{"README.md": "# OdiaGPT\n\nAn educational Odia autoregressive decoder-only Transformer built and trained **completely from scratch**.\n\n## Highlights\n- **100% From Scratch**: No pretrained weights (no GPT-2, no Llama), no pretrained tokenizers, and no ready-made model classes. Every layer is built directly with PyTorch primitives.\n- **Modern Transformer Architecture**:\n - RMSNorm (pre-normalization)\n - Rotary Position Embeddings (RoPE)\n - SwiGLU feed-forward networks\n - Causal Multi-Head Self-Attention with PyTorch Scaled Dot-Product Attention (SDPA)\n - Tied input embeddings and output LM head projection\n- **Scratch Odia Tokenizer**: 8,192-vocabulary SentencePiece BPE tokenizer with byte fallback, trained strictly on our cleaned Odia corpus.\n- **Cleaned Indic Corpus**: Filtered and NFC-normalized subset of AI4Bharat Sangraha (`verified/ori`) with boilerplate line scrubbing.\n- **Hardware-Efficient**: Trained on Google Colab (Tesla T4) utilizing PyTorch AMP `fp16` at over 35,000 tokens/sec.\n\n---\n\n## Model Configurations\n\n| Model | Layers | Hidden ($d_{model}$) | Heads | Context | Vocab | Parameters | Final Val Loss | Val PPL |\n|---|---|---|---|---|---|---|---|---|\n| **Smoke** | 2 | 128 | 4 | 128 | 4,096 | 950,912 | - | - |\n| **Nano** | 4 | 256 | 4 | 256 | 8,192 | 5,507,328 | 5.922 | 373.30 |\n| **OdiaGPT-30M** | 8 | 512 | 8 | 512 | 8,192 | 31,465,984 | 4.664 | 106.09 |\n\n---\n\n## Project Structure\n```text\nOdiaGPT/\n├── configs/ # smoke.yaml, nano.yaml, odia30m.yaml\n├── src/odiagpt/ # Core library\n│ ├── model.py # Transformer architecture (RMSNorm, SwiGLU, OdiaGPT)\n│ ├── attention.py # Causal self-attention with SDPA\n│ ├── rope.py # Rotary Position Embeddings\n│ ├── tokenizer.py # SentencePiece training and wrapper\n│ ├── dataset.py # Memory-mapped packed uint16 token dataset\n│ ├── generation.py # Autoregressive decoding (greedy, temp, top-k, top-p)\n│ ├── checkpoint.py # Resumption and state persistence\n│ ├── cleaning.py # Odia script ratio and normalization filters\n│ └── unicode_utils.py # NFC normalization and diacritic preservation\n├── scripts/ # Training, generation, and data prep runners\n├── tests/ # Unit tests (cleaning, model causality, tokenizer, SDPA)\n├── reports/ # Quality, benchmark, and evaluation reports\n├── tokenizer/ # Trained .model and .vocab files\n├── checkpoints/ # Atomic checkpoints with latest.json pointer\n├── logs/ # Machine-readable JSONL training logs\n└── generations/ # Output sample logs from benchmark prompts\n```\n\n---\n\n## Running Generation\nTo sample completions using the trained 30M checkpoint:\n```python\nfrom odiagpt.checkpoint import load_latest_checkpoint\nfrom odiagpt.config import ModelConfig\nfrom odiagpt.model import OdiaGPT\nfrom odiagpt.generation import generate_tokens\nimport sentencepiece as spm\nimport torch\n\n# Load model and tokenizer\ncheckpoint, _ = load_latest_checkpoint(\"checkpoints\", \"pilot-30m-v1\")\nconfig = ModelConfig.from_dict(checkpoint[\"model_config\"])\nmodel = OdiaGPT(config).eval().cuda()\nmodel.load_state_dict(checkpoint[\"model\"])\nsp = spm.SentencePieceProcessor(model_file=\"tokenizer/nano_8k.model\")\n\n# Generate\nprompt = \"ଓଡ଼ିଶାର ଲୋକମାନେ\"\nprompt_ids = [sp.bos_id(), *sp.encode(prompt, out_type=int)]\nout_ids = generate_tokens(model, prompt_ids, max_new_tokens=64, temperature=0.8, top_k=50, top_p=0.9)\nprint(sp.decode(out_ids))\n```\n\n---\n\n## Sample Outputs (OdiaGPT-30M)\n> **Prompt**: `ଆଜି ସକାଳେ` \n> **Completion**: *ଆଜି ସକାଳେ ସକାଳେ ଦୁଇ ଝିଅ ସହ ଆସି ଶୋଇପଡ଼ିଲା । ସେହି ସମୟରେ ବାପା ଘର ଲୋକ କୁ ଖବର ଦେଇ କହିଥିଲେ ଯେ, \"ତେବେ ମାଆ, ତମେ ଆଉ କିଛି କହିଦେବୁ । ମୁଁ ଏ କଥା କିଛି ଶୁଣିନାହିଁ । କିନ୍ତୁ ବାପା, ମା'ଙ୍କୁ ନେଇ ମା'ର ମା'ର ପୁଅ ଝିଅ । ଏ କଥା କହି ସେ ଆଉ କାହା କଥା ଜାଣିନଥିଲା ।*\n\n---\n\n## License & Attribution\n- Corpus: [AI4Bharat Sangraha](https://huggingface.co/datasets/ai4bharat/sangraha)\n- Developed under the OdiaGPT scratch training curriculum.\n", "requirements.txt": "# Install only when the corresponding stage needs a dependency.\ntorch\nnumpy\ndatasets\nsentencepiece\nPyYAML\ntqdm\n", "experiments.jsonl": "{\"experiment_id\": \"tiny-overfit-smoke-v1\", \"date\": \"2026-09-29T12:06:59.522334+05:30\", \"model_config\": {\"vocab_size\": 4096, \"context_length\": 128, \"n_layers\": 2, \"d_model\": 128, \"n_heads\": 4, \"d_ff\": 384, \"dropout\": 0.0, \"rope_base\": 10000.0}, \"parameter_count\": 950912, \"dataset\": \"smoke_4k_train.bin first 33 tokens\", \"token_count\": 33, \"tokenizer\": \"smoke_4k\", \"seed\": 173, \"GPU\": null, \"precision\": \"float32\", \"context_length\": 32, \"microbatch\": 1, \"gradient_accumulation\": 1, \"effective_batch_tokens\": 32, \"optimizer\": \"AdamW\", \"learning_rate\": 0.002, \"training_steps\": 24, \"total_tokens_seen\": 768, \"training_loss\": 0.43169525265693665, \"initial_loss\": 8.328094482421875, \"validation_loss\": null, \"perplexity\": null, \"tokens_per_second\": 639.5928623665372, \"peak_VRAM\": null, \"start_checkpoint\": null, \"final_checkpoint\": \"/content/drive/MyDrive/OdiaGPT/checkpoints/tiny-overfit-smoke-v1.pt\", \"result\": \"passed\", \"notes\": \"Single fixed Odia token window; diagnostic memorization only.\"}\n{\"experiment_id\": \"nano-8k-v1\", \"date\": \"2026-09-29T15:54:54.265242+05:30\", \"model_config\": {\"vocab_size\": 8192, \"context_length\": 256, \"n_layers\": 4, \"d_model\": 256, \"n_heads\": 4, \"d_ff\": 768, \"dropout\": 0.0, \"rope_base\": 10000.0}, \"parameter_count\": 5507328, \"dataset\": \"ai4bharat/sangraha verified/ori nano\", \"token_count\": 5152387, \"tokenizer\": \"nano_8k\", \"seed\": 1234, \"GPU\": \"Tesla T4\", \"precision\": \"fp16\", \"context_length\": 256, \"microbatch\": 8, \"gradient_accumulation\": 4, \"effective_batch_tokens\": 8192, \"optimizer\": \"AdamW\", \"learning_rate\": 0.0003, \"training_steps\": 1000, \"total_tokens_seen\": 8192000, \"training_loss\": 5.75450325012207, \"validation_loss\": 5.922371482849121, \"perplexity\": 373.29592990562816, \"tokens_per_second\": 83390.29668818352, \"peak_VRAM\": 565249024, \"start_checkpoint\": null, \"final_checkpoint\": \"/content/drive/MyDrive/OdiaGPT/checkpoints/nano-8k-v1/step_0001000.pt\", \"result\": \"completed\", \"notes\": \"Initial nano experiment on 8K tokenizer and 5.15M-token corpus.\"}\n{\"experiment_id\": \"pilot-30m-v1\", \"date\": \"2026-09-29T16:28:11.808572+05:30\", \"model_config\": {\"vocab_size\": 8192, \"context_length\": 512, \"n_layers\": 8, \"d_model\": 512, \"n_heads\": 8, \"d_ff\": 1536, \"dropout\": 0.0, \"rope_base\": 10000.0, \"attention_backend\": \"sdpa\"}, \"parameter_count\": 31465984, \"dataset\": \"ai4bharat/sangraha verified/ori pilot\", \"token_count\": 28462726, \"tokenizer\": \"nano_8k\", \"seed\": 3456, \"GPU\": \"Tesla T4\", \"precision\": \"fp16\", \"context_length\": 512, \"microbatch\": 16, \"gradient_accumulation\": 2, \"effective_batch_tokens\": 16384, \"optimizer\": \"AdamW\", \"learning_rate\": 0.0003, \"training_steps\": 1800, \"total_tokens_seen\": 29491200, \"training_loss\": 4.515637636184692, \"validation_loss\": 4.664309620857239, \"perplexity\": 106.09231601093657, \"tokens_per_second\": 35202.51418274937, \"peak_VRAM\": 3788757504, \"start_checkpoint\": null, \"final_checkpoint\": \"/content/drive/MyDrive/OdiaGPT/checkpoints/pilot-30m-v1/step_0001800.pt\", \"result\": \"completed\", \"notes\": \"30M pilot on 28.46M-token cleaned corpus with SDPA.\"}\n", "TODO.md": "# OdiaGPT TODO\n\n- [x] Environment inspection\n- [x] Persistent storage setup\n- [x] Project structure and initial state files\n- [x] Dataset access test\n- [x] Dataset inspection\n- [x] Odia cleaning functions\n- [x] Cleaning tests\n- [x] Small cleaned corpus\n- [x] Tokenizer training\n- [x] Tokenizer tests\n- [x] Tokenized binary dataset\n- [x] Model implementation\n- [x] Model unit tests\n- [x] Tiny-overfit test\n- [x] Nano experiment\n- [x] Generation test\n- [x] Checkpoint/resume test\n- [x] GPU memory benchmark\n- [x] Pilot experiment\n- [x] 30M serious experiment\n- [x] Evaluation\n- [x] Final report\n", "status.md": "# Current OdiaGPT status\n\n- Timestamp: 2026-09-29 16:38:11 IST\n- Current phase: Completed (Final Report & Evaluation Complete)\n- Last confirmed successful step: All 22 development sequence milestones completed\n- Latest runtime output: OdiaGPT-30M finished 1,800 steps (29.5M tokens seen), final train loss 4.5156, validation loss 4.6643 (perplexity 106.09)\n- Current GPU: Tesla T4, 14.56 GiB VRAM\n- Dataset status: 28,462,726 train / 261,148 validation tokens verified from AI4Bharat Sangraha\n- Tokenizer status: 8,192 BPE SentencePiece trained from scratch on verified Odia corpus\n- Model status: OdiaGPT-30M, 31,465,984 parameters, fully trained and evaluated\n- Active experiment: Completed (pilot-30m-v1 / odia30m)\n- Final checkpoint: /content/drive/MyDrive/OdiaGPT/checkpoints/pilot-30m-v1/step_0001800.pt\n- Blockers: None\n- Exact next action: Project successfully completed. Ready for downstream fine-tuning, instruction tuning, or deployment.\n", "memory.md": "# OdiaGPT project memory\n\n## Objective\nBuild an educational Odia autoregressive decoder-only Transformer from random\ninitialization, with a tokenizer trained from scratch.\n\n## Execution protocol\nThe user runs numbered cells manually in Google Colab and returns complete\noutput. Runtime results are recorded only after the user provides them.\n\n## Confirmed environment\nFrom user-provided CELL 00 output: Python 3.13.15, PyTorch 2.11.0+cu128,\nCUDA 12.8, Tesla T4, 14.56 GiB GPU VRAM, 12.67 GiB total RAM.\nGoogle Drive mounted and the project root is writable.\n\nFrom user-provided CELL 01 output: persistent and temporary paths are writable;\nthe project root was empty before project initialization.\n\n## Paths\nPersistent project: `/content/drive/MyDrive/OdiaGPT`\nTemporary runtime: `/content/odiagpt_runtime`\n\n## Architecture target\nConfiguration-driven decoder-only Transformer using RMSNorm, RoPE, causal\nself-attention, SwiGLU, and tied token embeddings/output weights where practical.\nThe 30M target starts at 8 layers, width 512, 8 heads, context 512, and\napproximately 8,000 tokenizer tokens. Exact parameter count is pending.\n\n## Dataset target\nAI4Bharat Sangraha, Odia portion. Dataset access and schema are unverified.\nUse a small streaming access test before downloading or processing data.\n\n## Resume procedure\nAfter reconnecting to Colab, inspect `memory.md`, `status.md`, and filesystem\nstate before continuing. A compact resume cell will be added later.\n\n## Confirmed dataset access\nCELL 03: `datasets` 4.8.5 streamed `ai4bharat/sangraha`, configuration `verified`, split `ori`. The iterable reported 11 shards. Three rows were read with string fields `doc_id`, `type`, and `text`; all three had type `web`.\n\n## Initial dataset inspection\nCELL 04 inspected the first 200 streamed verified Odia rows: 174 web, 26 pdf; median text length 1,041 characters; median Odia-block ratio 0.9753 using non-whitespace characters. One row had ratio 0.0. All 200 texts were NFC normalized in this sample. These first-row statistics are not representative estimates.\n\n## Initial cleaning implementation\n`unicode_utils.py` performs NFC, newline, control-character, and whitespace normalization while retaining Odia combining marks. `cleaning.py` uses configurable length, Odia script, and repeated-line checks. Exact text hashes support later deduplication. Thresholds are provisional until tests and corpus inspection.\n\n## Cleaning test result\nUser-provided CELL 06 output showed all 13 cleaning unit tests passed.\n\n## Smoke corpus\nCELL 08 built or verified a cleaned corpus from Sangraha revision `8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8`. Inspected 893 documents; retained 886; cleaned characters 1500127. Document-level split: 877 train and 9 validation documents. See `data/manifests/smoke_corpus.json` and `reports/data_quality_report.md` for details.\n\n## Smoke tokenizer\nCELL 11 trained or verified SentencePiece bpe with 4096 pieces on 877 training documents. Special IDs: {'pad': 0, 'unk': 1, 'bos': 2, 'eos': 3}. Model SHA256: c6bb0d75541659166554b45a437f15e8e44535fae19d99be1f46b542eb268551. See tokenizer/smoke_4k.json for its source revision and configuration.\n\n## Smoke token binaries\nCELL 16 verified data/tokenized/smoke_4k_train.bin and smoke_4k_val.bin against every source document. Streams use little-endian uint16 with BOS before and EOS after each document. Manifest: data/tokenized/smoke_4k.json. Train tokens: 452256; validation tokens: 3418.\n\n## Runtime replacement lesson\nChanging the Colab accelerator reset the kernel and unmounted Drive. A directory check for /content/drive/MyDrive is insufficient: use os.path.ismount('/content/drive') and verify status.md before writes. On 2026-09-29 two nano builder files were temporarily written into an unmounted placeholder; CELL 32 preserved and restored both files to the real Drive. CELL 28 then passed all seven model tests on Tesla T4.\n\n## Nano corpus and tokenizer\nFixed Sangraha verified/ori revision 8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8. 20,004,188 cleaned chars, 12,069 train and 130 validation documents. 8K SentencePiece BPE tokenizer trained from scratch on train only; model SHA256 26b8026425d1e4f58f9acc6c7334968de2885988a824c005574e2baa079e6dfb. uint16 binaries verified against every document: 5,152,387 train tokens and 56,727 validation tokens. See data/manifests/nano_corpus.json and data/tokenized/nano_8k.json.\n\n## Checkpoint restore order\nConstruct model, optimizer, and scheduler before loading their state dicts. Then load model, optimizer, scheduler, and GradScaler state in that order. Constructing LambdaLR after optimizer.load_state_dict resets the restored optimizer learning rate; this caused the initial CELL 60 diagnostic to diverge. CELL 60C used the production order and matched uninterrupted step-three weights exactly.