removal.
+    return re.sub(r'\n{3,}', '\n\n\n', content)
+
+
 def main() -> None:
     content = MODELS_PATH.read_text()
     fixed = fix_discriminators(content)
+    fixed = deduplicate_error_type_enum(fixed)
 
     if fixed != content:
         MODELS_PATH.write_text(fixed)
-        print(f'Fixed discriminator values in {MODELS_PATH}')
+        print(f'Fixed generated models in {MODELS_PATH}')
     else:
-        print('No discriminator fixes needed')
+        print('No fixes needed')
 
 
 if __name__ == '__main__':