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Refactor constants management to use Pydantic BaseModel for configuration
- Replaced module-level path variables in constants.py with a structured Pydantic Config class. - Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure. - Fixed bugs related to image processing and model saving. - Enhanced test infrastructure to accommodate the new configuration approach. This refactor improves code maintainability and clarity by centralizing configuration management.
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# Model Split Tests
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**Task**: AZ-158_test_model_split
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**Name**: Model Split Tests
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**Description**: Implement 2 blackbox tests for model split storage — size constraint and reassembly integrity
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**Complexity**: 1 point
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**Dependencies**: AZ-152_test_infrastructure
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**Component**: Blackbox Tests
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**Jira**: AZ-158
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**Epic**: AZ-151
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## Problem
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Encrypted models are split into small and big parts for CDN storage. Tests must verify the split respects size constraints and reassembly produces the original.
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## Outcome
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- 2 passing pytest tests in `tests/test_model_split.py`
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## Scope
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### Included
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- BT-SPL-01: Split respects size constraint (small ≤ max(3072 bytes, 20% of total))
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- BT-SPL-02: Reassembly produces original (small + big == encrypted bytes)
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### Excluded
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- CDN upload/download (requires external service)
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## Acceptance Criteria
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**AC-1: Size constraint**
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Given 10000 encrypted bytes
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When split into small + big
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Then small ≤ max(3072, total × 0.2); big = remainder
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**AC-2: Reassembly**
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Given split parts from 10000 encrypted bytes
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When small + big concatenated
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Then equals original encrypted bytes
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## Constraints
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- Uses generated binary data (no fixture files needed)
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- References SMALL_SIZE_KB constant from constants.py
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