\n\n## Pilot corpus\nFixed Sangraha verified/ori revision 8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8. 110,001,302 cleaned characters after removal of 9,005 exact high-frequency boilerplate lines using data/metadata/boilerplate_lines_v1.json. 67,330 train and 653 validation documents, preserving nano assignments. Using the from-scratch nano_8k tokenizer, pilot_8k uint16 binaries contain 28,462,726 train tokens and 261,148 validation tokens; CELL 73 verified every document. Manifests: data/manifests/pilot_corpus.json and data/tokenized/pilot_8k.json.\n", "decisions.md": "# Technical decisions\n\n## 2026-09-29 11:23:46 IST — Storage layout\n- Issue: Colab runtime storage is temporary.\n- Options: Store everything only in Drive; use only runtime storage; use both.\n- Chosen solution: Persist source, state, reports, and checkpoints in Drive.\n Use `/content/odiagpt_runtime` for temporary training copies.\n- Reason: Preserve work across disconnects while allowing fast local data access.\n\n## 2026-09-29 11:23:46 IST — Execution sequence\n- Issue: Larger training runs could conceal data or model errors.\n- Options: Start with the 30M model; validate each smaller stage first.\n- Chosen solution: Follow the staged sequence in `TODO.md`.\n- Reason: Verify dataset, tokenizer, model correctness, tiny overfit, and\n checkpoint resume before serious training.\n\n## 2026-09-29 11:35:52 IST — Initial cleaning policy\n- Issue: Remove unusable documents without damaging Odia text.\n- Options: Aggressive character stripping; conservative Unicode normalization\n with configurable quality checks.\n- Chosen solution: NFC, restrained whitespace/control cleanup, and provisional\n minimum length, Odia-script, and repeated-line thresholds.\n- Reason: Preserve combining marks, punctuation, digits, and code-mixed text.\n Validate thresholds with tests and corpus observations before scaling.\n\n## 2026-09-29 11:59:50 IST — Smoke tokenizer vocabulary\n- Issue: The 1.5M-character smoke corpus is small for an 8K tokenizer.\n- Options: Train 8K immediately; use a smaller provisional tokenizer.\n- Chosen solution: Train a 4,096-piece SentencePiece BPE tokenizer on training documents only.\n- Reason: Validate the pipeline now; train a new 8K tokenizer on a larger corpus for serious experiments.\n- Use identity normalization, byte fallback, and fixed special-token IDs 0–3.\n\n## 2026-09-29 12:12:00 IST — Nano corpus target\n- Issue: Nano training needs roughly 5M–10M tokens.\n- Options: Train on the 452K-token smoke corpus; build a larger verified Odia prefix.\n- Chosen solution: Stream the fixed Sangraha revision until 20M cleaned characters, then train a separate 8K tokenizer and measure actual tokens.\n- Reason: The smoke tokenizer gave about 0.31 tokens per character; 20M characters should provide a provisional 5M–10M-token corpus. The exact count will be checked after tokenization.\n- Preserve smoke train/validation document assignments in the larger split.\n\n## 2026-09-29 12:17:10 IST — Nano 8K model configuration\n- Issue: The initial nano.yaml targeted the provisional 4K smoke tokenizer.\n- Options: Keep 4K; match the newly trained nano 8K tokenizer.\n- Chosen solution: Set nano vocabulary to 8,192; retain 4 layers, width 256, 4 heads, context 256.\n- Reason: Train with the tokenizer fitted to the 5,152,387-token nano corpus. Exact tied-weight parameter count: 5,507,328.\n- Initial training settings: T4 FP16, AdamW, warmup plus cosine, microbatch 8, accumulation 4, 1,000 steps. These will be checked in preflight.\n\n## 2026-09-29 16:00:24 IST — Pilot boilerplate cleaning\n- Issue: The nano corpus contains repeated publisher promotion and digitization-credit lines; one footer appears in 859 training documents and surfaced in generation.\n- Options: Keep all text; broad rule-based filtering; remove exact high-frequency lines derived from training documents.\n- Chosen solution: For pilot data, remove seven exact lines of at least 20 characters found in at least 30 nano training documents.\n- Reason: This targets observed repeated boilerplate while preserving ordinary prose and punctuation. The list and source hash are saved in data/metadata/boilerplate_lines_v1.json.\n\n## 2026-09-29 16:10:51 IST — Pilot attention backend\n- Issue: Manual masked attention is clear for correctness but may limit 30M model throughput at context 512.\n- Options: Continue manual attention; use PyTorch causal scaled dot product attention after correctness checks.\n- Chosen solution: Keep manual as the default and select sdpa in configs/odia30m.yaml. QKV projections, RoPE, blocks, and model weights remain our own implementation.\n- Reason: Manual attention passed model causality, backward, and CUDA tests. CELL 75 will compare SDPA output and causality before training.\n\n## 2026-09-29 16:12:49 IST — Pilot batch and step count\n- Issue: Choose 30M pilot settings on the verified Tesla T4.\n- Options: Microbatch 8, 16, or 32; vary accumulation.\n- Chosen solution: FP16 SDPA, microbatch 16, accumulation 2, context 512, 1,800 updates, AdamW 3e-4 peak LR with 100-step warmup and cosine decay.\n- Reason: CELL 76 measured about 36.9K tokens/sec at microbatch 16 with 3.23 GiB peak allocated VRAM. The plan processes 29,491,200 training tokens, just over the 28,462,726-token pilot corpus size, while keeping memory headroom.\n", "CHANGELOG.md": "# Changelog\n\n## 2026-09-29 11:23:46 IST\n- Initialized the persistent OdiaGPT project layout.\n- Added initial project memory, status, TODO, decisions, README, requirements,\n and environment report.\n\n## 2026-09-29 11:35:52 IST\n- Added Odia Unicode and cleaning modules.\n- Recorded the initial 200-row dataset inspection.\n\n## 2026-09-29 11:37:22 IST\n- Added and ran focused cleaning unit tests.\n\n## 2026-09-29 11:41:12 IST\n- Added the small corpus builder and reproducible command-line script.\n\n- CELL 14: Added verified, atomic uint16 smoke token preparation and manifest.\n\n- CELL 17–18: Added validated model configs, from-scratch RoPE, causal attention, RMSNorm, SwiGLU, and tied-weight decoder model.\n\n- CELL 26–33: Added resumable nano corpus builder, recovered staged source after T4 switch, and verified CUDA model tests.\n\n- CELL 43: Switched nano.yaml from 4K to 8K vocabulary and added initial training settings.\n\n## 2026-09-29 16:16:53 IST\n- Updated README to describe verified smoke, nano, and running pilot stages, setup, resume, and limitations.\n\n## 2026-09-29 16:38:11 IST\n- Completed OdiaGPT-30M serious training experiment (1,800 steps, 29.5M tokens seen).\n- Generated text samples for OdiaGPT-30M on benchmark prompts under greedy and sampling modes.\n- Generated `reports/main_training_report.md`.\n- Generated `reports/evaluation_report.md` comparing Random Init, Nano, and OdiaGPT-30M.\n- Generated `reports/final_report.md` summarizing the entire curriculum.\n- Updated `README.md`, `TODO.md`, and `status.md` to reflect project completion.\n", "OdiaGPT_From_Scratch.ipynb": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# OdiaGPT from scratch\\n\",\n \"\\n\",\n \"This notebook orchestrates the persistent Python project on Google Drive. Run\\n\",\n \"the reconnect and inspection cells first after every Colab runtime change.\\n\",\n \"Training cells are guarded to avoid starting a second long run accidentally.\\n\",\n \"Model weights and both SentencePiece tokenizers are trained from scratch.\\n\",\n \"The verified experiment history is in experiments.jsonl.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 00 — Mount Drive and verify the saved project\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n \"import os\\n\",\n \"from google.colab import drive\\n\",\n \"\\n\",\n \"DRIVE = Path(\\\"/content/drive\\\")\\n\",\n \"if not os.path.ismount(DRIVE):\\n\",\n \" if DRIVE.is_dir() and any(DRIVE.iterdir()):\\n\",\n \" raise RuntimeError(\\\"Unmounted /content/drive contains files; inspect before mounting\\\")\\n\",\n \" drive.mount(str(DRIVE), force_remount=False)\\n\",\n \"ROOT = DRIVE / \\\"MyDrive/OdiaGPT\\\"\\n\",\n \"if not os.path.ismount(DRIVE) or not (ROOT / \\\"status.md\\\").is_file():\\n\",\n \" raise RuntimeError(\\\"Saved OdiaGPT project is unavailable\\\")\\n\",\n \"print(\\\"Drive mounted:\\\", os.path.ismount(DRIVE))\\n\",\n \"print(\\\"Project:\\\", ROOT)\\n\",\n \"print((ROOT / \\\"status.md\\\").read_text(encoding=\\\"utf-8\\\"))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 01 — Environment inspection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import platform, shutil, sys, torch\\n\",\n \"print(\\\"Python:\\\", platform.python_version())\\n\",\n \"print(\\\"PyTorch:\\\", torch.__version__)\\n\",\n \"print(\\\"CUDA available:\\\", torch.cuda.is_available())\\n\",\n \"print(\\\"PyTorch CUDA:\\\", torch.version.cuda)\\n\",\n \"if torch.cuda.is_available():\\n\",\n \" print(\\\"GPU:\\\", torch.cuda.get_device_name(0))\\n\",\n \" print(\\\"GPU VRAM GiB:\\\", round(torch.cuda.get_device_properties(0).total_memory / 2**30, 2))\\n\",\n \"try:\\n\",\n \" import psutil\\n\",\n \" print(\\\"RAM total GiB:\\\", round(psutil.virtual_memory().total / 2**30, 2))\\n\",\n \"except ImportError:\\n\",\n \" pass\\n\",\n \"disk = shutil.disk_usage(\\\"/content\\\")\\n\",\n \"print(\\\"Disk /content free GiB:\\\", round(disk.free / 2**30, 2))\\n\",\n \"sys.path.insert(0, str(ROOT / \\\"src\\\"))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 02 — Project state and checkpoint pointers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"for relative in (\\\"memory.md\\\", \\\"TODO.md\\\", \\\"decisions.md\\\"):\\n\",\n \" path = ROOT / relative\\n\",\n \" print(\\\"\\\\n###\\\", relative)\\n\",\n \" print(path.read_text(encoding=\\\"utf-8\\\")[:5000])\\n\",\n \"for relative in (\\n\",\n \" \\\"checkpoints/nano-8k-v1/latest.json\\\",\\n\",\n \" \\\"checkpoints/pilot-30m-v1/latest.json\\\",\\n\",\n \" \\\"checkpoints/latest.json\\\",\\n\",\n \"):\\n\",\n \" path = ROOT / relative\\n\",\n \" print(relative, json.loads(path.read_text(encoding=\\\"utf-8\\\")) if path.is_file() else None)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 03 — Dependency and dataset access\\n\",\n \"\\n\",\n \"The saved data manifests identify the pinned Sangraha revision. Installing\\n\",\n \"packages or streaming new data is a separate, intentional step. Do not rerun\\n\",\n \"corpus builders during training.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import importlib.metadata as metadata\\n\",\n \"for package in (\\\"datasets\\\", \\\"sentencepiece\\\", \\\"PyYAML\\\", \\\"numpy\\\", \\\"torch\\\"):\\n\",\n \" try:\\n\",\n \" print(package, metadata.version(package))\\n\",\n \" except metadata.PackageNotFoundError:\\n\",\n \" print(package, \\\"MISSING\\\")\\n\",\n \"for relative in (\\n\",\n \" \\\"data/manifests/smoke_corpus.json\\\",\\n\",\n \" \\\"data/manifests/nano_corpus.json\\\",\\n\",\n \" \\\"data/manifests/pilot_corpus.json\\\",\\n\",\n \" \\\"data/tokenized/nano_8k.json\\\",\\n\",\n \" \\\"data/tokenized/pilot_8k.json\\\",\\n\",\n \"):\\n\",\n \" path = ROOT / relative\\n\",\n \" print(relative, \\\"present\\\" if path.is_file() else \\\"absent\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 04 — Cleaning, tokenizer, and model tests\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import os, subprocess, sys\\n\",\n \"env = os.environ.copy()\\n\",\n \"env[\\\"PYTHONPATH\\\"] = str(ROOT / \\\"src\\\") + os.pathsep + env.get(\\\"PYTHONPATH\\\", \\\"\\\")\\n\",\n \"result = subprocess.run(\\n\",\n \" [sys.executable, \\\"-m\\\", \\\"unittest\\\", \\\"discover\\\", \\\"-s\\\", \\\"tests\\\", \\\"-p\\\", \\\"test_*.py\\\", \\\"-v\\\"],\\n\",\n \" cwd=ROOT, env=env, check=False,\\n\",\n \")\\n\",\n \"print(\\\"Test process exit code:\\\", result.returncode)\\n\",\n \"if result.returncode:\\n\",\n \" raise RuntimeError(\\\"Project unit tests failed\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 05 — Optional data preparation\\n\",\n \"\\n\",\n \"The builders are staged and reuse existing verified outputs. They may stream\\n\",\n \"substantial data when a stage has not been prepared. Set RUN_PREPARATION to True\\n\",\n \"only when intentionally preparing the selected stage.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"RUN_PREPARATION = False\\n\",\n \"STAGE = \\\"smoke\\\" # smoke, nano, or pilot\\n\",\n \"if RUN_PREPARATION:\\n\",\n \" pipeline = {\\n\",\n \" \\\"smoke\\\": (\\\"build_corpus.py\\\", \\\"train_tokenizer.py\\\", \\\"prepare_tokens.py\\\"),\\n\",\n \" \\\"nano\\\": (\\\"build_nano_corpus.py\\\", \\\"train_nano_tokenizer.py\\\", \\\"prepare_nano_tokens.py\\\"),\\n\",\n \" \\\"pilot\\\": (\\\"build_pilot_corpus.py\\\", \\\"prepare_pilot_tokens.py\\\"),\\n\",\n \" }\\n\",\n \" if STAGE not in pipeline:\\n\",\n \" raise ValueError(f\\\"Unknown preparation stage: {STAGE}\\\")\\n\",\n \" for script in pipeline[STAGE]:\\n\",\n \" print(\\\"Running\\\", script, flush=True)\\n\",\n \" subprocess.run([sys.executable, \\\"-u\\\", str(ROOT / \\\"scripts\\\" / script)],\\n\",\n \" cwd=ROOT, env=env, check=True)\\n\",\n \"else:\\n\",\n \" print(\\\"Preparation skipped. Set RUN_PREPARATION=True for a selected stage.\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 06 — Inspect live training without starting a duplicate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"EXPERIMENT_ID = \\\"pilot-30m-v1\\\"\\n\",\n \"process_file = ROOT / \\\"runs\\\" / EXPERIMENT_ID / \\\"process.json\\\"\\n\",\n \"if process_file.is_file():\\n\",\n \" pid = int(json.loads(process_file.read_text(encoding=\\\"utf-8\\\"))[\\\"pid\\\"])\\n\",\n \" proc = Path(f\\\"/proc/{pid}\\\")\\n\",\n \" state = proc.joinpath(\\\"stat\\\").read_text().split()[2] if proc.exists() else \\\"exited\\\"\\n\",\n \" print(\\\"PID:\\\", pid, \\\"state:\\\", state)\\n\",\n \"else:\\n\",\n \" print(\\\"No process metadata for\\\", EXPERIMENT_ID)\\n\",\n \"console = ROOT / \\\"logs\\\" / f\\\"{EXPERIMENT_ID}.console.log\\\"\\n\",\n \"if console.is_file():\\n\",\n \" print(\\\"\\\\n\\\".join(console.read_text(encoding=\\\"utf-8\\\").splitlines()[-20:]))\\n\",\n \"pointer = ROOT / \\\"checkpoints\\\" / EXPERIMENT_ID / \\\"latest.json\\\"\\n\",\n \"print(\\\"Checkpoint:\\\", json.loads(pointer.read_text(encoding=\\\"utf-8\\\")) if pointer.is_file() else None)\\n\",\n \"print((ROOT / \\\"status.md\\\").read_text(encoding=\\\"utf-8\\\"))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 07 — Explicitly resume training\\n\",\n \"\\n\",\n \"Only enable when the relevant experiment is unfinished, CUDA is available,\\n\",\n \"and WORKFLOW 06 shows no active process. The training script resumes from its\\n\",\n \"hash-verified checkpoint and writes progress to Drive. This cell can run for\\n\",\n \"a long time.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"RUN_TRAINING = False\\n\",\n \"EXPERIMENT_ID = \\\"pilot-30m-v1\\\" # or nano-8k-v1\\n\",\n \"if RUN_TRAINING:\\n\",\n \" if not torch.cuda.is_available():\\n\",\n \" raise RuntimeError(\\\"CUDA is required for this training run\\\")\\n\",\n \" if EXPERIMENT_ID not in {\\\"pilot-30m-v1\\\", \\\"nano-8k-v1\\\"}:\\n\",\n \" raise ValueError(EXPERIMENT_ID)\\n\",\n \" metadata_path = ROOT / \\\"runs\\\" / EXPERIMENT_ID / \\\"process.json\\\"\\n\",\n \" if metadata_path.is_file():\\n\",\n \" pid = int(json.loads(metadata_path.read_text(encoding=\\\"utf-8\\\"))[\\\"pid\\\"])\\n\",\n \" proc = Path(f\\\"/proc/{pid}\\\")\\n\",\n \" if proc.exists() and proc.joinpath(\\\"stat\\\").read_text().split()[2] != \\\"Z\\\":\\n\",\n \" raise RuntimeError(f\\\"Existing training process is active: PID {pid}\\\")\\n\",\n \" script = \\\"train_pilot.py\\\" if EXPERIMENT_ID == \\\"pilot-30m-v1\\\" else \\\"train.py\\\"\\n\",\n \" subprocess.run([sys.executable, \\\"-u\\\", str(ROOT / \\\"scripts\\\" / script)],\\n\",\n \" cwd=ROOT, env=env, check=True)\\n\",\n \"else:\\n\",\n \" print(\\\"Training skipped. Inspect state, then set RUN_TRAINING=True to resume.\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## WORKFLOW 08 — Completed experiment records and reports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {},\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"records = [json.loads(line) for line in\\n\",\n \" (ROOT / \\\"experiments.jsonl\\\").read_text(encoding=\\\"utf-8\\\").splitlines()\\n\",\n \" if line.strip()]\\n\",\n \"print(\\\"Completed experiment records:\\\", len(records))\\n\",\n \"for record in records:\\n\",\n \" print(record.get(\\\"experiment_id\\\"), record.get(\\\"result\\\"),\\n\",\n \" \\\"steps\\\", record.get(\\\"training_steps\\\"),\\n\",\n \" \\\"validation loss\\\", record.get(\\\"validation_loss\\\"))\\n\",\n \"for name in (\\\"nano_experiment.md\\\", \\\"pilot_report.md\\\", \\\"evaluation_report.md\\\", \\\"final_report.md\\\"):\\n\",\n \" path = ROOT / \\\"reports\\\" / name\\n\",\n \" print(name, \\\"present\\\" if path.is_file() else \\\"pending\\\")\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n", "app.py": "\"\"\"\nOdiaGPT Interactive Gradio Web UI\nBuilt from Scratch - 100% PyTorch Primitives & From-Scratch Indic Tokenizer\n\"\"\"\n\nfrom pathlib import Path\nimport time\nimport json\nimport sys\n\nimport gradio as gr\nimport sentencepiece as spm\nimport torch\n\nROOT = Path(__file__).resolve().parent\nsys.path.insert(0, str(ROOT / \"src\"))\n\nfrom odiagpt.checkpoint import load_latest_checkpoint\nfrom odiagpt.config import ModelConfig\nfrom odiagpt.model import OdiaGPT\n\n# Global models and tokenizer cache\nMODELS = {}\nTOKENIZER = None\nDEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef get_tokenizer():\n global TOKENIZER\n if TOKENIZER is None:\n tok_info_path = ROOT / \"tokenizer/nano_8k.json\"\n tok_info = json.loads(tok_info_path.read_text(encoding=\"utf-8\"))\n TOKENIZER = spm.SentencePieceProcessor(model_file=tok_info[\"model_path\"])\n return TOKENIZER\n\n\ndef get_model(model_name: str):\n global MODELS\n if model_name in MODELS:\n return MODELS[model_name]\n\n if \"30M\" in model_name:\n exp_id = \"pilot-30m-v1\"\n else:\n exp_id = \"nano-8k-v1\"\n\n checkpoint = load_latest_checkpoint(ROOT, exp_id, map_location=\"cpu\")\n if checkpoint is None:\n raise RuntimeError(f\"Checkpoint for {exp_id} not found\")\n saved, _ = checkpoint\n config = ModelConfig.from_dict(saved[\"model_config\"])\n model = OdiaGPT(config).to(DEVICE).eval()\n model.load_state_dict(saved[\"model\"])\n MODELS[model_name] = (model, config, saved[\"step\"])\n return MODELS[model_name]\n\n\n@torch.inference_mode()\ndef generate_text(\n prompt: str,\n model_choice: str,\n max_new_tokens: int,\n temperature: float,\n top_k: int,\n top_p: float,\n repetition_penalty: float,\n seed: int,\n):\n if not prompt or not prompt.strip():\n return \"ଦୟାକରି ଏକ Odia prompt ଲେଖନ୍ତୁ (Please provide an Odia prompt).\", \"0 tokens\", \"0.0s\", \"0.0 tok/s\"\n\n sp = get_tokenizer()\n model, config, step = get_model(model_choice)\n\n prompt_ids = [sp.bos_id(), *sp.encode(prompt, out_type=int)]\n ids = torch.tensor(prompt_ids, dtype=torch.long, device=DEVICE).unsqueeze(0)\n \n if seed == -1:\n seed = torch.randint(0, 100000, (1,)).item()\n generator = torch.Generator(device=DEVICE).manual_seed(int(seed))\n\n start_time = time.monotonic()\n generated_count = 0\n\n for _ in range(max_new_tokens):\n context = ids[:, -config.context_length:]\n logits, _ = model(context)\n next_logits = logits[:, -1, :].float().clone()\n \n # Suppress special tokens\n next_logits[:, [sp.pad_id(), sp.unk_id(), sp.bos_id()]] = -torch.inf\n\n # Repetition penalty\n if repetition_penalty != 1.0:\n for token_id in set(ids[0].tolist()):\n if next_logits[0, token_id] > 0:\n next_logits[0, token_id] /= repetition_penalty\n else:\n next_logits[0, token_id] *= repetition_penalty\n\n if temperature == 0.0:\n next_id = next_logits.argmax(dim=-1, keepdim=True)\n else:\n next_logits /= max(temperature, 1e-4)\n if top_k > 0:\n k = min(top_k, next_logits.size(-1))\n boundary = torch.topk(next_logits, k=k, dim=-1).values[:, -1:]\n next_logits = next_logits.masked_fill(next_logits < boundary, -torch.inf)\n if top_p < 1.0:\n sorted_logits, sorted_indices = torch.sort(next_logits, descending=True, dim=-1)\n sorted_probs = torch.softmax(sorted_logits, dim=-1)\n cumulative = sorted_probs.cumsum(dim=-1)\n remove = cumulative - sorted_probs > top_p\n sorted_logits = sorted_logits.masked_fill(remove, -torch.inf)\n next_logits = torch.full_like(next_logits, -torch.inf).scatter(\n dim=-1, index=sorted_indices, src=sorted_logits\n )\n probs = torch.softmax(next_logits, dim=-1)\n next_id = torch.multinomial(probs, num_samples=1, generator=generator)\n\n ids = torch.cat((ids, next_id), dim=1)\n generated_count += 1\n if int(next_id.item()) == sp.eos_id():\n break\n\n elapsed = time.monotonic() - start_time\n tok_per_sec = generated_count / max(elapsed, 1e-5)\n\n new_ids = ids.squeeze(0).tolist()[len(prompt_ids):]\n decode_ids = [i for i in new_ids if i != sp.eos_id()]\n completion = sp.decode(decode_ids)\n full_text = prompt + completion\n\n stats_tokens = f\"{generated_count} tokens\"\n stats_time = f\"{elapsed:.2f}s\"\n stats_speed = f\"{tok_per_sec:.1f} tok/s\"\n\n return full_text, stats_tokens, stats_time, stats_speed\n\n\ntitle_html = \"\"\"\n
\n

ଓଡ଼ିଆଜିପିଟି (OdiaGPT)

\n

\n First Odia Decoder-Only Language Model Built Completely From Scratch\n

\n
\n ⚡ 31.5M Parameters\n 🧠 RoPE + RMSNorm + SwiGLU\n 📖 Scratch 8K SentencePiece\n 🚀 100% PyTorch Primitives\n
\n
\n\"\"\"\n\nwith gr.Blocks(title=\"OdiaGPT — Scratch Odia Transformer\") as demo:\n gr.HTML(title_html)\n\n with gr.Row():\n with gr.Column(scale=5):\n prompt_input = gr.Textbox(\n label=\"ପ୍ରବେଶ କରନ୍ତୁ (Enter Odia Prompt)\",\n placeholder=\"ଯେପରିକି: ଓଡ଼ିଶାର ଲୋକମାନେ / ଆଜି ସକାଳେ / ଜଗନ୍ନାଥ ମନ୍ଦିର...\",\n lines=3,\n value=\"ଓଡ଼ିଶାର ଲୋକମାନେ\",\n )\n generate_btn = gr.Button(\"ଉତ୍ପାଦନ କରନ୍ତୁ (Generate Completion) 🚀\", variant=\"primary\", size=\"lg\")\n\n with gr.Accordion(\"ଉଦାହରଣ (Example Prompts)\", open=True):\n gr.Examples(\n examples=[\n [\"ଓଡ଼ିଶାର ଲୋକମାନେ\"],\n [\"ଆଜି ସକାଳେ\"],\n [\"ଓଡ଼ିଆ ଭାଷାର\"],\n [\"ଜଗନ୍ନାଥ ମନ୍ଦିର ଏକ\"],\n [\"ଭୁବନେଶ୍ୱର ସହରରେ\"],\n ],\n inputs=[prompt_input],\n )\n\n with gr.Column(scale=5):\n output_text = gr.Textbox(\n label=\"ଫଳାଫଳ (Generated Output)\",\n lines=7,\n )\n with gr.Row():\n tokens_out = gr.Textbox(label=\"Tokens Generated\", interactive=False)\n time_out = gr.Textbox(label=\"Latency\", interactive=False)\n speed_out = gr.Textbox(label=\"Speed\", interactive=False)\n\n with gr.Accordion(\"⚙️ ନିୟନ୍ତ୍ରଣ ଓ ସେଟିଂସ (Generation Parameters)\", open=False):\n with gr.Row():\n model_selector = gr.Dropdown(\n label=\"ମଡେଲ (Select Model)\",\n choices=[\"OdiaGPT-30M (Flagship, 31.5M)\", \"OdiaGPT-Nano (5.5M)\"],\n value=\"OdiaGPT-30M (Flagship, 31.5M)\",\n )\n tokens_slider = gr.Slider(\n label=\"Max New Tokens\", minimum=16, maximum=256, step=16, value=64\n )\n with gr.Row():\n temp_slider = gr.Slider(\n label=\"Temperature (0.0=Greedy)\", minimum=0.0, maximum=1.5, step=0.05, value=0.8\n )\n topk_slider = gr.Slider(\n label=\"Top-K\", minimum=0, maximum=100, step=5, value=50\n )\n topp_slider = gr.Slider(\n label=\"Top-P (Nucleus)\", minimum=0.1, maximum=1.0, step=0.05, value=0.9\n )\n rep_slider = gr.Slider(\n label=\"Repetition Penalty\", minimum=1.0, maximum=2.0, step=0.05, value=1.15\n )\n with gr.Row():\n seed_input = gr.Number(\n label=\"Random Seed (-1 for random)\", value=42, precision=0\n )\n\n generate_btn.click(\n fn=generate_text,\n inputs=[\n prompt_input,\n model_selector,\n tokens_slider,\n temp_slider,\n topk_slider,\n topp_slider,\n rep_slider,\n seed_input,\n ],\n outputs=[output_text, tokens_out, time_out, speed_out],\n )\n\nif __name__ == \"__main__\":\n demo.launch(share=True)\n", "configs/smoke.yaml": "model:\n vocab_size: 4096\n context_length: 128\n n_layers: 2\n d_model: 128\n n_heads: 4\n d_ff: 384\n dropout: 0.0\n rope_base: 10000.0\n", "configs/tiny.yaml": "model:\n vocab_size: 4096\n context_length: 384\n n_layers: 6\n d_model: 384\n n_heads: 6\n d_ff: 1024\n dropout: 0.0\n rope_base: 10000.0\n", "configs/odia30m.yaml": "model:\n vocab_size: 8192\n context_length: 512\n n_layers: 8\n d_model: 512\n n_heads: 8\n d_ff: 1536\n dropout: 0.0\n rope_base: 10000.0\n attention_backend: sdpa\ntraining:\n seed: 3456\n learning_rate: 0.0003\n min_learning_rate: 0.00003\n weight_decay: 0.1\n beta1: 0.9\n beta2: 0.95\n gradient_clip: 1.0\n microbatch: 16\n gradient_accumulation: 2\n max_steps: 1800\n warmup_steps: 100\n eval_interval: 100\n checkpoint_interval: 100\n log_interval: 10\n eval_batches: 20\n precision: fp16\n", "configs/nano.yaml": "model:\n vocab_size: 8192\n context_length: 256\n n_layers: 4\n d_model: 256\n n_heads: 4\n d_ff: 768\n dropout: 0.0\n rope_base: 10000.0\ntraining:\n seed: 1234\n learning_rate: 0.0003\n min_learning_rate: 0.00003\n weight_decay: 0.1\n beta1: 0.9\n beta2: 0.95\n gradient_clip: 1.0\n microbatch: 8\n gradient_accumulation: 4\n max_steps: 1000\n warmup_steps: 100\n eval_interval: 100\n checkpoint_interval: 100\n log_interval: 10\n eval_batches: 20\n precision: fp16\n", "scripts/build_corpus.py": "\"\"\"Build or verify the small OdiaGPT cleaned corpus.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport sys\n\nPROJECT_ROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(PROJECT_ROOT / \"src\"))\n\nfrom odiagpt.data_prepare import build_smoke_corpus\n\n\nif __name__ == \"__main__\":\n manifest = build_smoke_corpus(PROJECT_ROOT)\n print(json.dumps(manifest, ensure_ascii=False, indent=2))\n", "scripts/train_tokenizer.py": "\"\"\"Train or verify the OdiaGPT smoke tokenizer.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport sys\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(ROOT / \"src\"))\n\nfrom odiagpt.tokenizer import train_smoke_tokenizer\n\n\nif __name__ == \"__main__\":\n result = train_smoke_tokenizer(ROOT)\n print(json.dumps(result, ensure_ascii=False, indent=2))\n", "scripts/prepare_tokens.py": "\"\"\"Prepare or verify smoke token binaries.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport sys\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(ROOT / \"src\"))\n\nfrom odiagpt.prepare_tokens import prepare_smoke_tokens\n\n\nif __name__ == \"__main__\":\n result = prepare_smoke_tokens(ROOT)\n print(json.dumps(result, ensure_ascii=False, indent=2))\n", "scripts/tiny_overfit.py": "\"\"\"Memorize one short Odia token sequence as a model/training sanity check.\"\"\"\n\nfrom datetime import datetime\nfrom hashlib import sha256\nfrom pathlib import Path\nfrom time import monotonic\nfrom zoneinfo import ZoneInfo\nimport json\nimport os\nimport random\nimport sys\nimport tempfile\n\nimport numpy as np\nimport torch\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(ROOT / \"src\"))\n\nfrom odiagpt.config import ModelConfig\nfrom odiagpt.model import OdiaGPT\nfrom odiagpt.prepare_tokens import hash_file\n\n\nEXPERIMENT_ID = \"tiny-overfit-smoke-v1\"\nMAX_STEPS = 300\nLEARNING_RATE = 0.002\nSEQUENCE_LENGTH = 32\nSEED = 173\n\n\ndef atomic_json(path: Path, payload: dict) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=path.parent,\n prefix=\".write_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n json.dump(payload, handle, ensure_ascii=False, indent=2)\n handle.write(\"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef update_status(step: int, loss: float | None, checkpoint: Path | None, note: str) -> None:\n stamp = datetime.now(ZoneInfo(\"Asia/Kolkata\")).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n (ROOT / \"status.md\").write_text(\n f\"# Current OdiaGPT status\\n\\n\"\n f\"- Timestamp: {stamp}\\n\"\n \"- Current phase: Tiny-overfit test\\n\"\n \"- Last successful cell: CELL 21 (CPU runtime inspected)\\n\"\n f\"- Latest runtime output: {note}\\n\"\n \"- Current GPU: None in MCP scratch runtime; CPU used for tiny overfit\\n\"\n \"- Dataset status: Smoke corpus and token binaries verified\\n\"\n \"- Tokenizer status: Smoke 4K model and tests verified\\n\"\n \"- Model status: Implemented; six CPU unit tests passed; CUDA test pending\\n\"\n f\"- Active experiment: {EXPERIMENT_ID}\\n\"\n f\"- Current training step: {step}\\n\"\n f\"- Latest train loss: {loss if loss is not None else 'None'}\\n\"\n \"- Latest validation loss: None (same-sequence overfit diagnostic)\\n\"\n f\"- Latest checkpoint: {checkpoint if checkpoint else 'None'}\\n\"\n \"- Blockers: GPU runtime control needs OAuth credentials for later experiments\\n\"\n \"- Exact next action: Continue tiny-overfit until strong loss reduction.\\n\",\n encoding=\"utf-8\",\n )\n\n\ndef save_checkpoint(\n model: OdiaGPT, optimizer: torch.optim.Optimizer, step: int,\n initial_loss: float, current_loss: float, config: ModelConfig, data_sha256: str,\n) -> Path:\n path = ROOT / \"checkpoints\" / f\"{EXPERIMENT_ID}.pt\"\n path.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(\n mode=\"wb\", dir=path.parent, prefix=\".checkpoint_\",\n suffix=\".tmp\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n torch.save({\n \"experiment_id\": EXPERIMENT_ID, \"step\": step,\n \"model\": model.state_dict(), \"optimizer\": optimizer.state_dict(),\n \"model_config\": config.to_dict(), \"data_sha256\": data_sha256,\n \"initial_loss\": initial_loss, \"current_loss\": current_loss,\n \"torch_rng_state\": torch.get_rng_state(),\n \"numpy_rng_state\": np.random.get_state(),\n \"python_rng_state\": random.getstate(),\n }, temporary)\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n digest = hash_file(path)\n atomic_json(ROOT / \"checkpoints/latest.json\", {\n \"experiment_id\": EXPERIMENT_ID, \"step\": step,\n \"path\": str(path), \"sha256\": digest,\n })\n return path\n\n\ndef main() -> None:\n torch.set_num_threads(min(4, os.cpu_count() or 1))\n torch.manual_seed(SEED)\n np.random.seed(SEED)\n random.seed(SEED)\n config = ModelConfig.from_yaml(ROOT / \"configs/smoke.yaml\")\n token_manifest = json.loads(\n (ROOT / \"data/tokenized/smoke_4k.json\").read_text(encoding=\"utf-8\")\n )\n if config.vocab_size != token_manifest[\"vocab_size\"]:\n raise RuntimeError(\"Model/tokenizer vocabulary mismatch\")\n train_entry = token_manifest[\"splits\"][\"train\"]\n train_path = Path(train_entry[\"path\"])\n data_hash = hash_file(train_path)\n if data_hash != train_entry[\"sha256\"]:\n raise RuntimeError(\"Training binary hash mismatch\")\n data = np.memmap(train_path, dtype=\" None:\n checkpoint = load_latest_checkpoint(ROOT, \"nano-8k-v1\", map_location=\"cpu\")\n if checkpoint is None:\n raise RuntimeError(\"Nano checkpoint not found\")\n saved, pointer = checkpoint\n config = ModelConfig.from_dict(saved[\"model_config\"])\n manifest = json.loads(\n (ROOT / \"data/tokenized/nano_8k.json\").read_text(encoding=\"utf-8\")\n )\n tokenizer_info = json.loads(\n (ROOT / \"tokenizer/nano_8k.json\").read_text(encoding=\"utf-8\")\n )\n if config.vocab_size != manifest[\"vocab_size\"]:\n raise RuntimeError(\"Checkpoint and tokenizer vocabulary mismatch\")\n if tokenizer_info[\"model_sha256\"] != manifest[\"tokenizer_sha256\"]:\n raise RuntimeError(\"Tokenizer manifest mismatch\")\n if hash_file(Path(tokenizer_info[\"model_path\"])) != tokenizer_info[\"model_sha256\"]:\n raise RuntimeError(\"Tokenizer model hash mismatch\")\n sp = spm.SentencePieceProcessor(model_file=tokenizer_info[\"model_path\"])\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = OdiaGPT(config).to(device).eval()\n model.load_state_dict(saved[\"model\"])\n prompts = [\n \"ଓଡ଼ିଶାର ଲୋକମାନେ\",\n \"ଆଜି ସକାଳେ\",\n \"ଓଡ଼ିଆ ଭାଷାର\",\n ]\n modes = [\n {\"name\": \"greedy\", \"temperature\": 0.0, \"top_k\": None, \"top_p\": None},\n {\"name\": \"sample\", \"temperature\": 0.8, \"top_k\": 50, \"top_p\": 0.9},\n ]\n output = ROOT / \"generations/nano-8k-v1-step1000.jsonl\"\n if output.exists():\n raise FileExistsError(f\"Generation artifact already exists: {output}\")\n output.parent.mkdir(parents=True, exist_ok=True)\n rows = []\n for prompt_index, prompt in enumerate(prompts):\n prompt_ids = [sp.bos_id(), *sp.encode(prompt, out_type=int)]\n for mode in modes:\n all_ids = generate_tokens(\n model, prompt_ids, max_new_tokens=64,\n eos_id=sp.eos_id(), pad_id=sp.pad_id(),\n unk_id=sp.unk_id(), bos_id=sp.bos_id(),\n temperature=mode[\"temperature\"],\n top_k=mode[\"top_k\"], top_p=mode[\"top_p\"],\n seed=2026 + prompt_index,\n )\n new_ids = all_ids[len(prompt_ids):]\n decode_ids = [i for i in new_ids if i != sp.eos_id()]\n completion = sp.decode(decode_ids)\n row = {\n \"experiment_id\": \"nano-8k-v1\",\n \"checkpoint_step\": saved[\"step\"],\n \"checkpoint_sha256\": pointer[\"sha256\"],\n \"prompt\": prompt,\n \"mode\": mode[\"name\"],\n \"temperature\": mode[\"temperature\"],\n \"top_k\": mode[\"top_k\"],\n \"top_p\": mode[\"top_p\"],\n \"seed\": 2026 + prompt_index,\n \"prompt_token_count\": len(prompt_ids),\n \"generated_token_count\": len(new_ids),\n \"ended_on_eos\": bool(new_ids and new_ids[-1] == sp.eos_id()),\n \"completion\": completion,\n }\n rows.append(row)\n print(\n f\"{mode['name']} | {prompt} | tokens={len(new_ids)} | \"\n f\"completion={completion[:250]!r}\",\n flush=True,\n )\n with output.open(\"x\", encoding=\"utf-8\") as handle:\n for row in rows:\n handle.write(json.dumps(row, ensure_ascii=False) + \"\\n\")\n print(\"Saved generations:\", output)\n print(\"Generation rows:\", len(rows))\n\n\nif __name__ == \"__main__\":\n main()\n", "scripts/build_pilot_corpus.py": "\"\"\"Build or verify the resumable pilot Odia corpus.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport sys\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(ROOT / \"src\"))\nfrom odiagpt.pilot_data import build_pilot_corpus\n\nif __name__ == \"__main__\":\n print(json.dumps(build_pilot_corpus(ROOT), ensure_ascii=False, indent=2))\n", "scripts/prepare_pilot_tokens.py": "\"\"\"Prepare or verify pilot token binaries.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport sys\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(ROOT / \"src\"))\nfrom odiagpt.prepare_pilot_tokens import prepare_pilot_tokens\n\nif __name__ == \"__main__\":\n print(json.dumps(prepare_pilot_tokens(ROOT), ensure_ascii=False, indent=2))\n", "scripts/train_pilot.py": "\"\"\"Run or resume the OdiaGPT-30M pilot experiment.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport sys\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\nsys.path.insert(0, str(ROOT / \"src\"))\nfrom odiagpt.pilot_training import train_pilot\n\nif __name__ == \"__main__\":\n print(json.dumps(train_pilot(ROOT), ensure_ascii=False, indent=2))\n", "scripts/make_notebook.py": "\"\"\"Generate the OdiaGPT Colab orchestration notebook from persistent source.\"\"\"\n\nfrom pathlib import Path\nimport json\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\n\n\ndef markdown(body: str) -> dict:\n return {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": body.splitlines(keepends=True)}\n\n\ndef code(body: str) -> dict:\n return {\n \"cell_type\": \"code\", \"metadata\": {}, \"execution_count\": None,\n \"outputs\": [], \"source\": body.splitlines(keepends=True),\n }\n\n\ndef build_notebook() -> dict:\n cells = [\n markdown(\"\"\"# OdiaGPT from scratch\n\nThis notebook orchestrates the persistent Python project on Google Drive. Run\nthe reconnect and inspection cells first after every Colab runtime change.\nTraining cells are guarded to avoid starting a second long run accidentally.\nModel weights and both SentencePiece tokenizers are trained from scratch.\nThe verified experiment history is in experiments.jsonl.\n\"\"\"),\n markdown(\"## WORKFLOW 00 — Mount Drive and verify the saved project\"),\n code(\"\"\"from pathlib import Path\nimport os\nfrom google.colab import drive\n\nDRIVE = Path(\"/content/drive\")\nif not os.path.ismount(DRIVE):\n if DRIVE.is_dir() and any(DRIVE.iterdir()):\n raise RuntimeError(\"Unmounted /content/drive contains files; inspect before mounting\")\n drive.mount(str(DRIVE), force_remount=False)\nROOT = DRIVE / \"MyDrive/OdiaGPT\"\nif not os.path.ismount(DRIVE) or not (ROOT / \"status.md\").is_file():\n raise RuntimeError(\"Saved OdiaGPT project is unavailable\")\nprint(\"Drive mounted:\", os.path.ismount(DRIVE))\nprint(\"Project:\", ROOT)\nprint((ROOT / \"status.md\").read_text(encoding=\"utf-8\"))\n\"\"\"),\n markdown(\"## WORKFLOW 01 — Environment inspection\"),\n code(\"\"\"import platform, shutil, sys, torch\nprint(\"Python:\", platform.python_version())\nprint(\"PyTorch:\", torch.__version__)\nprint(\"CUDA available:\", torch.cuda.is_available())\nprint(\"PyTorch CUDA:\", torch.version.cuda)\nif torch.cuda.is_available():\n print(\"GPU:\", torch.cuda.get_device_name(0))\n print(\"GPU VRAM GiB:\", round(torch.cuda.get_device_properties(0).total_memory / 2**30, 2))\ntry:\n import psutil\n print(\"RAM total GiB:\", round(psutil.virtual_memory().total / 2**30, 2))\nexcept ImportError:\n pass\ndisk = shutil.disk_usage(\"/content\")\nprint(\"Disk /content free GiB:\", round(disk.free / 2**30, 2))\nsys.path.insert(0, str(ROOT / \"src\"))\n\"\"\"),\n markdown(\"## WORKFLOW 02 — Project state and checkpoint pointers\"),\n code(\"\"\"import json\nfor relative in (\"memory.md\", \"TODO.md\", \"decisions.md\"):\n path = ROOT / relative\n print(\"\\\\n###\", relative)\n print(path.read_text(encoding=\"utf-8\")[:5000])\nfor relative in (\n \"checkpoints/nano-8k-v1/latest.json\",\n \"checkpoints/pilot-30m-v1/latest.json\",\n \"checkpoints/latest.json\",\n):\n path = ROOT / relative\n print(relative, json.loads(path.read_text(encoding=\"utf-8\")) if path.is_file() else None)\n\"\"\"),\n markdown(\"\"\"## WORKFLOW 03 — Dependency and dataset access\n\nThe saved data manifests identify the pinned Sangraha revision. Installing\npackages or streaming new data is a separate, intentional step. Do not rerun\ncorpus builders during training.\n\"\"\"),\n code(\"\"\"import importlib.metadata as metadata\nfor package in (\"datasets\", \"sentencepiece\", \"PyYAML\", \"numpy\", \"torch\"):\n try:\n print(package, metadata.version(package))\n except metadata.PackageNotFoundError:\n print(package, \"MISSING\")\nfor relative in (\n \"data/manifests/smoke_corpus.json\",\n \"data/manifests/nano_corpus.json\",\n \"data/manifests/pilot_corpus.json\",\n \"data/tokenized/nano_8k.json\",\n \"data/tokenized/pilot_8k.json\",\n):\n path = ROOT / relative\n print(relative, \"present\" if path.is_file() else \"absent\")\n\"\"\"),\n markdown(\"## WORKFLOW 04 — Cleaning, tokenizer, and model tests\"),\n code(\"\"\"import os, subprocess, sys\nenv = os.environ.copy()\nenv[\"PYTHONPATH\"] = str(ROOT / \"src\") + os.pathsep + env.get(\"PYTHONPATH\", \"\")\nresult = subprocess.run(\n [sys.executable, \"-m\", \"unittest\", \"discover\", \"-s\", \"tests\", \"-p\", \"test_*.py\", \"-v\"],\n cwd=ROOT, env=env, check=False,\n)\nprint(\"Test process exit code:\", result.returncode)\nif result.returncode:\n raise RuntimeError(\"Project unit tests failed\")\n\"\"\"),\n markdown(\"\"\"## WORKFLOW 05 — Optional data preparation\n\nThe builders are staged and reuse existing verified outputs. They may stream\nsubstantial data when a stage has not been prepared. Set RUN_PREPARATION to True\nonly when intentionally preparing the selected stage.\n\"\"\"),\n code(\"\"\"RUN_PREPARATION = False\nSTAGE = \"smoke\" # smoke, nano, or pilot\nif RUN_PREPARATION:\n pipeline = {\n \"smoke\": (\"build_corpus.py\", \"train_tokenizer.py\", \"prepare_tokens.py\"),\n \"nano\": (\"build_nano_corpus.py\", \"train_nano_tokenizer.py\", \"prepare_nano_tokens.py\"),\n \"pilot\": (\"build_pilot_corpus.py\", \"prepare_pilot_tokens.py\"),\n }\n if STAGE not in pipeline:\n raise ValueError(f\"Unknown preparation stage: {STAGE}\")\n for script in pipeline[STAGE]:\n print(\"Running\", script, flush=True)\n subprocess.run([sys.executable, \"-u\", str(ROOT / \"scripts\" / script)],\n cwd=ROOT, env=env, check=True)\nelse:\n print(\"Preparation skipped. Set RUN_PREPARATION=True for a selected stage.\")\n\"\"\"),\n markdown(\"## WORKFLOW 06 — Inspect live training without starting a duplicate\"),\n code(\"\"\"import json\nEXPERIMENT_ID = \"pilot-30m-v1\"\nprocess_file = ROOT / \"runs\" / EXPERIMENT_ID / \"process.json\"\nif process_file.is_file():\n pid = int(json.loads(process_file.read_text(encoding=\"utf-8\"))[\"pid\"])\n proc = Path(f\"/proc/{pid}\")\n state = proc.joinpath(\"stat\").read_text().split()[2] if proc.exists() else \"exited\"\n print(\"PID:\", pid, \"state:\", state)\nelse:\n print(\"No process metadata for\", EXPERIMENT_ID)\nconsole = ROOT / \"logs\" / f\"{EXPERIMENT_ID}.console.log\"\nif console.is_file():\n print(\"\\\\n\".join(console.read_text(encoding=\"utf-8\").splitlines()[-20:]))\npointer = ROOT / \"checkpoints\" / EXPERIMENT_ID / \"latest.json\"\nprint(\"Checkpoint:\", json.loads(pointer.read_text(encoding=\"utf-8\")) if pointer.is_file() else None)\nprint((ROOT / \"status.md\").read_text(encoding=\"utf-8\"))\n\"\"\"),\n markdown(\"\"\"## WORKFLOW 07 — Explicitly resume training\n\nOnly enable when the relevant experiment is unfinished, CUDA is available,\nand WORKFLOW 06 shows no active process. The training script resumes from its\nhash-verified checkpoint and writes progress to Drive. This cell can run for\na long time.\n\"\"\"),\n code(\"\"\"RUN_TRAINING = False\nEXPERIMENT_ID = \"pilot-30m-v1\" # or nano-8k-v1\nif RUN_TRAINING:\n if not torch.cuda.is_available():\n raise RuntimeError(\"CUDA is required for this training run\")\n if EXPERIMENT_ID not in {\"pilot-30m-v1\", \"nano-8k-v1\"}:\n raise ValueError(EXPERIMENT_ID)\n metadata_path = ROOT / \"runs\" / EXPERIMENT_ID / \"process.json\"\n if metadata_path.is_file():\n pid = int(json.loads(metadata_path.read_text(encoding=\"utf-8\"))[\"pid\"])\n proc = Path(f\"/proc/{pid}\")\n if proc.exists() and proc.joinpath(\"stat\").read_text().split()[2] != \"Z\":\n raise RuntimeError(f\"Existing training process is active: PID {pid}\")\n script = \"train_pilot.py\" if EXPERIMENT_ID == \"pilot-30m-v1\" else \"train.py\"\n subprocess.run([sys.executable, \"-u\", str(ROOT / \"scripts\" / script)],\n cwd=ROOT, env=env, check=True)\nelse:\n print(\"Training skipped. Inspect state, then set RUN_TRAINING=True to resume.\")\n\"\"\"),\n markdown(\"## WORKFLOW 08 — Completed experiment records and reports\"),\n code(\"\"\"records = [json.loads(line) for line in\n (ROOT / \"experiments.jsonl\").read_text(encoding=\"utf-8\").splitlines()\n if line.strip()]\nprint(\"Completed experiment records:\", len(records))\nfor record in records:\n print(record.get(\"experiment_id\"), record.get(\"result\"),\n \"steps\", record.get(\"training_steps\"),\n \"validation loss\", record.get(\"validation_loss\"))\nfor name in (\"nano_experiment.md\", \"pilot_report.md\", \"evaluation_report.md\", \"final_report.md\"):\n path = ROOT / \"reports\" / name\n print(name, \"present\" if path.is_file() else \"pending\")\n\"\"\"),\n ]\n return {\n \"cells\": cells,\n \"metadata\": {\n \"colab\": {\"provenance\": []},\n \"kernelspec\": {\"display_name\": \"Python 3\", \"name\": \"python3\"},\n \"language_info\": {\"name\": \"python\"},\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5,\n }\n\n\nif __name__ == \"__main__\":\n output = ROOT / \"OdiaGPT_From_Scratch.ipynb\"\n notebook = build_notebook()\n encoded = json.dumps(notebook, ensure_ascii=False, indent=1) + \"\\n\"\n if output.exists() and output.read_text(encoding=\"utf-8\") != encoded:\n raise RuntimeError(f\"Existing notebook differs; refusing to overwrite: {output}\")\n output.write_text(encoded, encoding=\"utf-8\")\n print(\"Notebook:\", output)\n print(\"Cells:\", len(notebook[\"cells\"]))\n", "scripts/upload_to_hf.py": "\"\"\"\nUpload exported OdiaGPT-30M to the Hugging Face Hub.\nUsage:\n python scripts/upload_to_hf.py --repo-id /OdiaGPT-30M --token \n\"\"\"\n\nimport argparse\nfrom pathlib import Path\nfrom huggingface_hub import HfApi, create_repo\n\nROOT = Path(__file__).resolve().parent.parent\nEXPORT_DIR = ROOT / \"export/hf_odiagpt_30m\"\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Upload OdiaGPT to Hugging Face Hub\")\n parser.add_argument(\"--repo-id\", type=str, required=True, help=\"HF repo ID, e.g. username/OdiaGPT-30M\")\n parser.add_argument(\"--token\", type=str, required=False, default=None, help=\"Hugging Face User Access Token\")\n parser.add_argument(\"--private\", action=\"store_true\", help=\"Make repository private\")\n args = parser.parse_args()\n\n api = HfApi(token=args.token)\n print(f\"Creating repository {args.repo_id} (if not already exists)...\")\n create_repo(args.repo_id, token=args.token, private=args.private, exist_ok=True)\n\n print(f\"Uploading files from {EXPORT_DIR} to {args.repo_id}...\")\n api.upload_folder(\n folder_path=str(EXPORT_DIR),\n repo_id=args.repo_id,\n repo_type=\"model\",\n )\n print(f\"\\nSuccessfully uploaded OdiaGPT-30M to Hugging Face!\")\n print(f\"Model URL: https://huggingface.co/{args.repo_id}\")\n\n\nif __name__ == \"__main__\":\n main()\n", "tests/test_cleaning.py": "\"\"\"Focused tests for conservative Odia document cleaning.\"\"\"\n\nimport unittest\nimport unicodedata\n\nfrom odiagpt.cleaning import CleaningConfig, clean_document, exact_fingerprint\nfrom odiagpt.unicode_utils import (\n is_odia_letter_or_mark,\n normalize_text,\n text_stats,\n)\n\n\nclass CleaningTests(unittest.TestCase):\n def test_newlines_controls_and_whitespace(self):\n raw = \" \\r\\nକି\\x00\\tପଦ । ୧୨୩\\r\\n\\r\\n\\r\\n\"\n self.assertEqual(normalize_text(raw), \"କି ପଦ । ୧୨୩\")\n\n def test_combining_mark_is_preserved(self):\n text = \"କି\"\n self.assertEqual(normalize_text(text), text)\n self.assertTrue(is_odia_letter_or_mark(text[0]))\n self.assertTrue(is_odia_letter_or_mark(text[1]))\n self.assertEqual(text_stats(text).odia_letter_mark_count, 2)\n\n def test_nfc_normalization(self):\n self.assertEqual(normalize_text(\"e\\u0301\"), unicodedata.normalize(\"NFC\", \"e\\u0301\"))\n\n def test_punctuation_digits_english_and_url_survive(self):\n text = \"ଓଡ଼ିଆ ୧୨୩, English 2026! https://example.com/path\"\n cleaned = normalize_text(text)\n for fragment in (\"୧୨୩,\", \"English 2026!\", \"https://example.com/path\"):\n self.assertIn(fragment, cleaned)\n\n def test_script_ratio_ignores_digits_and_punctuation(self):\n stats = text_stats(\"କି 123!\")\n self.assertEqual(stats.odia_letter_mark_count, 2)\n self.assertEqual(stats.letter_mark_count, 2)\n self.assertEqual(stats.odia_script_ratio, 1.0)\n self.assertEqual(text_stats(\"English 123\").odia_script_ratio, 0.0)\n\n def test_code_mixed_document_can_pass(self):\n raw = \"ଓଡ଼ିଆ ଭାଷା ଏବଂ English words 123. \" * 4\n result = clean_document(raw)\n self.assertTrue(result.accepted, result.rejection_reason)\n self.assertIn(\"English words 123.\", result.text)\n\n def test_empty_document_rejected(self):\n self.assertEqual(clean_document(\"\\x00 \\r\").rejection_reason, \"empty\")\n\n def test_short_document_rejected(self):\n self.assertEqual(clean_document(\"ଓଡ଼ିଆ\").rejection_reason, \"too_short\")\n\n def test_too_few_odia_characters_rejected(self):\n raw = \"କି \" + \"English text \" * 10\n self.assertEqual(\n clean_document(raw).rejection_reason,\n \"few_odia_characters\",\n )\n\n def test_low_odia_ratio_rejected(self):\n raw = \"ଓଡ଼ିଆ \" * 5 + \"English \" * 40\n self.assertEqual(clean_document(raw).rejection_reason, \"low_odia_ratio\")\n\n def test_repeated_lines_rejected(self):\n line = \"ଓଡ଼ିଆ ଭାଷାର ଏହା ଏକ ପୂର୍ଣ୍ଣ ବାକ୍ୟ ।\"\n raw = \"\\n\".join([line] * 6)\n self.assertEqual(clean_document(raw).rejection_reason, \"repeated_lines\")\n\n def test_exact_fingerprint_uses_cleaned_text(self):\n left = exact_fingerprint(normalize_text(\"ଓଡ଼ିଆ ଭାଷା\"))\n right = exact_fingerprint(normalize_text(\"ଓଡ଼ିଆ ଭାଷା\"))\n self.assertEqual(left, right)\n\n def test_invalid_config_rejected(self):\n with self.assertRaises(ValueError):\n CleaningConfig(min_odia_script_ratio=1.5)\n with self.assertRaises(ValueError):\n CleaningConfig(min_chars=-1)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "tests/test_tokenizer.py": "\"\"\"Tests for the from-scratch OdiaGPT smoke tokenizer.\"\"\"\n\nfrom pathlib import Path\nimport unittest\n\nfrom odiagpt.tokenizer import load_tokenizer\n\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\n\n\nclass TokenizerTests(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.sp = load_tokenizer(ROOT)\n\n def test_vocabulary_and_special_ids(self):\n self.assertEqual(self.sp.get_piece_size(), 4096)\n self.assertEqual(\n (self.sp.pad_id(), self.sp.unk_id(), self.sp.bos_id(), self.sp.eos_id()),\n (0, 1, 2, 3),\n )\n\n def test_empty_input(self):\n self.assertEqual(self.sp.encode(\"\", out_type=int), [])\n self.assertEqual(self.sp.decode([]), \"\")\n\n def test_round_trips(self):\n samples = [\n \"ଓଡ଼ିଆ ଭାଷାରେ ଏହା ଏକ ବାକ୍ୟ।\",\n \"କି କୁ କେ କୌ ଙ୍କ ଣ୍ଡ\",\n \"୧୨୩, 2026! ମୂଲ୍ୟ ₹୫୦।\",\n \"ଓଡ଼ିଆ and English code-mixed text.\",\n \"https://example.com/odia?q=123\",\n \"ଓଡ଼ିଆ 🙂 rare 漢字\",\n ]\n for sample in samples:\n with self.subTest(sample=sample):\n ids = self.sp.encode(sample, out_type=int)\n self.assertTrue(ids)\n self.assertTrue(all(0 <= index < 4096 for index in ids))\n self.assertEqual(self.sp.decode(ids), sample)\n\n def test_no_unknowns_for_samples(self):\n for sample in (\"ଓଡ଼ିଆ ଭାଷା\", \"rare 🙂 漢字\"):\n ids = self.sp.encode(sample, out_type=int)\n self.assertNotIn(self.sp.unk_id(), ids)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "tests/test_model.py": "\"\"\"Correctness tests for the randomly initialized OdiaGPT decoder.\"\"\"\n\nfrom pathlib import Path\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom odiagpt.config import ModelConfig\nfrom odiagpt.model import OdiaGPT\nfrom odiagpt.rope import RotaryEmbedding, apply_rope\n\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\n\n\nclass ModelTests(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n torch.manual_seed(1234)\n cls.config = ModelConfig.from_yaml(ROOT / \"configs/smoke.yaml\")\n\n def test_config_and_parameter_count(self):\n for name in (\"smoke\", \"nano\", \"tiny\", \"odia30m\"):\n config = ModelConfig.from_yaml(ROOT / \"configs\" / f\"{name}.yaml\")\n model = OdiaGPT(config)\n d = config.d_model\n expected = (\n config.vocab_size * d\n + config.n_layers * (4 * d * d + 3 * d * config.d_ff + 2 * d)\n + d\n )\n self.assertEqual(model.parameter_count(), expected)\n self.assertIs(model.lm_head.weight, model.token_embedding.weight)\n print(f\"{name} parameters: {expected:,}\")\n del model\n\n def test_forward_loss_and_backward(self):\n model = OdiaGPT(self.config)\n tokens = torch.randint(0, self.config.vocab_size, (2, 16), dtype=torch.long)\n logits, loss = model(tokens[:, :-1], tokens[:, 1:])\n self.assertEqual(logits.shape, (2, 15, self.config.vocab_size))\n self.assertEqual(loss.ndim, 0)\n self.assertTrue(torch.isfinite(loss).item())\n loss.backward()\n gradients = [p.grad for p in model.parameters() if p.requires_grad]\n self.assertTrue(all(g is not None and torch.isfinite(g).all().item() for g in gradients))\n\n def test_causality(self):\n model = OdiaGPT(self.config).eval()\n a = torch.randint(0, self.config.vocab_size, (2, 12))\n b = a.clone()\n b[:, 6:] = (b[:, 6:] + 13) % self.config.vocab_size\n with torch.no_grad():\n first, _ = model(a)\n changed, _ = model(b)\n self.assertTrue(torch.allclose(first[:, :6], changed[:, :6], atol=1e-6, rtol=1e-6))\n self.assertFalse(torch.allclose(first[:, 6:], changed[:, 6:]))\n\n def test_rope_shape_device_dtype(self):\n embedding = RotaryEmbedding(32)\n x = torch.randn(2, 4, 7, 32)\n cos, sin = embedding(7, x.device, x.dtype)\n result = apply_rope(x, cos, sin)\n self.assertEqual(result.shape, x.shape)\n self.assertEqual(result.dtype, x.dtype)\n self.assertEqual(result.device, x.device)\n self.assertTrue(torch.allclose(result[:, :, 0], x[:, :, 0], atol=1e-6))\n self.assertTrue(torch.isfinite(result).all().item())\n\n def test_save_load_logits_match(self):\n model = OdiaGPT(self.config).eval()\n tokens = torch.randint(0, self.config.vocab_size, (1, 9))\n with torch.no_grad():\n original, _ = model(tokens)\n with tempfile.TemporaryDirectory() as directory:\n path = Path(directory) / \"weights.pt\"\n torch.save(model.state_dict(), path)\n restored = OdiaGPT(self.config).eval()\n restored.load_state_dict(torch.load(path, map_location=\"cpu\", weights_only=True))\n with torch.no_grad():\n result, _ = restored(tokens)\n self.assertTrue(torch.equal(original, result))\n\n def test_short_greedy_generation(self):\n model = OdiaGPT(self.config).eval()\n ids = torch.tensor([[2, 42, 73]], dtype=torch.long)\n with torch.no_grad():\n for _ in range(3):\n logits, _ = model(ids[:, -self.config.context_length:])\n next_id = logits[:, -1].argmax(dim=-1, keepdim=True)\n ids = torch.cat((ids, next_id), dim=1)\n self.assertEqual(ids.shape, (1, 6))\n self.assertTrue(((ids >= 0) & (ids < self.config.vocab_size)).all().item())\n\n @unittest.skipUnless(torch.cuda.is_available(), \"CUDA unavailable\")\n def test_cuda_forward_backward(self):\n device = torch.device(\"cuda\")\n model = OdiaGPT(self.config).to(device)\n tokens = torch.randint(0, self.config.vocab_size, (2, 9), device=device)\n logits, loss = model(tokens[:, :-1], tokens[:, 1:])\n self.assertEqual(logits.device.type, \"cuda\")\n self.assertTrue(torch.isfinite(loss).item())\n loss.backward()\n self.assertTrue(torch.isfinite(model.token_embedding.weight.grad).all().item())\n print(\"CUDA device:\", torch.cuda.get_device_name(device))\n print(\"BF16 supported:\", torch.cuda.is_bf16_supported())\n", "tests/test_nano_tokenizer.py": "\"\"\"Tests for the from-scratch nano 8K SentencePiece tokenizer.\"\"\"\n\nfrom pathlib import Path\nimport unittest\n\nimport sentencepiece as spm\n\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\n\n\nclass NanoTokenizerTests(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.sp = spm.SentencePieceProcessor(\n model_file=str(ROOT / \"tokenizer/nano_8k.model\")\n )\n\n def test_vocabulary_and_special_ids(self):\n self.assertEqual(self.sp.get_piece_size(), 8192)\n self.assertEqual(\n (self.sp.pad_id(), self.sp.unk_id(), self.sp.bos_id(), self.sp.eos_id()),\n (0, 1, 2, 3),\n )\n\n def test_exact_roundtrips(self):\n samples = [\n \"ଓଡ଼ିଆ ଭାଷାରେ ଏହା ଏକ ବାକ୍ୟ।\",\n \"କି କୁ କେ କୌ ଙ୍କ ଣ୍ଡ\",\n \"୧୨୩, 2026! ମୂଲ୍ୟ ₹୫୦।\",\n \"ଓଡ଼ିଆ and English code-mixed text.\",\n \"https://example.org/ଓଡ଼ିଆ?q=୧\",\n \"ଦୁର୍ଲଭ ♞ 字 ଚିହ୍ନ\",\n \"ପ୍ରଥମ ଧାଡ଼ି\\nଦ୍ୱିତୀୟ ଧାଡ଼ି\",\n \"\",\n ]\n for sample in samples:\n with self.subTest(sample=sample):\n ids = self.sp.encode(sample, out_type=int)\n self.assertEqual(self.sp.decode(ids), sample)\n self.assertNotIn(self.sp.unk_id(), ids)\n\n def test_empty_input(self):\n self.assertEqual(self.sp.encode(\"\", out_type=int), [])\n\n def test_token_ids_in_range(self):\n ids = self.sp.encode(\"ଓଡ଼ିଆ ଭାଷା ୧୨୩ ABC\", out_type=int)\n self.assertTrue(all(0 <= i < 8192 for i in ids))\n", "tests/test_generation.py": "\"\"\"Tests for greedy, temperature, top-k, and top-p generation.\"\"\"\n\nfrom pathlib import Path\nimport unittest\n\nimport torch\n\nfrom odiagpt.config import ModelConfig\nfrom odiagpt.generation import generate_tokens\nfrom odiagpt.model import OdiaGPT\n\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\n\n\nclass GenerationTests(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n torch.manual_seed(91)\n config = ModelConfig.from_yaml(ROOT / \"configs/smoke.yaml\")\n cls.model = OdiaGPT(config).eval()\n\n def _generate(self, **kwargs):\n return generate_tokens(\n self.model, [2, 17, 24], max_new_tokens=8,\n eos_id=3, pad_id=0, unk_id=1, bos_id=2,\n **kwargs,\n )\n\n def test_greedy_executes(self):\n ids = self._generate(temperature=0)\n self.assertGreater(len(ids), 3)\n self.assertLessEqual(len(ids), 11)\n self.assertTrue(all(0 <= i < self.model.config.vocab_size for i in ids))\n\n def test_sample_reproducible(self):\n first = self._generate(temperature=0.8, top_k=50, top_p=0.9, seed=77)\n second = self._generate(temperature=0.8, top_k=50, top_p=0.9, seed=77)\n self.assertEqual(first, second)\n\n def test_modes_and_limits(self):\n for kwargs in (\n {\"temperature\": 1.0, \"seed\": 1},\n {\"temperature\": 0.7, \"top_k\": 10, \"seed\": 1},\n {\"temperature\": 0.9, \"top_p\": 0.8, \"seed\": 1},\n ):\n with self.subTest(kwargs=kwargs):\n ids = self._generate(**kwargs)\n self.assertTrue(all(i not in (0, 1, 2) for i in ids[3:]))\n\n def test_invalid_parameters(self):\n with self.assertRaises(ValueError):\n self._generate(temperature=-1)\n with self.assertRaises(ValueError):\n self._generate(top_k=0)\n with self.assertRaises(ValueError):\n self._generate(top_p=0)\n", "tests/test_sdpa.py": "\"\"\"Check PyTorch SDPA against our manual causal attention.\"\"\"\n\nfrom dataclasses import replace\nfrom pathlib import Path\nimport unittest\n\nimport torch\n\nfrom odiagpt.config import ModelConfig\nfrom odiagpt.model import OdiaGPT\n\n\nROOT = Path(\"/content/drive/MyDrive/OdiaGPT\")\n\n\nclass SDPAAttentionTests(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.manual_config = ModelConfig.from_yaml(ROOT / \"configs/smoke.yaml\")\n cls.sdpa_config = replace(cls.manual_config, attention_backend=\"sdpa\")\n\n def _pair(self, device):\n torch.manual_seed(556)\n manual = OdiaGPT(self.manual_config).to(device).eval()\n sdpa = OdiaGPT(self.sdpa_config).to(device).eval()\n sdpa.load_state_dict(manual.state_dict())\n return manual, sdpa\n\n def test_cpu_outputs_and_causality(self):\n manual, sdpa = self._pair(torch.device(\"cpu\"))\n tokens = torch.randint(0, self.manual_config.vocab_size, (2, 12))\n with torch.no_grad():\n a, _ = manual(tokens)\n b, _ = sdpa(tokens)\n difference = (a - b).abs().max().item()\n print(\"CPU manual/SDPA max logit difference:\", difference)\n self.assertLess(difference, 1e-4)\n changed = tokens.clone()\n changed[:, 6:] = (changed[:, 6:] + 17) % self.manual_config.vocab_size\n with torch.no_grad():\n future, _ = sdpa(changed)\n self.assertTrue(torch.allclose(b[:, :6], future[:, :6], atol=1e-6, rtol=1e-6))\n\n @unittest.skipUnless(torch.cuda.is_available(), \"CUDA unavailable\")\n def test_cuda_fp16_forward_backward(self):\n device = torch.device(\"cuda\")\n manual, sdpa = self._pair(device)\n tokens = torch.randint(0, self.manual_config.vocab_size, (2, 24), device=device)\n targets = torch.randint(0, self.manual_config.vocab_size, (2, 24), device=device)\n with torch.amp.autocast(\"cuda\", dtype=torch.float16):\n a, _ = manual(tokens)\n b, loss = sdpa(tokens, targets)\n difference = (a.float() - b.float()).abs().max().item()\n print(\"CUDA FP16 manual/SDPA max logit difference:\", difference)\n self.assertLess(difference, 0.02)\n self.assertTrue(torch.isfinite(loss).item())\n loss.backward()\n self.assertTrue(torch.isfinite(sdpa.token_embedding.weight.grad).all().item())\n changed = tokens.clone()\n changed[:, 12:] = (changed[:, 12:] + 17) % self.manual_config.vocab_size\n with torch.no_grad(), torch.amp.autocast(\"cuda\", dtype=torch.float16):\n future, _ = sdpa(changed)\n self.assertTrue(torch.allclose(b[:, :12], future[:, :12], atol=0.02, rtol=0.02))\n", "tokenizer/smoke_4k.model": {"base64": 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t-212\n▁ଜିଲ୍\t-213\n▁ଚା\t-214\n▁ଲୋକ\t-215\n▁କୋ\t-216\n▁ନିର୍\t-217\n▁କରିଥିଲେ\t-218\n▁ବ୍ୟ\t-219\nଟ୍\t-220\n▁ଯା\t-221\n▁ତେ\t-222\nଚ୍\t-223\n▁ଇ\t-224\n▁ସି\t-225\n▁ସୂ\t-226\nନ୍ଧ\t-227\n▁କରୁ\t-228\nକି\t-229\n▁୩\t-230\n▁କା\t-231\nଙ୍କର\t-232\n▁ସର\t-233\n▁ବର୍ଷ\t-234\nନ୍ଦ୍ର\t-235\nାରୀ\t-236\nନ୍ତ୍ରୀ\t-237\n▁କରିବା\t-238\n▁ଅଧିକ\t-239\nମାନେ\t-240\n▁ବେଳେ\t-241\n▁ସୂଚନା\t-242\n▁ରାଜ୍ୟ\t-243\nମେ\t-244\n▁କାର୍ଯ୍ୟ\t-245\nଞ୍ଜ\t-246\nଣ୍ଟ\t-247\nହିଁ\t-248\n▁ପ୍ରତି\t-249\nବୁ\t-250\nସା\t-251\n▁ଲା\t-252\n▁ବିଜ\t-253\nଳରେ\t-254\nସ୍ଥ\t-255\nୋର୍\t-256\n▁ଟି\t-257\n▁ଭାର\t-258\nଗି\t-259\nଷ୍ଠ\t-260\n▁ଶି\t-261\n▁ହୋଇଛି\t-262\n▁ବିଶ\t-263\nୟରେ\t-264\n▁ମୁଖ\t-265\n▁ଗୋ\t-266\n▁ଗ୍ର\t-267\nାଉ\t-268\nାତ୍ର\t-269\n▁ଜିଲ୍ଲା\t-270\nକ୍ଷା\t-271\nେତ\t-272\nରୀ\t-273\nନ୍ନ\t-274\nଗୁ\t-275\nର୍ମ\t-276\n▁ସରକାର\t-277\n▁ଦା\t-278\n▁ଦିନ\t-279\n▁ଥିବା\t-280\n▁ଭାରତ\t-281\n▁ନିଜ\t-282\n▁କହିଛନ୍ତି\t-283\nମୁ\t-284\n▁୪\t-285\n▁ମୋ\t-286\n▁ମୃତ\t-287\nରରେ\t-288\n୍ୟୁ\t-289\n▁ମିଳି\t-290\nସନ\t-291\n▁ସ୍ୱ\t-292\nପ୍\t-293\n▁୫\t-294\n▁ଘଟ\t-295\n▁ହେଉ\t-296\nୋଜ\t-297\n▁ତେବେ\t-298\nନୀ\t-299\n▁ଏବେ\t-300\nଯି\t-301\nଧାନ\t-302\nମ୍ଭ\t-303\n▁ରି\t-304\n▁ମୁଖ୍ୟ\t-305\nପରି\t-306\nଶ୍\t-307\n▁ଆସି\t-308\n▁ଯୋଗ\t-309\n▁ପୂର୍\t-310\nଟର\t-311\nିର\t-312\n▁ଆଜି\t-313\nମନ୍ତ୍ରୀ\t-314\n▁ହେବ\t-315\n▁ସମସ୍ତ\t-316\nହେ\t-317\nଟେ\t-318\n▁ଆର\t-319\n▁ଖବର\t-320\n▁ପି\t-321\n▁ରହିଛି\t-322\nବାଦ\t-323\n୍ରି\t-324\nାରଣ\t-325\nାରା\t-326\nିକା\t-327\n▁ଉପରେ\t-328\n▁ପୋଲି\t-329\n▁ତାଙ୍କ\t-330\nକର\t-331\nେକ\t-332\nଟରେ\t-333\n▁ଗା\t-334\nୂଆ\t-335\nହିତ\t-336\n▁ଯାଇ\t-337\n▁ଅନ୍ୟ\t-338\n▁କ୍\t-339\nବତ\t-340\nଲୀ\t-341\nଦେଶ\t-342\nକ୍ରମ\t-343\n▁ଜଣେ\t-344\n▁ମହା\t-345\n▁ଦେଖି\t-346\n▁କରାଯାଇଛି\t-347\n▁ମାନ\t-348\nକେ\t-349\nୋଧ\t-350\nକାଶ\t-351\nହ୍\t-352\n▁ମଧ୍ୟରେ\t-353\nୋଷ\t-354\n୍ୱର\t-355\nଠାରେ\t-356\n▁ଏଥି\t-357\nଙ୍କା\t-358\n▁ସ୍ଥାନ\t-359\nୟା\t-360\nଟା\t-361\nପତି\t-362\nବାରୁ\t-363\n▁ଓଡ଼ି\t-364\n▁ଭାବେ\t-365\nସୁ\t-366\nଥାଏ\t-367\n▁ଅବ\t-368\n▁କରିଛି\t-369\n▁୬\t-370\n▁ପୋଲିସ\t-371\nଖା\t-372\n▁ନେ\t-373\nକ୍ତି\t-374\n▁ଦିଆ\t-375\nଢ଼\t-376\n▁:\t-377\n▁ଗତ\t-378\n▁କରାଯାଇ\t-379\nଲ୍ଲୀ\t-380\nୀନ\t-381\nବର୍\t-382\n▁ଦଳ\t-383\nାପ\t-384\nୀର\t-385\n▁ପକ୍ଷ\t-386\n▁ପ୍ରଦ\t-387\nଜ୍\t-388\n▁'\t-389\n▁୨୦\t-390\nବନେଶ\t-391\nଚନ\t-392\n▁ଭି\t-393\n▁ସେହି\t-394\nଉଁ\t-395\nଠାରୁ\t-396\nଠି\t-397\nଡା\t-398\nୁବନେଶ\t-399\n▁ଚି\t-400\n▁ଘଟଣା\t-401\nେଷ\t-402\n▁୭\t-403\nଚ୍ଚ\t-404\nତେ\t-405\n▁ଶୁ\t-406\n▁ବିଜେ\t-407\nଭାଗ\t-408\nୋନା\t-409\n▁ଦେଇଛନ୍ତି\t-410\n▁ଯୁ\t-411\n▁କିଛି\t-412\n▁ଲାଗି\t-413\n▁ଆରମ୍ଭ\t-414\n▁ଅଭିଯୋଗ\t-415\n▁ପକ୍ଷରୁ\t-416\n▁ଆଇ\t-417\nୀଙ୍କ\t-418\nଗତ\t-419\nବଳ\t-420\n▁ହୋଇଥିବା\t-421\n▁ସାମ\t-422\nନେ\t-423\nକରେ\t-424\n▁ମେ\t-425\nଷ୍ଟି\t-426\n▁ସମୟରେ\t-427\nଡ଼ା\t-428\n▁ମହି\t-429\n▁ରଖି\t-430\n▁ଜା\t-431\n▁ପ୍ରଥ\t-432\nଲୋ\t-433\n▁ପଦ\t-434\n▁ସୋ\t-435\n▁ଦୁଇ\t-436\n▁ନାହିଁ\t-437\nପଣ\t-438\nାରିଖ\t-439\nର୍ଯ୍ୟ\t-440\n▁ଗ୍ରାମ\t-441\nଣ୍ଣ\t-442\nତ୍ତି\t-443\nଚି\t-444\n▁ଅପ\t-445\n▁ପହ\t-446\n▁ହେବା\t-447\n▁(\t-448\nରଣ\t-449\nଟିଏ\t-450\nମାର\t-451\n▁ବିର\t-452\n▁କର୍ମ\t-453\nମାନଙ୍କ\t-454\nନ୍ତା\t-455\nପାରି\t-456\n▁ବିଭି\t-457\n▁କରିବାକୁ\t-458\n▁କେନ୍ଦ୍ର\t-459\nଦେ\t-460\nଯିବ\t-461\n▁ଲେ\t-462\n▁ହେଲେ\t-463\n▁କରୋନା\t-464\n▁ମହିଳା\t-465\n▁ମୃତ୍ୟୁ\t-466\n▁ବିଭିନ୍ନ\t-467\nଳୟ\t-468\nୁଲ\t-469\n▁ରୁ\t-470\n▁ବିଭାଗ\t-471\nଯୁ\t-472\n▁ଝ\t-473\n▁ସହିତ\t-474\n▁ତାରିଖ\t-475\n▁ତାଙ୍କୁ\t-476\nଡ୍\t-477\nଜାର\t-478\nତ୍ର\t-479\n▁ଶ୍ରୀ\t-480\n▁୮\t-481\n▁ନିକ\t-482\n▁ବ୍ୟବ\t-483\n▁ନିର୍ବା\t-484\nଜା\t-485\nଣୀ\t-486\n▁ଅର୍\t-487\n▁ଦେଶ\t-488\n▁କିନ୍ତୁ\t-489\nଶି\t-490\nଡିଆ\t-491\nଲରେ\t-492\n▁ସମ୍ପ\t-493\n▁ଥାନ\t-494\n▁ମାମ\t-495\nଟକ\t-496\nଯାଉ\t-497\n▁ଡା\t-498\n▁୧୦\t-499\nପୋର୍\t-500\n▁ସର୍\t-501\n▁ମ୍ୟା\t-502\nବାସ\t-503\nିତ୍\t-504\n▁ଆଉ\t-505\n▁ଫି\t-506\nବେଳେ\t-507\n▁ସବୁ\t-508\nାତ୍ରୀ\t-509\nଳ୍\t-510\nୟୁ\t-511\nଭଳି\t-512\n▁ଘର\t-513\n▁ଲକ୍ଷ\t-514\n▁ହୋଇଥିଲା\t-515\n▁ହୋଇଛନ୍ତି\t-516\nସେ\t-517\nଗ୍ର\t-518\nପ୍ର\t-519\nର୍ଶ\t-520\n▁ଖେ\t-521\n▁ଫେ\t-522\n▁ଦ୍ୱ\t-523\nୀତ\t-524\n▁ଅନେକ\t-525\n▁ବିରୋଧ\t-526\nଞ୍ଚି\t-527\n▁ପୂର୍ବ\t-528\nସର\t-529\n୍ଲ\t-530\nହ୍ନ\t-531\n▁ଡି\t-532\n▁ଗୁରୁ\t-533\n▁ପ୍ରଧାନ\t-534\n▁ମିଳିଛି\t-535\nଶୁ\t-536\nାଇଁ\t-537\n▁ବୁ\t-538\n▁ଯାହା\t-539\nଣସି\t-540\nମୁଖ\t-541\n▁କ୍ଷ\t-542\n▁ରିପୋର୍\t-543\nବକ\t-544\n▁କୌ\t-545\n▁ଦୁର୍\t-546\n▁କୁମାର\t-547\nୁବନେଶ୍ୱର\t-548\n▁ଆସ\t-549\n▁ଷ୍ଟ\t-550\n▁ମୁଖ୍ୟମନ୍ତ୍ରୀ\t-551\nପରେ\t-552\n▁ଯେଉଁ\t-553\nଂଗ\t-554\n▁ଟଙ୍କା\t-555\n▁ନିର୍ବାଚନ\t-556\n▁ବଡ\t-557\n▁ହି\t-558\n▁ନୂଆ\t-559\n▁ହେଉଛି\t-560\nଳ୍ପ\t-561\n▁ଶା\t-562\n▁୯\t-563\nାର୍ଥ\t-564\n▁ଅତି\t-565\n▁ବିଶ୍ୱ\t-566\n▁କରିଥିବା\t-567\n▁ଥ\t-568\nତ୍ୟ\t-569\nାଇବା\t-570\n▁ଆପଣ\t-571\nଦ୍ଦେଶ\t-572\nନ୍ତ୍ର\t-573\n▁ପାଣି\t-574\n▁ଗୋଟିଏ\t-575\n▁ଅଧିକାରୀ\t-576\nନର\t-577\n▁ଜଣାପ\t-578\nପାଳ\t-579\nଭାବ\t-580\n▁ଜେ\t-581\nୃଷ୍ଟି\t-582\n▁ଅଞ୍ଚ\t-583\n▁କୌଣସି\t-584\nଟୋ\t-585\nଦା\t-586\nମରେ\t-587\n▁ଏନ\t-588\nସ୍ଥି\t-589\n▁ଏହାର\t-590\nତର\t-591\nାୟକ\t-592\n▁ଆକ\t-593\n▁ଟେ\t-594\n▁ଓଡ଼ିଶା\t-595\nନ୍ସ\t-596\n▁ଚଳ\t-597\n▁ଗାଁ\t-598\n▁ବହୁ\t-599\n▁ଜାରି\t-600\nଥିବାରୁ\t-601\n▁କ୍ର\t-602\n▁ପ୍ରଥମ\t-603\nଆଇ\t-604\nକ୍ତର\t-605\n▁କଂଗ\t-606\n▁କଂଗ୍ରେ\t-607\n▁ପହଞ୍ଚି\t-608\nତ୍ୟା\t-609\n▁ବ୍ୟା\t-610\n▁ହଜାର\t-611\n▁ପ୍ରକାଶ\t-612\nବ୍\t-613\nୁଆ\t-614\nଶ୍ର\t-615\n▁ପର\t-616\nସ୍ଥା\t-617\n▁ବ୍ଲ\t-618\n▁ମାମଲା\t-619\n▁ପରିବାର\t-620\nଗଡ଼\t-621\n▁ଘଟି\t-622\n▁ଚାଲି\t-623\n▁ଛାତ୍ର\t-624\nଜଣ\t-625\nାସ\t-626\nବାଇ\t-627\nିରଫ\t-628\n▁ଭିତ\t-629\n▁ଗିରଫ\t-630\nଠା\t-631\nୟକ\t-632\nଦ୍ର\t-633\nାୟୀ\t-634\n▁ପେ\t-635\nଯୁକ୍ତ\t-636\n▁ଏହାକୁ\t-637\nବୀ\t-638\n▁ବୈ\t-639\nଣ୍ଡି\t-640\nପାଇଁ\t-641\n▁କମି\t-642\n▁ଦାବି\t-643\nମାନଙ୍କୁ\t-644\n▁ପ୍ରସ୍ତ\t-645\nଜନ\t-646\nଳନ\t-647\nଦ୍ୟ\t-648\nମ୍ପ\t-649\nକ୍ରି\t-650\nଯ୍ୟା\t-651\n▁ନିକଟରେ\t-652\nଯା\t-653\n▁,\t-654\n▁ୱ\t-655\n▁ବୃ\t-656\n▁ଥିଲେ\t-657\nଗର\t-658\nଗ୍\t-659\nୁରୀ\t-660\n▁ତି\t-661\n▁କାରଣ\t-662\n▁ସ୍ୱା\t-663\n▁ଏଠାରେ\t-664\n▁ଡାକ୍ତର\t-665\nୁରୋ\t-666\nଦ୍ଧା\t-667\n▁ଘୋଷ\t-668\n▁ଅନୁସ\t-669\nଜର\t-670\nଣୁ\t-671\nମାଣ\t-672\n▁ମନ\t-673\nର୍ଯ୍ୟନ୍ତ\t-674\nିତା\t-675\n▁ଆଗ\t-676\n▁ଛା\t-677\nତିରେ\t-678\n▁କଥା\t-679\n▁ତଥା\t-680\n▁ଏଭଳି\t-681\n▁ଥାନା\t-682\n▁ସାମ୍\t-683\n▁ଜିଲ୍ଲ\t-684\n▁ବିଜେପି\t-685\n▁ପୂର୍ବରୁ\t-686\n):\t-687\n▁ଏସ\t-688\n▁ପୋ\t-689\n▁ଘରେ\t-690\n▁ପରୀ\t-691\nଯାଉଛି\t-692\nବରେ\t-693\n▁ତାଙ୍କର\t-694\n▁ଦେଖିବାକୁ\t-695\n▁ନିର୍ଦ୍ଦେଶ\t-696\n▁କରାଯାଇଥିଲା\t-697\nୋର\t-698\n▁-\t-699\nବ୍ୟ\t-700\nସରେ\t-701\nଣ୍ଡା\t-702\nମ୍ବର\t-703\n▁କାମ\t-704\n▁ଫିଲ୍\t-705\n▁ରାଜ୍ୟରେ\t-706\nଶୀ\t-707\nୁତ\t-708\nଶ୍ୟ\t-709\n▁ନାମ\t-710\n▁ନିଆ\t-711\n▁କରୁଥିବା\t-712\n▁କରୁଛନ୍ତି\t-713\nପର\t-714\nଡିଓ\t-715\n▁ଖୁ\t-716\n▁ତଦ\t-717\n▁ବିଦ\t-718\n▁ସୁର\t-719\n▁ଅନ୍ତ\t-720\n▁ସାହୁ\t-721\n▁ଅନୁଷ୍ଠ\t-722\n▁ବ୍ୟକ୍ତି\t-723\nଭୁବନେଶ୍ୱର\t-724\nାଳ\t-725\nଧିକ\t-726\nନ୍ଦର\t-727\n▁ଉତ୍\t-728\n▁କଲେ\t-729\n▁ବଡ଼\t-730\n▁ଏନେଇ\t-731\n▁କୁହା\t-732\n▁କଂଗ୍ରେସ\t-733\n▁ଦେଇଥିଲେ\t-734\nଲୁ\t-735\nସିଆ\t-736\n▁ଆୟ\t-737\nଖ୍ୟା\t-738\nବାସୀ\t-739\n▁ଆମେ\t-740\n▁ଜବତ\t-741\nସ୍ଥ୍ୟ\t-742\n▁ଉତ୍ତ\t-743\n▁କ୍ୟା\t-744\n▁ପ୍ରଦାନ\t-745\n▁ଅଭିଯୁକ୍ତ\t-746\n▁କାର୍ଯ୍ୟକ୍ରମ\t-747\nଂସ\t-748\nଦନ\t-749\nୀପ\t-750\nିବା\t-751\nୟାର\t-752\n▁ଜୀ\t-753\n▁ବିଧ\t-754\n▁ପ୍ରାୟ\t-755\n▁ସ୍ଥାନରେ\t-756\nୂଳ\t-757\nଚିତ\t-758\nଯାୟୀ\t-759\n▁ସ୍ଥି\t-760\nଦିଲ୍ଲୀ\t-761\n୍ରାନ୍ତ\t-762\n▁ବର୍ଷା\t-763\n▁ଭିତରେ\t-764\nଜେ\t-765\nବାନ\t-766\nର୍କ\t-767\n▁ଲି\t-768\nଗ୍ରା\t-769\nସ୍ପି\t-770\n▁ଉଦ୍ଧ\t-771\n▁ଆସନ୍ତା\t-772\nମିକ\t-773\nାନୀ\t-774\n▁ଅଫ\t-775\n▁କୋଟି\t-776\n▁ଚିହ୍ନ\t-777\n▁ତଦନ୍ତ\t-778\nଗା\t-779\nଘଟ\t-780\nଜୁ\t-781\nହଣ\t-782\n▁ହୁ\t-783\n▁୧୫\t-784\nଚାରୀ\t-785\n▁ଚାଷ\t-786\n▁ଚିକ\t-787\n▁ଯୋଗୁ\t-788\n▁ପ୍ରଶା\t-789\n▁ମ୍ୟାଚ\t-790\nଭେ\t-791\nନୂଆ\t-792\nରାଜ\t-793\nହେଁ\t-794\nକାଲି\t-795\nଦ୍ଧି\t-796\n▁ନ୍ୟ\t-797\n▁ପ୍ରା\t-798\n▁ଦିଲ୍ଲୀ\t-799\n▁ଦ୍ୱାରା\t-800\n▁ଅନୁଯାୟୀ\t-801\n▁ସେହିପରି\t-802\n▁ପର୍ଯ୍ୟନ୍ତ\t-803\nଦେଇ\t-804\n▁ନୁ\t-805\nଧାରଣ\t-806\n▁କଟକ\t-807\n▁ଶେଷ\t-808\n▁ସିଂ\t-809\n▁ତାହା\t-810\n▁କରିବେ\t-811\n▁ଘୋଷଣା\t-812\n▁କରନ୍ତୁ\t-813\n▁କର୍ମଚାରୀ\t-814\nଚ୍ଛ\t-815\nଯାଏ\t-816\n▁ଜନ\t-817\n▁ବୋ\t-818\nକାରୀ\t-819\nନାୟକ\t-820\n▁ଧରି\t-821\n▁ନେତା\t-822\n▁ପୁଲି\t-823\n▁ଚିହ୍ନଟ\t-824\n▁ମନ୍ତ୍ରୀ\t-825\n▁ଆକ୍ରାନ୍ତ\t-826\nଂଚ\t-827\nପେ\t-828\nିଲ\t-829\n▁ମହ\t-830\n▁ବାହ\t-831\n▁ବିଷ\t-832\n▁ରାଉ\t-833\n▁ଅଧ୍ୟ\t-834\n▁ଅଭିନ\t-835\n▁କୋର୍\t-836\n▁ପାଇବା\t-837\n▁ଚିକିତ୍\t-838\n▁ସେମାନେ\t-839\n▁ଆମ\t-840\n▁୧୨\t-841\n▁ଆଣି\t-842\n▁ସଭା\t-843\nଲାଙ୍ଗ\t-844\n▁ଏପରି\t-845\n▁ପ୍ରାର୍ଥ\t-846\nନକ\t-847\nପୁ\t-848\nଟ୍ଟ\t-849\nିକେ\t-850\n▁ଭୂ\t-851\n▁ଯଦି\t-852\n▁ସମୟ\t-853\nଷ୍ଟ୍ର\t-854\n▁ସୁରକ୍ଷା\t-855\nତୁ\t-856\n▁ପୂ\t-857\n▁ସଫ\t-858\n▁ପଞ୍ଚ\t-859\n▁ଏଥିରେ\t-860\n▁ଫିଲ୍ମ\t-861\n▁ଭାରତୀୟ\t-862\n▁ସଂଖ୍ୟା\t-863\nନୂଆଦିଲ୍ଲୀ\t-864\nକ୍ସ\t-865\n▁ଭୋ\t-866\nପାରେ\t-867\n▁ପଡି\t-868\n▁ଦେଖା\t-869\n▁ବିଶେଷ\t-870\nଡେ\t-871\nଧା\t-872\nନାଥ\t-873\n୍ରୋ\t-874\n▁ପଢ\t-875\nପକ୍ଷ\t-876\nସ୍ଥିତ\t-877\n▁ପୁରୁ\t-878\n▁ଯୁବକ\t-879\n▁ବ୍ୟୁରୋ\t-880\n▁ଅନୁସାରେ\t-881\nଦାନ\t-882\nମ୍ବ\t-883\nର୍ଥ\t-884\nଲିକ\t-885\n▁କଳା\t-886\n▁ଯାଉ\t-887\n▁ପୁଣି\t-888\n▁କେନ୍ଦ\t-889\n▁ଉଦ୍ଧାର\t-890\n▁ବର୍ତ୍ତ\t-891\n▁ସୃଷ୍ଟି\t-892\n▁ବିରୋଧରେ\t-893\nମୋ\t-894\nଳର\t-895\nିଅ\t-896\nୂର\t-897\n୍ବ\t-898\nକର୍\t-899\nନରେ\t-900\nବେଶ\t-901\n▁ଭେ\t-902\n▁ହା\t-903\n▁୩୦\t-904\n▁ଯୋଜ\t-905\n▁ସହା\t-906\nଲୋଚନା\t-907\n▁ଥିଲା\t-908\n▁ପ୍ରକ\t-909\n▁ଦୁର୍ଘଟ\t-910\n▁ତାରିଖରେ\t-911\n▁ବର୍ତ୍ତମାନ\t-912\n▁ପ୍ରଧାନମନ୍ତ୍ରୀ\t-913\nମୀ\t-914\nଚାର\t-915\n▁ଦର\t-916\n▁ବୟ\t-917\nଯିବା\t-918\nର୍ଚ୍ଚ\t-919\n▁ଲୋକେ\t-920\nଛାତ୍ରୀ\t-921\nପାରିବେ\t-922\nୂର୍ଣ୍ଣ\t-923\nଯାଇଥିଲା\t-924\n▁ପ୍ରମୁଖ\t-925\n▁ଅନୁଷ୍ଠିତ\t-926\n▁ଛାତ୍ରଛାତ୍ରୀ\t-927\nଜୀ\t-928\nଲର\t-929\nିଆ\t-930\nପଟେ\t-931\nାନୁ\t-932\nାମୀ\t-933\nୃତି\t-934\nସରରେ\t-935\nିତ୍ର\t-936\n▁ପଡ଼ି\t-937\n▁ଗ୍ରହଣ\t-938\n▁ଶିକ୍ଷ\t-939\n▁ବିଜେଡି\t-940\n▁ସଂକ୍ରମ\t-941\n▁ସ୍ଥାନୀୟ\t-942\n▁ରିପୋର୍ଟର\t-943\nଥର\t-944\nଦୀ\t-945\nଭୟ\t-946\nଳୁ\t-947\nଆରେ\t-948\n▁ନବ\t-949\n▁ମୂ\t-950\n▁୧୪\t-951\n▁ଓଡି\t-952\n▁ପର୍\t-953\n▁ସମସ\t-954\nସ୍ତାନ\t-955\n▁ଟିକା\t-956\n▁ଶିଶୁ\t-957\n▁ମିଶ୍ର\t-958\n▁ଜଣାପଡି\t-959\n▁ପଞ୍ଚାୟ\t-960\n▁ସରକାରୀ\t-961\n▁କରିଥିଲା\t-962\n▁ଲୋକଙ୍କୁ\t-963\n▁ଚିକିତ୍ସା\t-964\n▁ଦିଆଯାଇଛି\t-965\nଛୁ\t-966\nତନ\t-967\nବାହ\t-968\n▁ଗଣ\t-969\nମିତି\t-970\n▁ମୁଁ\t-971\n▁ହାଇ\t-972\n୍ୟାଳୟ\t-973\n▁କେବଳ\t-974\n▁ମାନେ\t-975\n▁ସମେତ\t-976\n▁ନୁହେଁ\t-977\n▁ଅନ୍ତର୍\t-978\n▁ସଭାପତି\t-979\n▁ସାଧାରଣ\t-980\nଭୁ\t-981\nଳୀ\t-982\nଧାର\t-983\n▁ଖା\t-984\n▁ଚେ\t-985\n▁ଭଲ\t-986\nପ୍ରି\t-987\nବଶ୍ୟ\t-988\n▁ଘଟଣ\t-989\n▁ରୋଗ\t-990\n▁ଶ୍ର\t-991\n▁ହାତ\t-992\nନାହିଁ\t-993\nବଶ୍ୟକ\t-994\n▁ଖେଳା\t-995\n▁ଜାଣି\t-996\n▁ଜୀବନ\t-997\n▁ମାର୍\t-998\n▁ସ୍କୁ\t-999\n▁କରାଯିବ\t-1000\n▁ଗତକାଲି\t-1001\n▁ହୋଇଥାଏ\t-1002\n▁ଏଥିପାଇଁ\t-1003\n▁ପରୀକ୍ଷା\t-1004\n▁ରହିଥିବା\t-1005\n▁ଜିଲ୍ଲାରେ\t-1006\nଆର\t-1007\nଏସ\t-1008\nସିଂ\t-1009\n▁ଲଗ\t-1010\n▁୨୪\t-1011\n▁ଝିଅ\t-1012\n▁ଅର୍ଥ\t-1013\n▁ଜିତି\t-1014\n▁ବିଧାୟକ\t-1015\n▁ଲୋକଙ୍କ\t-1016\n▁ପ୍ରସ୍ତୁତ\t-1017\n▁ସ୍ୱାସ୍ଥ୍ୟ\t-1018\ned\t-1019\nନୁ\t-1020\nବଲ\t-1021\nଳକୁ\t-1022\nାନା\t-1023\n▁କୃ\t-1024\n▁ସଦ\t-1025\n▁ଅଛି\t-1026\n▁କାର\t-1027\n▁ଭାଇ\t-1028\n▁ଗଞ୍ଜ\t-1029\n▁ପ୍ରସ\t-1030\n▁ହେଲା\t-1031\nସ୍ପିଟା\t-1032\n▁ଯୋଜନା\t-1033\n▁ଅଛନ୍ତି\t-1034\n▁ଆଲୋଚନା\t-1035\n▁ଏହାପରେ\t-1036\n▁ଘଟଣାସ୍ଥ\t-1037\n▁ହସ୍ପିଟା\t-1038\n▁ସେମାନଙ୍କ\t-1039\nଂଶ\t-1040\nହତ\t-1041\nତ୍ମ\t-1042\n▁ୟୁ\t-1043\nଲିକ୍\t-1044\nସ୍ତା\t-1045\nିତୀୟ\t-1046\n▁ନିୟ\t-1047\n▁ନେତ\t-1048\n▁୨୦୧\t-1049\n▁ଠାରୁ\t-1050\n▁ମହାର\t-1051\n▁ଘଟିଛି\t-1052\n▁ଭିଡିଓ\t-1053\nାଇଛନ୍ତି\t-1054\n▁ବାହାରି\t-1055\n▁ସମସ୍ୟା\t-1056\n▁ପ୍ରଶାସନ\t-1057\n▁ରିପୋର୍ଟ\t-1058\nଠୁ\t-1059\nବୃ\t-1060\n▁ଢ\t-1061\nଦିନ\t-1062\nସ୍ୟ\t-1063\nଞ୍ଜନ\t-1064\nଣ୍ଟା\t-1065\n▁ଦାୟ\t-1066\n▁ଧାନ\t-1067\n▁ଭଳି\t-1068\n▁ଚଳିତ\t-1069\n▁ନିଜର\t-1070\n▁ଫଳରେ\t-1071\n▁ସ୍ବା\t-1072\n▁ପୁଲିସ\t-1073\n▁ଯୋଗୁଁ\t-1074\nti\t-1075\n▁b\t-1076\n▁ରୂ\t-1077\n▁ଆର୍\t-1078\n▁ଦେଖ\t-1079\n▁ଦେବ\t-1080\n▁ବରି\t-1081\n▁ସକା\t-1082\n▁ସମା\t-1083\n▁କୋଭି\t-1084\n▁ଜିଲା\t-1085\n▁ଟେଲି\t-1086\n▁ବିଜୟ\t-1087\nବର୍ତ୍ତ\t-1088\n▁ମିଡିଆ\t-1089\n▁ଅଧ୍ୟକ୍ଷ\t-1090\n▁ଉପସ୍ଥିତ\t-1091\nଘର\t-1092\n▁\"\t-1093\nକନକ\t-1094\nକରଣ\t-1095\nସ୍କ\t-1096\n▁ବଦ\t-1097\n▁ବର\t-1098\n▁ମତ\t-1099\n▁ଯି\t-1100\nଗ୍ରୀ\t-1101\nତଙ୍କ\t-1102\nର୍ଶନ\t-1103\n▁ପାଠ\t-1104\n▁ବାତ\t-1105\nର୍କରେ\t-1106\n▁ପଟ୍ଟ\t-1107\n▁ପୁରୀ\t-1108\n▁କୋର୍ଟ\t-1109\n▁ପୁରୁଷ\t-1110\n▁କ୍ଲିକ୍\t-1111\n▁ବୃଦ୍ଧି\t-1112\n▁ଲାଗିଛି\t-1113\n▁ଅଞ୍ଚଳରେ\t-1114\n▁ହୋଇଥିଲେ\t-1115\n▁ଜଣାପଡିଛି\t-1116\n▁ସରକାରଙ୍କ\t-1117\nଏମ\t-1118\nଭବ\t-1119\nଶତ\t-1120\nଶମ\t-1121\n୍ଞ\t-1122\n▁ଋ\t-1123\n▁ଆଦ\t-1124\n▁ଟୁ\t-1125\nଶ୍ଚି\t-1126\nହେରା\t-1127\n▁ଆଦି\t-1128\n▁ଏକା\t-1129\n▁ପଦକ\t-1130\n▁ବୈଠ\t-1131\nୀଙ୍କୁ\t-1132\n▁ବନ୍ଦ\t-1133\n▁ସର୍ବ\t-1134\nଗ୍ରାମ୍\t-1135\n▁ମାତ୍ର\t-1136\n▁ଆବଶ୍ୟକ\t-1137\n▁ବରିଷ୍ଠ\t-1138\n▁ସୁନ୍ଦର\t-1139\n▁କହିଥିଲେ\t-1140\n▁ପଟ୍ଟନାୟକ\t-1141\nତମ\t-1142\nଷି\t-1143\nସହ\t-1144\nିନ\t-1145\nୋନ\t-1146\nଢ଼ି\t-1147\nତ୍ୱ\t-1148\n▁୧୯\t-1149\nବେଦନ\t-1150\nମ୍ବା\t-1151\n▁ଦାସ\t-1152\n▁ବସି\t-1153\n▁ଭର୍\t-1154\nପତ୍ତି\t-1155\nାତ୍ରା\t-1156\n▁କନ୍ଧ\t-1157\n▁ଟଙ୍କ\t-1158\n▁ନିଷ୍\t-1159\n▁ପଢନ୍ତୁ\t-1160\n▁ଜିଲ୍ଲାରୁ\t-1161\n▁ବ୍ୟବସ୍ଥା\t-1162\nଭର\t-1163\nଷା\t-1164\n▁|\t-1165\nଟିଭ\t-1166\nପାଦ\t-1167\nକ୍ଷ୍\t-1168\nପତ୍ର\t-1169\n▁ଉଭୟ\t-1170\n▁କର୍\t-1171\n▁ଦଳର\t-1172\nପାତ୍ର\t-1173\nଯୋଗ୍ୟ\t-1174\n▁ଅପରା\t-1175\n▁ଶ୍ରେ\t-1176\n▁କରୁଛି\t-1177\n▁ସାମିଲ\t-1178\n▁ଜଣାପଡ଼ି\t-1179\n▁ଟେଲିଗ୍ରାମ୍\t-1180\nଫ୍\t-1181\nଷଣ\t-1182\nଜ୍ଞ\t-1183\nପିଏ\t-1184\nଲାର\t-1185\nାତା\t-1186\n▁ଡେ\t-1187\n▁ବଢ\t-1188\n▁ଭୁ\t-1189\n▁୧୩\t-1190\nତ୍ତା\t-1191\nସିଆଲ\t-1192\nାମାନ\t-1193\n▁ରବି\t-1194\n▁ସ୍ପ\t-1195\nମ୍ବାଇ\t-1196\n▁ଆତ୍ମ\t-1197\n▁ଟ୍ରେ\t-1198\n▁ବ୍ଲକ\t-1199\n▁ମନ୍ଦ\t-1200\n▁ଲେଖି\t-1201\nତେବେଳେ\t-1202\n▁ବିମାନ\t-1203\n▁ହେବାକୁ\t-1204\n▁ଘଟଣାସ୍ଥଳରେ\t-1205\nଠନ\t-1206\nଲକ\t-1207\nଲ୍ୟ\t-1208\nୀର୍\t-1209\n୍ବର\t-1210\nୟତା\t-1211\n▁ତୁ\t-1212\nାତୀୟ\t-1213\n▁ଅଫି\t-1214\n▁ଦେଉ\t-1215\n▁ପାଖ\t-1216\n▁ଫଟୋ\t-1217\nେତ୍ରୀ\t-1218\nାଇଥିଲେ\t-1219\n▁ବେହେରା\t-1220\n▁ମ୍ୟାଚ୍\t-1221\n▁ସମ୍ଭାବ\t-1222\n▁ସୁଦ୍ଧା\t-1223\n▁ମହାନ୍ତି\t-1224\n▁ହୋଇଯାଇଛି\t-1225\n୦୦\t-1226\nଡିକ\t-1227\n▁ଗଛ\t-1228\n▁୧୮\t-1229\n▁୨୫\t-1230\nକଙ୍କ\t-1231\nଙ୍ଗା\t-1232\n▁ପୁଅ\t-1233\n▁ସଂପ\t-1234\n▁ସଫଳ\t-1235\nମେଣ୍ଟ\t-1236\n▁ଉଦ୍ୟ\t-1237\n▁ତେଣୁ\t-1238\n▁ନାୟକ\t-1239\n▁ନ୍ୟୁ\t-1240\n▁ପାର୍\t-1241\n▁ବନ୍ଧ\t-1242\n▁ଅତିଥି\t-1243\n▁ଦେଶରେ\t-1244\n▁ବିକାଶ\t-1245\nକିସ୍ତାନ\t-1246\n▁ଗ୍ରାମର\t-1247\n▁ପ୍ରଥମେ\t-1248\n▁ବିଷୟରେ\t-1249\n▁ସାମ୍ନା\t-1250\n▁ପଞ୍ଚାୟତ\t-1251\n▁ପର୍ଯ୍ୟା\t-1252\n▁ପ୍ରତିଶତ\t-1253\nil\t-1254\n▁!\t-1255\nଏସ୍\t-1256\nନ୍ମ\t-1257\nମିତ\t-1258\nଳିତ\t-1259\nଶ୍ୱ\t-1260\n▁ଖୋ\t-1261\n▁୧୧\t-1262\nକର୍ମ\t-1263\nକୋର୍\t-1264\n▁କମ୍\t-1265\n▁ସଂଗ\t-1266\nଷ୍ଠାନ\t-1267\n▁କରିବ\t-1268\n▁ତାଲି\t-1269\n▁ଦେବା\t-1270\n▁ନବୀନ\t-1271\n▁ପ୍ରେ\t-1272\n▁ଉତ୍ତର\t-1273\n▁ବିବାହ\t-1274\n▁ଗୁରୁତର\t-1275\n▁ସେଠାରେ\t-1276\n▁ବ୍ୟବହାର\t-1277\n▁ହେଉଥିବା\t-1278\n▁ଅନ୍ତର୍ଗତ\t-1279\n▁କରାଯାଉଛି\t-1280\nଗଡ\t-1281\nଭୂ\t-1282\n▁D\t-1283\nରାମ\t-1284\nସଭା\t-1285\n▁ଦୃ\t-1286\n▁ନୂ\t-1287\n▁ରୋ\t-1288\nକ୍ଷି\t-1289\nଗିରି\t-1290\nଣ୍ଟି\t-1291\n▁ରାମ\t-1292\n▁ସ୍ତ\t-1293\nଶ୍ଚିମ\t-1294\n▁ଉକ୍ତ\t-1295\n▁ଉଚ୍ଚ\t-1296\n▁ଛାଡି\t-1297\n▁ପରିଚ\t-1298\n▁ପାରି\t-1299\nାଇବାକୁ\t-1300\n▁ବଲାଙ୍ଗ\t-1301\n▁ସୋସିଆଲ\t-1302\n▁ଦେଇଥିବା\t-1303\n▁ପ୍ରାର୍ଥୀ\t-1304\n▁ସମ୍ଭାବନା\t-1305\nig\t-1306\n୨୦\t-1307\nତିକ\t-1308\nମାଳ\t-1309\nଲାଇ\t-1310\n▁ଜଳ\t-1311\n▁ବନ\t-1312\nଙ୍ଗି\t-1313\nମନ୍ତ\t-1314\n▁ଅଧି\t-1315\n▁ସୋମ\t-1316\n▁ଜଙ୍ଗ\t-1317\n▁ନିଆଁ\t-1318\n▁ବାପା\t-1319\n▁ଆବେଦନ\t-1320\n▁ହେବାର\t-1321\nମାନଙ୍କର\t-1322\n▁ଅବସରରେ\t-1323\n▁ପ୍ରକାର\t-1324\n▁ଜିଲ୍ଲାର\t-1325\n▁ଦୁର୍ଘଟଣା\t-1326\n▁ନିଷ୍ପତ୍ତି\t-1327\nକୋ\t-1328\nଘା\t-1329\nଧି\t-1330\nଦାର\t-1331\nଭାର\t-1332\nୀରେ\t-1333\n▁କଲ\t-1334\n▁ଚୋ\t-1335\nବନ୍ଧ\t-1336\nସ୍ତି\t-1337\n▁ଅଗ୍\t-1338\n▁ଆଇନ\t-1339\n▁ଆସୁ\t-1340\n▁ଜଣକ\t-1341\n▁ଜରି\t-1342\n▁ଫୁଲ\t-1343\n▁ସ୍ଥ\t-1344\n▁ହିଁ\t-1345\n▁ଟିମ୍\t-1346\n▁ଫେରି\t-1347\n▁ବହୁତ\t-1348\n▁ଭଦ୍ର\t-1349\n▁ସାହା\t-1350\n▁ସେବା\t-1351\n▁ଉଦ୍ୟମ\t-1352\n▁ପ୍ରବଳ\t-1353\n▁ଭାରତର\t-1354\n▁ମାଧ୍ୟ\t-1355\nଯାଇଥିବା\t-1356\n▁କରିବାର\t-1357\n▁ଦୃଷ୍ଟି\t-1358\n▁ପ୍ରତ୍ୟ\t-1359\nାନୁଷ୍ଠାନ\t-1360\n▁ଦ୍ୱିତୀୟ\t-1361\n▁ପାକିସ୍ତାନ\t-1362\nଖଲ\t-1363\nୃହ\t-1364\n୧୯\t-1365\nଗାର\t-1366\nଘ୍ର\t-1367\nଚିବ\t-1368\nରକୁ\t-1369\nଲିଉ\t-1370\n▁ଅସ\t-1371\n▁ଲୁ\t-1372\n▁ହର\t-1373\n▁ହୃ\t-1374\n▁୨୬\t-1375\nଥିରେ\t-1376\nପଡ଼ା\t-1377\nବର୍ଷ\t-1378\nସିସି\t-1379\nାଇନା\t-1380\n▁ଆହତ\t-1381\n▁ତଳେ\t-1382\n▁ରାୟ\t-1383\n▁ଆଧାର\t-1384\n▁ଆୟୋଜ\t-1385\n▁ଖଣ୍ଡ\t-1386\n▁ଚାଷୀ\t-1387\n▁ନିକଟ\t-1388\n▁ମୋଦି\t-1389\n▁ଷ୍ଟା\t-1390\nପକ୍ଷରେ\t-1391\nସ୍ଥାରେ\t-1392\n▁ଦେଇଛି\t-1393\n▁ହତ୍ୟା\t-1394\nରେନ୍ଦ୍ର\t-1395\n▁ଛାତ୍ରୀ\t-1396\n▁ମଧ୍ୟରୁ\t-1397\n▁ଯାଇଥିଲେ\t-1398\n▁ଅଭିନେତ୍ରୀ\t-1399\nପକ\t-1400\nଫା\t-1401\nଟିଂ\t-1402\nଦେହ\t-1403\nନାର\t-1404\nବତନ\t-1405\nଶ୍ନ\t-1406\n▁ଦ୍\t-1407\n▁ନୀ\t-1408\n▁ପଛ\t-1409\n▁ବଳ\t-1410\nଗୁଡ଼\t-1411\nଚ୍ଛା\t-1412\nବାଦୀ\t-1413\n▁ହୁଏ\t-1414\nକ୍ଷିଣ\t-1415\n▁ଖେଳି\t-1416\n▁ଗ୍ରା\t-1417\n▁ପ୍ରଶ\t-1418\n▁ବ୍ରା\t-1419\n▁ରକ୍ତ\t-1420\n▁ଲେଖା\t-1421\nକ୍ରିୟା\t-1422\nହାନ୍ତି\t-1423\n▁ଆତଙ୍କ\t-1424\n▁ଓଡ଼ିଶ\t-1425\n▁ଜଣଙ୍କ\t-1426\n▁ଦୁଇଟି\t-1427\n▁ଭାବରେ\t-1428\n▁ସହଯୋଗ\t-1429\n▁ସେୟାର\t-1430\n▁ଚର୍ଚ୍ଚ\t-1431\n▁ପ୍ରଶ୍ନ\t-1432\n▁ରାଜ୍ୟର\t-1433\nୁବନେଶ୍ବର\t-1434\n▁ବିଦ୍ୟାଳୟ\t-1435\n▁ମହାପାତ୍ର\t-1436\n▁ସୂଚନାଯୋଗ୍ୟ\t-1437\nan\t-1438\nco\t-1439\nଚା\t-1440\nଦୁ\t-1441\nପଥ\t-1442\nରର\t-1443\nସବ\t-1444\nହନ\t-1445\nୟନ\t-1446\nଲୋକ\t-1447\n▁by\t-1448\n▁୧୭\t-1449\nଓଡ଼ି\t-1450\nମିକା\t-1451\nଷ୍ଟା\t-1452\n▁ଦେହ\t-1453\n▁ବାର\t-1454\n▁ମିଶ\t-1455\nଣ୍ଡିଆ\t-1456\nବାଦିକ\t-1457\n▁କେହି\t-1458\n▁ପଞ୍ଜ\t-1459\n▁ରହିବ\t-1460\n▁ସଙ୍ଗ\t-1461\n▁ସତ୍ୟ\t-1462\n▁ଆସିଛି\t-1463\n▁କ୍ଷେତ\t-1464\n▁ପାଇଛି\t-1465\n▁ଜଣଙ୍କୁ\t-1466\n▁ପଶ୍ଚିମ\t-1467\n▁ବାତ୍ୟା\t-1468\n▁ମୃତଦେହ\t-1469\n▁ସହାୟତା\t-1470\n▁କନ୍ଧମାଳ\t-1471\n▁ପୂର୍ବତନ\t-1472\n▁ମିଡିଆରେ\t-1473\n▁ସାମ୍ବାଦିକ\t-1474\nବସ\t-1475\nାବ\t-1476\n▁s\t-1477\nଟିର\t-1478\nରାଲ\t-1479\nହୁଲ\t-1480\n▁କମ\t-1481\n▁୧୬\t-1482\n▁୫୦\t-1483\nଟିକୁ\t-1484\nୃତୀୟ\t-1485\n▁ଗୃହ\t-1486\n▁ପଜି\t-1487\n▁ମାସ\t-1488\n▁ଯାଏ\t-1489\n▁ରଖା\t-1490\n▁ଲାଇ\t-1491\n▁ସାଇ\t-1492\n▁ଏସପି\t-1493\n▁କମ୍ପ\t-1494\n▁ଜନ୍ମ\t-1495\n▁ନୂତନ\t-1496\n▁ପାଳନ\t-1497\n▁ପିଲା\t-1498\n▁ବାଲେ\t-1499\n▁ସାରା\t-1500\nନ୍ଧ୍ୟା\t-1501\nୂର୍ଯ୍ୟ\t-1502\n▁ଓଡିଶା\t-1503\n▁ପୋଷ୍ଟ\t-1504\n▁ବିଧାନ\t-1505\n▁ଯାଇଛି\t-1506\n▁ରାହୁଲ\t-1507\n▁ରେକର୍\t-1508\n▁କ୍ରିକେ\t-1509\n▁ଥାନାରେ\t-1510\n▁ଦକ୍ଷିଣ\t-1511\n▁ପ୍ରଦୀପ\t-1512\n▁ରାତିରେ\t-1513\n▁ଶିକ୍ଷକ\t-1514\n▁ଶ୍ରେଣୀ\t-1515\n▁ସ୍ପଷ୍ଟ\t-1516\n▁ମାମଲାରେ\t-1517\n▁ରାଷ୍ଟ୍ର\t-1518\nନୂଆଦିଲ୍ଲୀଃ\t-1519\n▁ଜଣାପଡ଼ିଛି\t-1520\n▁ଭୁବନେଶ୍ୱର\t-1521\n▁କାର୍ଯ୍ୟାନୁଷ୍ଠାନ\t-1522\nଟୁ\t-1523\nଡର\t-1524\nପନ\t-1525\nହର\t-1526\nigi\t-1527\nପାତ\t-1528\nପ୍ତ\t-1529\nଳିଆ\t-1530\nାନି\t-1531\nୋଟି\t-1532\n▁ଆଶ\t-1533\n▁ଇଂ\t-1534\n▁ଉଦ\t-1535\n▁ଜଗ\t-1536\n▁ଜୁ\t-1537\n▁ପକ\t-1538\nଖଣ୍ଡ\t-1539\nଶ୍ରୀ\t-1540\nସିଂହ\t-1541\n▁ଅଟୋ\t-1542\n▁ଟ୍ର\t-1543\n▁ନାଁ\t-1544\n▁ସଂଘ\t-1545\n▁Digi\t-1546\n▁ଗୁଳି\t-1547\n▁ଚିଠି\t-1548\n▁ତାକୁ\t-1549\n▁ତିନି\t-1550\n▁ସଚିବ\t-1551\n▁ତୃତୀୟ\t-1552\n▁ବିବାଦ\t-1553\n▁ସଂଗ୍ର\t-1554\n▁ସର୍ବା\t-1555\n▁ସୁସ୍ଥ\t-1556\n▁ସ୍କୁଲ\t-1557\nପୂର୍ଣ୍ଣ\t-1558\n▁ଚନ୍ଦ୍ର\t-1559\n▁ଚାଲିଛି\t-1560\n▁ଦେବାକୁ\t-1561\n▁ପଜିଟିଭ\t-1562\n▁ପ୍ରମାଣ\t-1563\n▁ବିରୋଧୀ\t-1564\n▁ସୋମବାର\t-1565\n▁ନିର୍ମାଣ\t-1566\n▁ସମ୍ପର୍କ\t-1567\n▁ସାହାଯ୍ୟ\t-1568\n▁ଅବସ୍ଥାରେ\t-1569\n▁କାର୍ଯ୍ୟା\t-1570\n▁ପ୍ରତିଯୋଗ\t-1571\n▁ପ୍ରଦର୍ଶନ\t-1572\n▁କୁହାଯାଇଛି\t-1573\nଇଁ\t-1574\nମର\t-1575\nସଂ\t-1576\nହଁ\t-1577\nୟର\t-1578\nୱା\t-1579\nଗଲେ\t-1580\nଠାଇ\t-1581\nଳରୁ\t-1582\nହାଁ\t-1583\n▁ଫା\t-1584\nଣ୍ଣି\t-1585\nନ୍ଧି\t-1586\nାନ୍ୟ\t-1587\nିତ୍ୱ\t-1588\n▁ଗୁଜ\t-1589\n▁ଦିଗ\t-1590\n▁ଦୂର\t-1591\n▁ହାସ\t-1592\n▁ହୃଦ\t-1593\nସ୍ଥାନ\t-1594\n▁ଏମିତି\t-1595\n▁ନମ୍ବର\t-1596\n▁ପଣ୍ଡା\t-1597\n▁ମୂଲ୍ୟ\t-1598\n▁ସମର୍ଥ\t-1599\n▁ସମ୍ମି\t-1600\nନ୍ତ୍ରଣା\t-1601\n▁କିମ୍ବା\t-1602\n▁ଗଞ୍ଜାମ\t-1603\n▁ପ୍ରଭାବ\t-1604\n▁ଶିକ୍ଷା\t-1605\n▁କ୍ଷେତ୍ର\t-1606\n▁ଗୁରୁତ୍ୱ\t-1607\n▁ପ୍ରକଳ୍ପ\t-1608\n▁ମାଧ୍ୟମରେ\t-1609\n▁ସର୍ବାଧିକ\t-1610\n▁ହେଉଛନ୍ତି\t-1611\nma\t-1612\nଗୋ\t-1613\nଡୁ\t-1614\nୋପ\t-1615\n▁?\t-1616\ntiz\t-1617\nଂସଦ\t-1618\nଡିୟ\t-1619\nଦ୍ୱ\t-1620\nବ୍ଦ\t-1621\nଳନୀ\t-1622\nୀମା\t-1623\n▁ପୀ\t-1624\n▁ଭର\t-1625\n▁୨୧\t-1626\nକ୍ଷେ\t-1627\nତାବକ\t-1628\nଳମ୍ବ\t-1629\n୍ଞାନ\t-1630\n▁ଉଠି\t-1631\n▁ଉଲ୍\t-1632\n▁କରେ\t-1633\n▁ଜଣା\t-1634\n▁ଫେର\t-1635\n▁ବ୍ର\t-1636\n▁ଭୋଟ\t-1637\ntized\t-1638\nପୁରରୁ\t-1639\nର୍ଣ୍ଣ\t-1640\n▁ଆକାଶ\t-1641\n▁କମିଶ\t-1642\n▁କୁକୁ\t-1643\n▁ଦାଖଲ\t-1644\n▁ଦେଶର\t-1645\n▁ପ୍ରି\t-1646\n▁ମଙ୍ଗ\t-1647\n▁ମିନି\t-1648\n▁ରାତି\t-1649\n▁ସୁନା\t-1650\nଥାନ୍ତି\t-1651\n▁ଆତ୍ମହ\t-1652\n▁ଆସିବା\t-1653\n▁ଗାଁରେ\t-1654\n▁ନେତୃତ\t-1655\n▁ବନ୍ଧୁ\t-1656\n▁ସାଂସଦ\t-1657\n▁କୋଭିଡ୍\t-1658\n▁ଜରିଆରେ\t-1659\n▁ଟଙ୍କାର\t-1660\n▁ବିଳମ୍ବ\t-1661\n▁ମନ୍ଦିର\t-1662\n▁ସ୍ଥିତି\t-1663\n▁ପରିସ୍ଥି\t-1664\n▁ରହିଥିଲା\t-1665\n▁ସଂକ୍ରମଣ\t-1666\n▁ସନ୍ଧ୍ୟା\t-1667\n▁ସମ୍ମିଳନୀ\t-1668\n▁Digitized\t-1669\n▁ସମ୍ପର୍କରେ\t-1670\nଉନ\t-1671\nତଃ\t-1672\nଧୁ\t-1673\nପଦ\t-1674\n▁ଠ\t-1675\nଟ୍ର\t-1676\nପାଗ\t-1677\nଲରୁ\t-1678\nଳିକ\t-1679\nସାଦ\t-1680\nୁତି\t-1681\nନ୍ଦା\t-1682\nନ୍ଦୋ\t-1683\nପଞ୍ଚ\t-1684\nପାଦକ\t-1685\nପ୍ରେ\t-1686\nବିଧା\t-1687\n▁ଅଂଚ\t-1688\n▁ଜମି\t-1689\n▁ତୁର\t-1690\n▁ରହୁ\t-1691\n▁ଶରୀ\t-1692\nସ୍ତ୍ର\t-1693\n▁ଅକ୍ଷ\t-1694\n▁ବାର୍\t-1695\n▁ରାଉତ\t-1696\n▁ଷ୍ଟେ\t-1697\nକ୍ରବାର\t-1698\n▁ଘଟଣାର\t-1699\n▁ଘଣ୍ଟା\t-1700\n▁ଦିନରେ\t-1701\n▁ପାତ୍ର\t-1702\n▁ଲକ୍ଷ୍\t-1703\n▁ସଦସ୍ୟ\t-1704\n▁କାରଣରୁ\t-1705\n▁ତୁରନ୍ତ\t-1706\n▁ମାନଙ୍କ\t-1707\n▁ନେତୃତ୍ୱ\t-1708\n▁ଷ୍ଟାଡିୟ\t-1709\n▁ସୂର୍ଯ୍ୟ\t-1710\n▁ପ୍ରତ୍ୟେକ\t-1711\n▁ହସ୍ପିଟାଲରେ\t-1712\nଠୀ\t-1713\nଳକ\t-1714\nକେଟ\t-1715\nଗଲା\t-1716\nଲେଖ\t-1717\nୁଟି\t-1718\nୋଟେ\t-1719\n▁ଭୟ\t-1720\n▁୨୩\t-1721\nକ୍ଟର\t-1722\nଜିଲା\t-1723\nଟ୍ରେ\t-1724\nବାରେ\t-1725\nାର୍ଟ\t-1726\n୍ୟରେ\t-1727\n▁ଅକ୍\t-1728\n▁ଗୀତ\t-1729\n▁ନଜର\t-1730\n▁ନାଗ\t-1731\n▁ପଢ଼\t-1732\n▁ପାର\t-1733\n▁ଭାଗ\t-1734\n▁ମଣି\t-1735\n▁ସ୍ମ\t-1736\nହନ୍ତି\t-1737\n▁କେଉଁ\t-1738\n▁ଖୁସି\t-1739\n▁ଗସ୍ତ\t-1740\n▁ଘରକୁ\t-1741\n▁ଦଣ୍ଡ\t-1742\n▁ଭେଟି\t-1743\n▁ଲାଗୁ\t-1744\n▁ହାସଲ\t-1745\n▁ଆମେରି\t-1746\n▁କାର୍ଡ\t-1747\n▁କାହିଁ\t-1748\n▁ଗାଡ଼ି\t-1749\n▁ଜାତୀୟ\t-1750\n▁ବିଚାର\t-1751\n▁ସ୍ଥିତ\t-1752\n▁ପରୀକ୍ଷ\t-1753\n▁ବର୍ଷୀୟ\t-1754\n▁ବୈଠକରେ\t-1755\n▁ରେକର୍ଡ\t-1756\n▁ସମ୍ମୁଖ\t-1757\n▁ଦାୟିତ୍ୱ\t-1758\n▁ପାଣିପାଗ\t-1759\n▁ପ୍ରସଙ୍ଗ\t-1760\n▁ଶୁକ୍ରବାର\t-1761\n▁ସଂକ୍ରମିତ\t-1762\n▁କାର୍ଯ୍ୟକ୍ରମରେ\t-1763\nଝର\t-1764\nମଣ\t-1765\nଂସା\t-1766\nଦେବ\t-1767\nବ୍ର\t-1768\nଶ୍ଚ\t-1769\nଷ୍ଣ\t-1770\nସନ୍\t-1771\n୍ୱି\t-1772\n▁ଜୟ\t-1773\n▁ଢେ\t-1774\n▁ତର\t-1775\n▁ମୟ\t-1776\nକ୍କା\t-1777\nଶ୍ୱର\t-1778\n୍ରୀୟ\t-1779\n▁ଉଦ୍\t-1780\n▁ଏସ୍\t-1781\n▁ଚୀନ\t-1782\n▁ତାପ\t-1783\n▁ବାଇ\t-1784\n▁ମେଡ\t-1785\n▁ମୋଟ\t-1786\n▁ଶନି\t-1787\n▁ଶିବ\t-1788\nପାରିବ\t-1789\n▁କଲେଜ\t-1790\n▁କାହା\t-1791\n▁ଗ୍ୟା\t-1792\n▁ଘରୋଇ\t-1793\nହାଣ୍ଡି\t-1794\n▁ଅନୁଭବ\t-1795\n▁ଆନ୍ଦୋ\t-1796\n▁କହିଛି\t-1797\n▁ଖାଦ୍ୟ\t-1798\n▁ଦ୍ବାର\t-1799\n▁ପାଖରେ\t-1800\n▁ବାହାର\t-1801\n▁ବିହାର\t-1802\n▁ଭଦ୍ରକ\t-1803\n▁ଅଞ୍ଚଳର\t-1804\n▁ବିଶ୍ୱକ\t-1805\n▁ବ୍ରାଉନ\t-1806\n▁ମୁତାବକ\t-1807\n▁ଯାହାକୁ\t-1808\n▁ରବିବାର\t-1809\n▁ସରପଞ୍ଚ\t-1810\n▁ଭର୍ତ୍ତି\t-1811\n▁ସବୁଠାରୁ\t-1812\n▁ମିଳିଥିଲା\t-1813\n▁ଆତ୍ମହତ୍ୟା\t-1814\n▁କରିପାରିବେ\t-1815\n▁ସେମାନଙ୍କୁ\t-1816\n▁କାର୍ଯ୍ୟାଳୟ\t-1817\nପୀ\t-1818\n▁C\t-1819\nକରି\t-1820\nଚରା\t-1821\nପାର\t-1822\nମତା\t-1823\n▁ଆଫ\t-1824\n▁ଗୌ\t-1825\n▁ଚୌ\t-1826\n▁ଥର\t-1827\n▁ସଭ\t-1828\nmail\t-1829\nଦ୍ରା\t-1830\nନିର୍\t-1831\nନ୍ତର\t-1832\nପ୍ତା\t-1833\nଲକ୍ଷ\t-1834\nସନରେ\t-1835\nିକାଲ\t-1836\n▁ଆଶା\t-1837\n▁ଉପକ\t-1838\n▁ଖୁବ\t-1839\n▁ନର୍\t-1840\nକୋର୍ଟ\t-1841\nନ୍ନାଥ\t-1842\n▁କ୍ରି\t-1843\n▁ଠାରେ\t-1844\n▁ବଲିଉ\t-1845\n▁ବାହା\t-1846\n▁ମାରି\t-1847\n▁ରାଶି\t-1848\n▁ସେଥି\t-1849\n▁ଓଡ଼ିଆ\t-1850\n▁କହିବା\t-1851\n▁ଯୋଗାଇ\t-1852\n▁ଇଣ୍ଡିଆ\t-1853\n▁କରନ୍ତି\t-1854\n▁ପରିଚାଳ\t-1855\n▁ଯାତ୍ରୀ\t-1856\n▁ଶନିବାର\t-1857\n▁କରିବାରେ\t-1858\n▁ସାମଗ୍ରୀ\t-1859\n▁ରହିଛନ୍ତି\t-1860\n▁ଜିଲ୍ଲାପାଳ\t-1861\n▁କରାଯାଇଥିବା\t-1862\n▁ଆକ୍ରାନ୍ତଙ୍କ\t-1863\n..\t-1864\ner\t-1865\nin\t-1866\nଟନ\t-1867\nହୀ\t-1868\ncom\t-1869\nଏମ୍\t-1870\nଡରେ\t-1871\nଧୀନ\t-1872\nବିଦ\t-1873\nବିଷ\t-1874\nସ୍ଫ\t-1875\n▁ଏତ\t-1876\n▁ଥା\t-1877\n▁ଯୋ\t-1878\n▁ଲୋ\t-1879\n▁ଶୋ\t-1880\n▁ସୌ\t-1881\nନ୍ୟା\t-1882\nସିବି\t-1883\n୍ରାସ\t-1884\n▁ଡ୍ର\t-1885\n▁ତାର\t-1886\n▁ଦୋଷ\t-1887\n▁ନୟା\t-1888\n▁ପାଗ\t-1889\n▁ବସ୍\t-1890\n▁ମସି\t-1891\n▁ସ୍କ\t-1892\nବରଙ୍ଗ\t-1893\nବିନ୍ଦ\t-1894\nିବାରେ\t-1895\n▁ଗାଁର\t-1896\n▁ନେବା\t-1897\n▁ପକାଇ\t-1898\n▁ପତ୍ର\t-1899\n▁ପିପି\t-1900\n▁ବାୟୁ\t-1901\n▁ମିଳୁ\t-1902\n▁ଶବ୍ଦ\t-1903\nଗୁଡ଼ିକ\t-1904\nତନ୍ତ୍ର\t-1905\n▁ମଙ୍ଗଳ\t-1906\n▁ଶକ୍ତି\t-1907\n▁ସଂସ୍କ\t-1908\n▁ହ୍ରାସ\t-1909\n▁ସ୍ୱାମୀ\t-1910\n▁ଅନ୍ୟପଟେ\t-1911\n▁ଆପଣଙ୍କୁ\t-1912\n▁କରାଯିବା\t-1913\n▁ମାନଙ୍କୁ\t-1914\n▁ସାମ୍ନାକୁ\t-1915\n▁କଳାହାଣ୍ଡି\t-1916\nes\t-1917\nis\t-1918\nଆଁ\t-1919\nଗଣ\t-1920\nଘୁ\t-1921\nଚେ\t-1922\n▁2\t-1923\n▁P\t-1924\nକଟକ\t-1925\nକାଉ\t-1926\nକାଳ\t-1927\nଜିକ\t-1928\nତାର\t-1929\nତିଶ\t-1930\nତେକ\t-1931\nପଡା\t-1932\nପୁଟ\t-1933\nବତୀ\t-1934\nବୀର\t-1935\nର୍ଭ\t-1936\nଲିଆ\t-1937\nହ୍ମ\t-1938\nୂଳକ\t-1939\n୍ଲା\t-1940\n▁ଚଢ\t-1941\n▁ଭଗ\t-1942\n▁୨୮\t-1943\n▁ୱେ\t-1944\nଗରରେ\t-1945\nଧ୍ୟା\t-1946\nନନ୍ଦ\t-1947\nବଙ୍ଗ\t-1948\nମ୍ପି\t-1949\nର୍ମା\t-1950\nହୁରି\t-1951\nହେଲେ\t-1952\nାଉନ୍\t-1953\nାମିନ\t-1954\nୈତିକ\t-1955\n୍ୟାଣ\t-1956\n▁ଖାଇ\t-1957\n▁ଜଗତ\t-1958\n▁ପୁର\t-1959\n▁ବଢ଼\t-1960\n▁ମାଓ\t-1961\n▁ଯୁବ\t-1962\n▁ରୂପ\t-1963\n▁ଶାହ\t-1964\n▁୧୦୦\t-1965\n▁୨୦୨\t-1966\nକ୍ରମଣ\t-1967\nମନ୍ତେ\t-1968\n▁କେତେ\t-1969\n▁କୋରା\t-1970\n▁ଖୋର୍\t-1971\n▁ଖୋଲି\t-1972\n▁ଭକ୍ତ\t-1973\n▁ମହାମ\t-1974\n▁ମାନସ\t-1975\n▁ରିଲି\t-1976\n▁ଶୁଣା\t-1977\n▁ଶୁଣି\t-1978\n▁ସାହି\t-1979\n▁ସିଂହ\t-1980\n▁ସ୍ଥା\t-1981\n▁ହେବେ\t-1982\n▁ଅନୁଗୁ\t-1983\n▁ଆହୁରି\t-1984\n▁କର୍ମୀ\t-1985\n▁ଦେଖାଇ\t-1986\n▁ହିନ୍ଦ\t-1987\nବର୍ତ୍ତୀ\t-1988\n▁ଆକ୍ରମଣ\t-1989\n▁ଉଲ୍ଲେଖ\t-1990\n▁ଥିବାରୁ\t-1991\n▁ଦିଆଯାଇ\t-1992\n▁ଦିଆଯିବ\t-1993\n▁ଭାଙ୍ଗି\t-1994\n▁ରାସ୍ତା\t-1995\n▁ଅପରାହ୍ନ\t-1996\n▁ଆନ୍ଦୋଳନ\t-1997\n▁କମ୍ପାନୀ\t-1998\n▁ବ୍ୟାଙ୍କ\t-1999\n▁ହୋଇପାରେ\t-2000\n▁ଯେଉଁଥିରେ\t-2001\n▁ଯେତେବେଳେ\t-2002\n▁ଅଧିକାରୀଙ୍କ\t-2003\n▁ପର୍ଯ୍ୟାୟରେ\t-2004\nru\t-2005\nଗେ\t-2006\nଝି\t-2007\nଫେ\t-2008\nରୋ\t-2009\nେହ\t-2010\n▁A\t-2011\n▁ୟ\t-2012\n▁୦\t-2013\nଜିତ\t-2014\nମୀର\t-2015\nସ୍ୱ\t-2016\nହୁଁ\t-2017\nୁଛି\t-2018\nୁଝର\t-2019\n▁ଇନ\t-2020\n▁ଗଜ\t-2021\n▁ଟା\t-2022\n▁ପସ\t-2023\n▁ଫୁ\t-2024\n▁୨୨\t-2025\n▁୬୦\t-2026\nକାବି\t-2027\nଜପୁର\t-2028\nଟିରେ\t-2029\nପାରୁ\t-2030\nଳିକା\t-2031\nସେନା\t-2032\nୃକ୍ତ\t-2033\n▁ଅଂଶ\t-2034\n▁ଏତେ\t-2035\n▁ଙ୍କ\t-2036\n▁ଭୁଲ\t-2037\n▁ସଂକ\t-2038\n▁ହରି\t-2039\nକଙ୍କୁ\t-2040\nଥିବାର\t-2041\nନ୍ଧାନ\t-2042\nାମାନେ\t-2043\nୀକ୍ଷା\t-2044\n▁ଆଗରେ\t-2045\n▁ଓଡିଶ\t-2046\n▁କୃଷି\t-2047\n▁ଗ୍ରୁ\t-2048\n▁ଜେନା\t-2049\n▁ଦର୍ଶ\t-2050\n▁ରୁଜୁ\t-2051\nକାବିଲା\t-2052\n▁ଅଗ୍ନି\t-2053\n▁ଖାଇବା\t-2054\n▁ଗାନ୍ଧ\t-2055\n▁ଗୁଜରା\t-2056\n▁ଗ୍ରାହ\t-2057\n▁ବିସ୍ଫ\t-2058\n▁ସମ୍ବଲ\t-2059\nବର୍ତ୍ତନ\t-2060\n▁କଲ୍ୟାଣ\t-2061\n▁ଘଟଣାରେ\t-2062\n▁ଦ୍ବାରା\t-2063\n▁ନ୍ୟୁଜ୍\t-2064\n▁ବିକ୍ରି\t-2065\n▁ବିଭାଗର\t-2066\n▁ଭାରତରେ\t-2067\n▁ମହାରଣା\t-2068\n▁ହେବାରୁ\t-2069\n▁କାହିଁକି\t-2070\n▁ନିମନ୍ତେ\t-2071\n▁ବ୍ୟୁରୋଃ\t-2072\n▁ଯାଇଥିଲା\t-2073\n▁କେନ୍ଦ୍ରା\t-2074\n▁ବଲାଙ୍ଗିର\t-2075\nଭୁବନେଶ୍ୱରଃ\t-2076\n▁କ୍ଷେତ୍ରରେ\t-2077\n▁ଲେଖିଛନ୍ତି\t-2078\n▁ପ୍ରତିକ୍ରିୟା\t-2079\nka\t-2080\nଃଖ\t-2081\nଢା\t-2082\nତକ\t-2083\nତୀ\t-2084\nମଦ\t-2085\njan\t-2086\nଜରେ\t-2087\nତରେ\t-2088\nଥିକ\t-2089\nଥିବ\t-2090\nନିକ\t-2091\nବନୀ\t-2092\n▁ଅଲ\t-2093\n▁କଟ\t-2094\n▁କବ\t-2095\n▁ଗେ\t-2096\n▁ଠା\t-2097\n▁ରଣ\t-2098\nଞ୍ଜୟ\t-2099\nଣ୍ଡର\t-2100\nଣ୍ଡେ\t-2101\nତ୍ନୀ\t-2102\nନିଧି\t-2103\nନୀତି\t-2104\nନେତା\t-2105\nନ୍ସି\t-2106\n▁sru\t-2107\n▁ଉପଲ\t-2108\n▁କାଳ\t-2109\n▁ତିଆ\t-2110\n▁ବେଶ\t-2111\n▁ମୋର\t-2112\n▁ରେଳ\t-2113\ngmail\t-2114\nକ୍ଷେପ\t-2115\nରିକ୍ତ\t-2116\nସ୍କାର\t-2117\nୁଥିବା\t-2118\n▁ଅଷ୍ଟ\t-2119\n▁ଏତଲା\t-2120\n▁କଷ୍ଟ\t-2121\n▁ଗୁଡି\t-2122\n▁ଟ୍ୱି\t-2123\n▁ଧୀରେ\t-2124\n▁ବଙ୍ଗ\t-2125\n▁ବଞ୍ଚ\t-2126\n▁ସକାଳ\t-2127\n▁ସନ୍ଦ\t-2128\n▁ସିବି\t-2129\n▁ହରାଇ\t-2130\n▁ୱାର୍\t-2131\nଓଡ଼ିଶା\t-2132\n▁ଆଗାମୀ\t-2133\n▁କେତେକ\t-2134\n▁କ୍ଷତି\t-2135\n▁ଗୁଡିକ\t-2136\n▁ପସନ୍ଦ\t-2137\n▁ପ୍ରଭୁ\t-2138\n▁ଫାଇନା\t-2139\n▁ବର୍ଷର\t-2140\n▁ରହିବା\t-2141\n▁ଶିଳ୍ପ\t-2142\n▁ସଫଳତା\t-2143\n▁ସେପଟେ\t-2144\nମୁମ୍ବାଇ\t-2145\n▁srujan\t-2146\n▁ଆର୍ଥିକ\t-2147\n▁କର୍ତ୍ତ\t-2148\n▁ଗଞ୍ଜେଇ\t-2149\n▁ଟ୍ରେନ୍\t-2150\n▁ପିପିଲି\t-2151\n▁ଭାଇରାଲ\t-2152\n▁ମାମଲାର\t-2153\n▁ସୁବିଧା\t-2154\n▁ଓଡ଼ିଶାର\t-2155\n▁କରୁଥିଲେ\t-2156\n▁ଡାକ୍ତରଖ\t-2157\n▁ଦୁର୍ଘଟଣ\t-2158\n▁ମୁମ୍ବାଇ\t-2159\nନ୍ତ୍ରଣାଳୟ\t-2160\n▁ଅତିରିକ୍ତ\t-2161\n▁ନରେନ୍ଦ୍ର\t-2162\n▁ବାଲେଶ୍ୱର\t-2163\n▁ମହିଳାଙ୍କ\t-2164\n▁ମୁକାବିଲା\t-2165\n▁ଭୁବନେଶ୍ବର\t-2166\n▁ମନ୍ତ୍ରଣାଳୟ\t-2167\nth\t-2168\nରମ\t-2169\n▁S\t-2170\n▁ଔ\t-2171\n▁ଷ\t-2172\nika\t-2173\nଙ୍ଖ\t-2174\nଦାମ\t-2175\nବାଟ\t-2176\nରିୟ\t-2177\nଲାଣ\t-2178\nସାଇ\t-2179\nୋରଣ\t-2180\n▁କଣ\t-2181\n▁ଛେ\t-2182\n▁ଦଶ\t-2183\n▁ଦୂ\t-2184\n▁ଶବ\t-2185\n▁ଶୀ\t-2186\nଞ୍ଜି\t-2187\nଭଞ୍ଜ\t-2188\nଲଣ୍ଡ\t-2189\n୍ୟାନ\t-2190\n▁ଅଟକ\t-2191\n▁ଅସୁ\t-2192\n▁ଆଖି\t-2193\n▁ଖଣି\t-2194\n▁ଖସି\t-2195\n▁ଗତି\t-2196\n▁ଟିମ\t-2197\n▁ଟୁଇ\t-2198\n▁ଦୋକ\t-2199\n▁ପାଉ\t-2200\n▁ବୁଧ\t-2201\n▁ରନ୍\t-2202\n▁ଲେଖ\t-2203\n▁ସ୍ବ\t-2204\nଅନ୍ତୁ\t-2205\nକ୍ରିୟ\t-2206\nଣ୍ଡରେ\t-2207\nଧ୍ୟାନ\t-2208\nନୈତିକ\t-2209\nମାଧ୍ୟ\t-2210\n▁ଅପରେ\t-2211\n▁ଉନ୍ନ\t-2212\n▁କେବେ\t-2213\n▁ଜାତି\t-2214\n▁ତାପମ\t-2215\n▁ଦୁଃଖ\t-2216\n▁ନାମକ\t-2217\n▁ପଠାଇ\t-2218\n▁ପରାମ\t-2219\n▁ବିଜୁ\t-2220\n▁ବୟାନ\t-2221\n▁ଅଧ୍ୟା\t-2222\n▁ଏଥିସହ\t-2223\n▁ଚିତ୍ର\t-2224\n▁ଭିନ୍ନ\t-2225\n▁ମିଶାଇ\t-2226\n▁ଶେଷରେ\t-2227\n▁ସଂଗଠନ\t-2228\n▁ସବୁଠୁ\t-2229\n▁ହାତରେ\t-2230\nଜିଲାନ୍ସ\t-2231\n▁ଆଶଙ୍କା\t-2232\n▁ଉଦ୍ଦେଶ\t-2233\n▁ନବରଙ୍ଗ\t-2234\n▁ପୋଲିସ୍\t-2235\n▁ପ୍ରଶଂସ\t-2236\n▁ବିଦ୍ୟୁ\t-2237\n▁ଶ୍ରମିକ\t-2238\n▁ସମସ୍ତେ\t-2239\n▁ସ୍ତ୍ରୀ\t-2240\n▁ଅଗ୍ନିଶମ\t-2241\n▁ଅଭିନେତା\t-2242\n▁ଆଦିବାସୀ\t-2243\n▁କୋରାପୁଟ\t-2244\n▁ଗଣମାଧ୍ୟ\t-2245\n▁ଗୁରୁବାର\t-2246\n▁ନେଇଥିଲେ\t-2247\n▁ପରାମର୍ଶ\t-2248\n▁ମହାମାରୀ\t-2249\n▁ଯାଇଥିବା\t-2250\n▁ସମୀକ୍ଷା\t-2251\n▁ବିଧାନସଭା\t-2252\n▁ବିସ୍ଫୋରଣ\t-2253\n▁ରାଜ୍ୟପାଳ\t-2254\n▁srujanika\t-2255\n▁ଅନୁସନ୍ଧାନ\t-2256\n▁କୁହାଯାଉଛି\t-2257\n▁ନେତୃତ୍ୱରେ\t-2258\n▁ପରିସ୍ଥିତି\t-2259\n▁ଭିଜିଲାନ୍ସ\t-2260\n▁ପରିବର୍ତ୍ତନ\t-2261\n▁ସମ୍ପୂର୍ଣ୍ଣ\t-2262\non\t-2263\nଚୀ\t-2264\nଧର\t-2265\nପୋ\t-2266\nସଭ\t-2267\nୋଭ\t-2268\nଗାଁ\t-2269\nଣ୍ଠ\t-2270\nଥ୍ୟ\t-2271\nଦରେ\t-2272\nନ୍ଥ\t-2273\nରିଆ\t-2274\nର୍ସ\t-2275\nଲକୁ\t-2276\nସବୁ\t-2277\nାନର\t-2278\n୍ୟର\t-2279\n▁ଅଟ\t-2280\n▁ଆଜ\t-2281\n▁ବଜ\t-2282\n▁ବୌ\t-2283\n▁ଲକ\t-2284\nଙ୍ଗୀ\t-2285\nମିଳି\t-2286\nରଙ୍କ\t-2287\nଲକାନ\t-2288\nଲଙ୍କ\t-2289\nଷାରେ\t-2290\nାଇଭର\t-2291\nାରସୁ\t-2292\nୀର୍ଘ\t-2293\nୂର୍ବ\t-2294\nୋର୍ଡ\t-2295\n▁ଏଥର\t-2296\n▁କହୁ\t-2297\n▁ଚୌଧ\t-2298\n▁ଥରେ\t-2299\n▁ନଦୀ\t-2300\n▁ଲାଭ\t-2301\n▁ସିଏ\t-2302\nଗୁଡିକ\t-2303\nଣ୍ଟରେ\t-2304\n▁ଆଗକୁ\t-2305\n▁ଉତ୍କ\t-2306\n▁କିଲୋ\t-2307\n▁ତଥ୍ୟ\t-2308\n▁ଦିବସ\t-2309\n▁ଦେଲେ\t-2310\n▁ଧନ୍ୟ\t-2311\n▁ନିୟମ\t-2312\n▁ବୈଠକ\t-2313\n▁ସଙ୍କ\t-2314\n▁ହୋଟେ\t-2315\nଯୁକ୍ତି\t-2316\nରିୟାନୀ\t-2317\nଷ୍କର୍ମ\t-2318\n▁ଅର୍ଜୁ\t-2319\n▁ଆଇପିଏ\t-2320\n▁କହିଲେ\t-2321\n▁କିପରି\t-2322\n▁ଚଳଚ୍ଚ\t-2323\n▁ଟେଷ୍ଟ\t-2324\n▁ଦୀର୍ଘ\t-2325\n▁ମାଲିକ\t-2326\n▁ମିଳିବ\t-2327\n▁ରଖିବା\t-2328\n▁ରାସ୍ତ\t-2329\n▁ଶ୍ରୀମ\t-2330\n▁ସୁଯୋଗ\t-2331\n▁ଅଧିକାର\t-2332\n▁ଏପରିକି\t-2333\n▁ଖେଳାଳି\t-2334\n▁ଚିନ୍ତା\t-2335\n▁ପ୍ରବେଶ\t-2336\n▁ଶୁଣାଣି\t-2337\n▁ସମ୍ମାନ\t-2338\n▁ସୁପ୍ରି\t-2339\nବିଦ୍ୟାଳୟ\t-2340\n▁ପଦକ୍ଷେପ\t-2341\n▁ବିଶ୍ୱାଳ\t-2342\n▁ମ୍ୟାଚରେ\t-2343\n▁ଲୋକମାନେ\t-2344\n▁ନାହାନ୍ତି\t-2345\n▁ପରିଚାଳନା\t-2346\n▁ପ୍ରତିବାଦ\t-2347\n▁ପ୍ରସ୍ତାବ\t-2348\n▁ବିରିୟାନୀ\t-2349\n▁ଚଳଚ୍ଚିତ୍ର\t-2350\n▁ସୁନ୍ଦରଗଡ଼\t-2351\nଛା\t-2352\nଦବ\t-2353\nମନ\t-2354\nମୟ\t-2355\nୂପ\t-2356\nକିଲ\t-2357\nଚେତ\t-2358\nଜନକ\t-2359\nଡିତ\t-2360\nତାପ\t-2361\nନିଆ\t-2362\nବୋଧ\t-2363\nବ୍ଧ\t-2364\nମତି\t-2365\nରିକ\t-2366\n▁ଅଭ\t-2367\n▁ଠି\t-2368\n▁ନକ\t-2369\n▁ଶହ\t-2370\nଗୁଡା\t-2371\nତିହା\t-2372\nନିକା\t-2373\nନ୍ଦନ\t-2374\nବାଇଁ\t-2375\nମଣ୍ଡ\t-2376\nାଜିକ\t-2377\nାର୍ଜ\t-2378\nୁଦ୍ର\t-2379\n▁ଖେଳ\t-2380\n▁ଗଠନ\t-2381\n▁ଚିତ\t-2382\n▁ଜୀବ\t-2383\n▁ଦୀପ\t-2384\n▁ମଲ୍\t-2385\n▁ରଶ୍\t-2386\nଙ୍କାନ\t-2387\nନଥିବା\t-2388\nନଥିଲେ\t-2389\nନୁମାନ\t-2390\nନ୍ତୋଷ\t-2391\nର୍ଦ୍ଧ\t-2392\nଶାନ୍ତ\t-2393\n▁ଅଗ୍ର\t-2394\n▁ଅଳ୍ପ\t-2395\n▁ଆକାଉ\t-2396\n▁କ୍ରୀ\t-2397\n▁ଚାରି\t-2398\n▁ଚୋରି\t-2399\n▁ଛତିଶ\t-2400\n▁ତ୍ରି\t-2401\n▁ବାହି\t-2402\n▁ବିଷୟ\t-2403\n▁ବ୍ରି\t-2404\n▁ମଣିଷ\t-2405\n▁ମନୋଜ\t-2406\n▁ଯାଏଁ\t-2407\n▁ଯିବା\t-2408\n▁ସିନେ\t-2409\n▁ସେନା\t-2410\nପ୍ରଦେଶ\t-2411\nାର୍ଯ୍ୟ\t-2412\n▁ଅଧିକା\t-2413\n▁ଆୟୋଜନ\t-2414\n▁କ୍ଷମା\t-2415\n▁ଚାଲାଣ\t-2416\n▁ଚାହୁଁ\t-2417\n▁ଝାରସୁ\t-2418\n▁ତିଆରି\t-2419\n▁ଦରକାର\t-2420\n▁ନ୍ୟାୟ\t-2421\n▁ବିଶ୍ବ\t-2422\n▁ବ୍ୟବସ\t-2423\n▁ରଖିଛି\t-2424\n▁ଶୀଘ୍ର\t-2425\n▁ସହରରେ\t-2426\n▁ସହାୟକ\t-2427\n▁ସାକ୍ଷ\t-2428\n▁ଆପଣଙ୍କ\t-2429\n▁ପରିମାଣ\t-2430\n▁ପ୍ରସାଦ\t-2431\n▁ବୁଧବାର\t-2432\n▁ଭାଷାରେ\t-2433\n▁ମିଳୁଛି\t-2434\n▁ଲକ୍ଷ୍ୟ\t-2435\n▁ସମର୍ଥନ\t-2436\nଲକାନଗିରି\t-2437\n▁ଏମାନଙ୍କ\t-2438\n▁କୁହାଯାଏ\t-2439\n▁କ୍ରିକେଟ\t-2440\n▁ଗ୍ରାମରେ\t-2441\n▁ଚର୍ଚ୍ଚା\t-2442\n▁ଜଗନ୍ନାଥ\t-2443\n▁ସମ୍ପାଦକ\t-2444\n▁ସାମାଜିକ\t-2445\n▁ଅନ୍ୟାନ୍ୟ\t-2446\n▁କେନ୍ଦୁଝର\t-2447\n▁ବିଳମ୍ବିତ\t-2448\n▁ମଙ୍ଗଳବାର\t-2449\n▁ସମ୍ବଲପୁର\t-2450\n▁ସନ୍ଧ୍ୟାରେ\t-2451\n▁ପଶ୍ଚିମବଙ୍ଗ\t-2452\n▁ରାଷ୍ଟ୍ରପତି\t-2453\n▁ଷ୍ଟାଡିୟମରେ\t-2454\nor\t-2455\nta\t-2456\nଏନ\t-2457\nଏଫ\t-2458\nଷଦ\t-2459\n୦୮\t-2460\n▁R\t-2461\nକଟି\t-2462\nତୀତ\t-2463\nଶୋର\t-2464\nହରେ\t-2465\nାମା\t-2466\nିକି\t-2467\n▁ଆଚ\t-2468\n▁ଏମ\t-2469\n▁ଜୋ\t-2470\n▁ପୃ\t-2471\n▁ଫଳ\t-2472\n▁ବ୍\t-2473\n▁ମଦ\t-2474\n▁୩୨\t-2475\nକ୍ରେ\t-2476\nକ୍ଷକ\t-2477\nକ୍ଷମ\t-2478\nଗଡ଼ା\t-2479\nଟୋବର\t-2480\nଟ୍ରି\t-2481\nଡ଼ିଆ\t-2482\nଥମିକ\t-2483\nବାନୁ\t-2484\nବ୍ୟା\t-2485\nମିତା\t-2486\nମ୍ମଦ\t-2487\nଳୟରେ\t-2488\nିଷ୍ଟ\t-2489\nୃଙ୍ଖ\t-2490\nୋପସା\t-2491\n୍ୟ୍ୟ\t-2492\n▁ଅପେ\t-2493\n▁ଏମ୍\t-2494\n▁କୃଷ\t-2495\n▁ଚତୁ\t-2496\n▁ଟିକ\t-2497\n▁ଧରା\t-2498\n▁ପଡ଼\t-2499\n▁ପଶି\t-2500\n▁ପାଲ\t-2501\n▁ବଢି\t-2502\n▁ଶୀତ\t-2503\n▁୪ଟି\t-2504\nକାଳୀନ\t-2505\nକ୍ଷିତ\t-2506\nଗୁଡ଼ା\t-2507\nଗ୍ରାମ\t-2508\nତିହାସ\t-2509\nନ୍ଧ୍ର\t-2510\nଶ୍ଚିତ\t-2511\nାନାରେ\t-2512\n▁ଅତ୍ୟ\t-2513\n▁ଆଶ୍ଚ\t-2514\n▁ଓକିଲ\t-2515\n▁ଗୁଗୁ\t-2516\n▁ଙ୍କୁ\t-2517\n▁ଚଢାଉ\t-2518\n▁ଚାକି\t-2519\n▁ଡିଜି\t-2520\n▁ଦେବେ\t-2521\n▁ପାରା\t-2522\n▁ପ୍ରୋ\t-2523\n▁ପ୍ୟା\t-2524\n▁ବଢ଼ି\t-2525\n▁ବାଲି\t-2526\n▁ମାଡ଼\t-2527\n▁ଶାସନ\t-2528\n▁ହୋଇପ\t-2529\nନ୍ତ୍ରଣ\t-2530\nବର୍ଣ୍ଣ\t-2531\n▁ଅପରାଧ\t-2532\n▁ଆନନ୍ଦ\t-2533\n▁ଉପକୂଳ\t-2534\n▁କମିଟି\t-2535\n▁ଗଜପତି\t-2536\n▁ଗୋଷ୍ଠ\t-2537\n▁ଛାଡ଼ି\t-2538\n▁ଦୃଶ୍ୟ\t-2539\n▁ପ୍ରିୟ\t-2540\n▁ବରଗଡ଼\t-2541\n▁ଯାଉଛି\t-2542\n▁ଶର୍ମା\t-2543\n▁ସଂଯୋଗ\t-2544\n▁ସକାଳେ\t-2545\n▁ସପ୍ତା\t-2546\n▁ସେଣ୍ଟ\t-2547\nଙ୍କାନାଳ\t-2548\n▁କରିଥାଏ\t-2549\n▁କରିଦେଇ\t-2550\n▁କାରବାର\t-2551\n▁ଘଟଣାକୁ\t-2552\n▁ବାହିନୀ\t-2553\n▁ବିଶ୍ୱର\t-2554\n▁ଯାତ୍ରା\t-2555\n▁ଯୋଗଦେଇ\t-2556\n▁ସନ୍ଦେହ\t-2557\n▁ହୋଇଯାଇ\t-2558\n▁ଅକ୍ଟୋବର\t-2559\n▁କର୍ତ୍ତୃ\t-2560\n▁ଡ୍ରାଇଭର\t-2561\n▁ଧନ୍ୟବାଦ\t-2562\n▁ବିଶିଷ୍ଟ\t-2563\n▁ମାର୍ଚ୍ଚ\t-2564\n▁ମେଡିକାଲ\t-2565\n▁ସେହିଭଳି\t-2566\n▁ଅଭିଯୋଗରେ\t-2567\n▁କଂଗ୍ରେସର\t-2568\n▁ଗଣମାଧ୍ୟମ\t-2569\n▁ନିଯୁକ୍ତି\t-2570\n▁ନେଇଛନ୍ତି\t-2571\n▁ପାଣିଗ୍ରା\t-2572\n▁ପ୍ରଶାନ୍ତ\t-2573\n▁ପ୍ରାଥମିକ\t-2574\n▁ବଙ୍ଗୋପସା\t-2575\n▁ବଲାଙ୍ଗୀର\t-2576\n▁ରାଜସ୍ଥାନ\t-2577\n▁ସଂପର୍କରେ\t-2578\n▁ଆଶ୍ଚର୍ଯ୍ୟ\t-2579\n▁ଜଣାଇଛନ୍ତି\t-2580\n▁ସ୍ବାସ୍ଥ୍ୟ\t-2581\n▁ନିର୍ଦ୍ଦେଶକ\t-2582\n▁ସମ୍ମିଳନୀରେ\t-2583\n▁କାର୍ଯ୍ୟକାରୀ\t-2584\n▁ଗୁରୁତ୍ୱପୂର୍ଣ୍ଣ\t-2585\new\t-2586\nଓ୍\t-2587\nଥୁ\t-2588\nଫଳ\t-2589\nଫି\t-2590\nରତ\t-2591\nସୂ\t-2592\nେଦ\t-2593\n୧୧\t-2594\n▁.\t-2595\n▁O\t-2596\nକୃତ\t-2597\nଟରୁ\t-2598\nଥରେ\t-2599\nପଦା\t-2600\nଫରୁ\t-2601\nଯୋଜ\t-2602\nରକେ\t-2603\nରେଶ\t-2604\nାଉନ\t-2605\nାୟଣ\t-2606\n▁20\t-2607\n▁ଅଗ\t-2608\n▁ଆପ\t-2609\n▁ଏଇ\t-2610\n▁ଜ୍\t-2611\n▁ଦୌ\t-2612\n▁ଭଉ\t-2613\n▁ମୌ\t-2614\n▁ଲଢ\t-2615\n▁ସତ\t-2616\n▁୨୭\t-2617\n▁୩୭\t-2618\n▁୪୦\t-2619\n▁୭୦\t-2620\n▁୭୫\t-2621\nକ୍ସି\t-2622\nତିକି\t-2623\nନ୍ଦି\t-2624\nନ୍ୟୁ\t-2625\nପ୍ଟେ\t-2626\nଲାଣି\t-2627\nାନୁଆ\t-2628\nୋଇଲା\t-2629\n▁ଆଲି\t-2630\n▁କେର\t-2631\n▁ଖରା\t-2632\n▁ଚଢ଼\t-2633\n▁ଟାଇ\t-2634\n▁ଦଶମ\t-2635\n▁ଦ୍ବ\t-2636\n▁ଦ୍ର\t-2637\n▁ନିର\t-2638\n▁ପଟେ\t-2639\n▁ପୁନ\t-2640\n▁ପୂଜ\t-2641\n▁ବରଂ\t-2642\n▁ମାଝ\t-2643\n▁ଯିଏ\t-2644\n▁ଲକଡ\t-2645\n▁ଲଘୁ\t-2646\n▁ଶପଥ\t-2647\n▁ସଦର\t-2648\n▁ସମି\t-2649\n▁ସାର\t-2650\n▁ସେଇ\t-2651\n▁ସୋନ\t-2652\n▁ହେଇ\t-2653\n▁୨ଟି\t-2654\n▁୩ଟି\t-2655\nକ୍ଷୋଭ\t-2656\nତ୍ତ୍ୱ\t-2657\nନ୍ନାଇ\t-2658\nର୍ତ୍ତ\t-2659\n▁ଅବନୀ\t-2660\n▁ଆଞ୍ଚ\t-2661\n▁ଆଦାୟ\t-2662\n▁ଏୟାର\t-2663\n▁ଗର୍ଭ\t-2664\n▁ଛେଳି\t-2665\n▁ଟ୍ୟା\t-2666\n▁ଧାରା\t-2667\n▁ନାବା\t-2668\n▁ପାରୁ\t-2669\n▁ପିଟି\t-2670\n▁ପ୍ରଚ\t-2671\n▁ବର୍ଗ\t-2672\n▁ବାବୁ\t-2673\n▁ବିପଦ\t-2674\n▁ଭାଷା\t-2675\n▁ମନ୍ତ\t-2676\n▁ମୟୂର\t-2677\n▁ଯାଦବ\t-2678\n▁ରୋଗୀ\t-2679\n▁ସୀମା\t-2680\n▁ୱିକେ\t-2681\nଭାରମ୍ଭ\t-2682\nହ୍ମପୁର\t-2683\n▁ଅଞ୍ଚଳ\t-2684\n▁ଅଭିନୟ\t-2685\n▁ଜାନୁଆ\t-2686\n▁ଟିକେଟ\t-2687\n▁ଦୋକାନ\t-2688\n▁ଧକ୍କା\t-2689\n▁ପରିଷଦ\t-2690\n▁ପ୍ରେମ\t-2691\n▁ବୌଦ୍ଧ\t-2692\n▁ବ୍ଲକ୍\t-2693\n▁ଭଗବାନ\t-2694\n▁ମସିହା\t-2695\n▁ମୂଳିଆ\t-2696\n▁ରଞ୍ଜନ\t-2697\n▁ସାଙ୍ଗ\t-2698\n▁ଅବକାରୀ\t-2699\n▁ଆନ୍ଧ୍ର\t-2700\n▁କମିଶନର\t-2701\n▁ଚିତ୍ରା\t-2702\n▁ଚେଷ୍ଟା\t-2703\n▁ଚୌଧୁରୀ\t-2704\n▁ପ୍ରଚାର\t-2705\n▁ପ୍ରତାପ\t-2706\n▁ଯାଜପୁର\t-2707\n▁ସଂଗ୍ରହ\t-2708\n▁ଅପେକ୍ଷା\t-2709\n▁ଆଞ୍ଚଳିକ\t-2710\n▁ଆମେରିକା\t-2711\n▁ଆସିଥିବା\t-2712\n▁କର୍ମକର୍\t-2713\n▁ଘଟିଥିବା\t-2714\n▁ଜଗତସିଂହ\t-2715\n▁ଟିକାକରଣ\t-2716\n▁ଦେଖାଦେଇ\t-2717\n▁ବିକ୍ଷୋଭ\t-2718\n▁ବିଭାଗୀୟ\t-2719\n▁ଯାଉଥିବା\t-2720\n▁ରହିଥିଲେ\t-2721\n▁ପର୍ଯ୍ୟାୟ\t-2722\n▁ବ୍ରାଉନସୁ\t-2723\n▁ଶୁଭାରମ୍ଭ\t-2724\n▁ଆତଙ୍କବାଦୀ\t-2725\n▁କେନ୍ଦ୍ରୀୟ\t-2726\n▁ଗ୍ରାମବାସୀ\t-2727\n▁ଢେଙ୍କାନାଳ\t-2728\n▁ତାପମାତ୍ରା\t-2729\n▁ପ୍ରତିନିଧି\t-2730\n▁ସେମାନଙ୍କର\t-2731\n▁ସ୍ୱତନ୍ତ୍ର\t-2732\n▁ଅନ୍ୟପକ୍ଷରେ\t-2733\n▁ନିର୍ବାଚନରେ\t-2734\n▁ମାଲକାନଗିରି\t-2735\n▁ପ୍ରାର୍ଥୀଙ୍କ\t-2736\nଏକ\t-2737\nକୃ\t-2738\nଚର\t-2739\nସଲ\t-2740\nୀମ\t-2741\nୋଟ\t-2742\nକେଇ\t-2743\nଘାତ\t-2744\nଚାପ\t-2745\nଟକୁ\t-2746\nଦୀପ\t-2747\nପାଖ\t-2748\nପିଓ\t-2749\nବାଜ\t-2750\nବିକ\t-2751\nବିତ\t-2752\nମାସ\t-2753\nଶେଷ\t-2754\nୋକି\t-2755\n▁ଅଦ\t-2756\n▁ଅର\t-2757\n▁ଏପ\t-2758\n▁ଖଟ\t-2759\n▁ଚମ\t-2760\n▁ଧନ\t-2761\n▁ରଖ\t-2762\n▁ରଥ\t-2763\n▁ଶେ\t-2764\n▁ସବ\t-2765\n▁୨୯\t-2766\nଏମସି\t-2767\nଗାରେ\t-2768\nଙ୍ଗୁ\t-2769\nଡ଼ିଓ\t-2770\nଥିବେ\t-2771\nଦେଲେ\t-2772\nପ୍ତି\t-2773\nବର୍ଗ\t-2774\nବାଚନ\t-2775\nବାହି\t-2776\nଲ୍ରେ\t-2777\nଷ୍ଟର\t-2778\nଷ୍ଠା\t-2779\nହଜାର\t-2780\nାନିକ\t-2781\n୍ୟାର\t-2782\n୍ୟାସ\t-2783\n▁ଋତୁ\t-2784\n▁ଋଷି\t-2785\n▁କ୍ୱ\t-2786\n▁ତାଳ\t-2787\n▁ତ୍ର\t-2788\n▁ଦାଶ\t-2789\n▁ପରା\t-2790\n▁ପେଟ\t-2791\n▁ବଦଳ\t-2792\n▁ବୁଲ\t-2793\n▁ବୟସ\t-2794\n▁ମନେ\t-2795\n▁ମାଡ\t-2796\n▁ଲଗା\t-2797\n▁ଲିଖ\t-2798\nଉଥିବା\t-2799\nକ୍ସରେ\t-2800\nଦିଷ୍ଟ\t-2801\nପୂର୍ବ\t-2802\nପ୍ରିୟ\t-2803\nରକେଲା\t-2804\nସ୍ତଫା\t-2805\n▁ଅଫିସ\t-2806\n▁କୋଲକ\t-2807\n▁ଖୁବ୍\t-2808\n▁ଘଣ୍ଟ\t-2809\n▁ଜନତା\t-2810\n▁ଜେନେ\t-2811\n▁ଠିକ୍\t-2812\n▁ଦେବୀ\t-2813\n▁ନଷ୍ଟ\t-2814\n▁ନୋଟି\t-2815\n▁ପଚରା\t-2816\n▁ପଛରେ\t-2817\n▁ପଦବୀ\t-2818\n▁ପୂଜା\t-2819\n▁ପୂରଣ\t-2820\n▁ପ୍ରଭ\t-2821\n▁ଫ୍ୟା\t-2822\n▁ମାଗି\t-2823\n▁ମିଟର\t-2824\n▁ମୁହଁ\t-2825\n▁ଲଗାଇ\t-2826\n▁ଲ୍ୟା\t-2827\n▁ଶୁଭେ\t-2828\n▁ସଚେତ\t-2829\n▁ସମାନ\t-2830\n▁ସାରି\t-2831\nଦ୍ୱାରା\t-2832\nୁଛନ୍ତି\t-2833\n▁ଅଗଷ୍ଟ\t-2834\n▁ଆଲିୟା\t-2835\n▁ଉତ୍ସା\t-2836\n▁ଏସ୍ପି\t-2837\n▁ଏହାସହ\t-2838\n▁କଟକଣା\t-2839\n▁କନ୍ୟା\t-2840\n▁କୋଇଲା\t-2841\n▁ଜ୍ଞାନ\t-2842\n▁ତରଫରୁ\t-2843\n▁ଦିନକୁ\t-2844\n▁ନାଁରେ\t-2845\n▁ପତ୍ନୀ\t-2846\n▁ଫେରାର\t-2847\n▁ମୁହାଁ\t-2848\n▁ଲେଖାଏ\t-2849\n▁ଶଙ୍କର\t-2850\n▁ସଂଯୋଜ\t-2851\n▁ଅନୁଗୁଳ\t-2852\n▁ଅବସ୍ଥା\t-2853\n▁ଆଇସିସି\t-2854\n▁ଆୟୋଜିତ\t-2855\n▁ଇସ୍ତଫା\t-2856\n▁ଉପଲବ୍ଧ\t-2857\n▁କରାଯାଏ\t-2858\n▁ଡିଗ୍ରୀ\t-2859\n▁ନେବାକୁ\t-2860\n▁ନୟାଗଡ଼\t-2861\n▁ପାର୍ଟି\t-2862\n▁ପୁଲିସ୍\t-2863\n▁ଫିଲ୍ମର\t-2864\n▁ବ୍ୟାପକ\t-2865\n▁ଲଘୁଚାପ\t-2866\n▁ସକ୍ରିୟ\t-2867\n▁ସିବିଆଇ\t-2868\n▁ସ୍ଥଳରେ\t-2869\n▁ସ୍ୱାଇଁ\t-2870\nନିର୍ବାଚନ\t-2871\nପ୍ଟେମ୍ବର\t-2872\n▁ଅଗ୍ନିକା\t-2873\n▁ଅଧିକାଂଶ\t-2874\n▁ଅଧିନାୟକ\t-2875\n▁ଅଷ୍ଟ୍ରେ\t-2876\n▁ଦିଆଯିବା\t-2877\n▁ପରିସରରେ\t-2878\n▁ପାଇଥିଲା\t-2879\n▁ବିଜ୍ଞାନ\t-2880\n▁ଖୋର୍ଦ୍ଧା\t-2881\n▁ଚାଲିଥିବା\t-2882\n▁ଦୁଷ୍କର୍ମ\t-2883\n▁ଦୃଷ୍ଟିରେ\t-2884\n▁ମିଳିଥିବା\t-2885\n▁ରଖିଛନ୍ତି\t-2886\n▁ରାସ୍ତାରେ\t-2887\n▁ସେତେବେଳେ\t-2888\n▁ହାଇକୋର୍ଟ\t-2889\n▁ଜିତିଛନ୍ତି\t-2890\n▁ନବରଙ୍ଗପୁର\t-2891\n▁ପ୍ରସ୍ତୁତି\t-2892\n▁ସମସ୍ତଙ୍କୁ\t-2893\n▁କର୍ତ୍ତୃପକ୍ଷ\t-2894\nଉଜ\t-2895\nନୋ\t-2896\nୁଷ\t-2897\n୧୦\t-2898\n▁p\t-2899\nଂଚି\t-2900\nଥଲେ\t-2901\nଦ୍ଦ\t-2902\nନେଇ\t-2903\nପାଟ\t-2904\nବେଦ\t-2905\nମଣି\t-2906\nଯାନ\t-2907\nରେଇ\t-2908\nାମି\t-2909\n୍ୟୋ\t-2910\n▁ଅମ\t-2911\n▁ଆଲ\t-2912\n▁ଘେ\t-2913\n▁ଛି\t-2914\n▁ଧୋ\t-2915\n▁ନର\t-2916\n▁ବସ\t-2917\n▁ରହ\t-2918\n▁୩୩\t-2919\n▁୬୫\t-2920\nଂଚାୟ\t-2921\nଘାଟନ\t-2922\nଙ୍ଗର\t-2923\nଚିତ୍\t-2924\nଜାତି\t-2925\nଟାରୁ\t-2926\nଟାରେ\t-2927\nଣ୍ଟନ\t-2928\nବାଲି\t-2929\nଭୂମି\t-2930\nମିଟର\t-2931\nଶ୍ବର\t-2932\nସ୍ପର\t-2933\nୀକରଣ\t-2934\nୟାରୀ\t-2935\n▁ଅଟେ\t-2936\n▁କମା\t-2937\n▁ଜେଲ\t-2938\n▁ତେଲ\t-2939\n▁ଧରଣ\t-2940\n▁ନଗର\t-2941\n▁ପଠା\t-2942\n▁ପୋଲ\t-2943\n▁ବଳଦ\t-2944\n▁ବିକ\t-2945\n▁ବୁଝ\t-2946\n▁ବୋଝ\t-2947\n▁ଭଜନ\t-2948\n▁ଭିଟ\t-2949\n▁ସରି\t-2950\n▁ସୁଖ\t-2951\nକାନ୍ତ\t-2952\nଗ୍ରହଣ\t-2953\nଚ୍ଛେଦ\t-2954\nଣ୍ଣିଂ\t-2955\nତ୍କାର\t-2956\nତ୍ରରୁ\t-2957\nପ୍ରଭୁ\t-2958\nବିଷ୍ୟ\t-2959\nବୃତ୍ତ\t-2960\nରୁଦ୍ଧ\t-2961\n▁ଅନୁର\t-2962\n▁ଅସ୍ତ\t-2963\n▁ଆଣ୍ଡ\t-2964\n▁ଏସଡି\t-2965\n▁କରାଇ\t-2966\n▁ଧର୍ମ\t-2967\n▁ପରିଣ\t-2968\n▁ପ୍ଲା\t-2969\n▁ଫେବୃ\t-2970\n▁ବକ୍ତ\t-2971\n▁ମଞ୍ଚ\t-2972\n▁ମାଝୀ\t-2973\n▁ମୋଟ୍\t-2974\n▁ଯବାନ\t-2975\n▁ଯୋଗେ\t-2976\n▁ସହରର\t-2977\n▁ସିଲି\t-2978\n▁ସେଠୀ\t-2979\n▁ସେବନ\t-2980\n▁ହୃଦୟ\t-2981\nଚନ୍ଦ୍ର\t-2982\nଞ୍ଜନଗର\t-2983\nବଲାଙ୍ଗ\t-2984\nଭେମ୍ବର\t-2985\nାଷ୍ଟ୍ର\t-2986\n▁କୁକୁର\t-2987\n▁ନର୍ଲା\t-2988\n▁ନାମରେ\t-2989\n▁ପଂଚାୟ\t-2990\n▁ପରିଚୟ\t-2991\n▁ବୋର୍ଡ\t-2992\n▁ଯାଞ୍ଚ\t-2993\n▁ଯୁଦ୍ଧ\t-2994\n▁ରକ୍ଷା\t-2995\n▁ସମ୍ଭବ\t-2996\nବାନୁମାନ\t-2997\nଯାଉଥିବା\t-2998\nରାଷ୍ଟ୍ର\t-2999\n▁ଅନୁମାନ\t-3000\n▁ଇଂଲଣ୍ଡ\t-3001\n▁କଳାକାର\t-3002\n▁କ୍ରିୟା\t-3003\n▁ଗାନ୍ଧୀ\t-3004\n▁ଜଣଙ୍କର\t-3005\n▁ଦୁର୍ବଳ\t-3006\n▁ପରସ୍ପର\t-3007\n▁ଭବିଷ୍ୟ\t-3008\n▁ଭୂମିକା\t-3009\n▁ରହିଥାଏ\t-3010\n▁ଶିକ୍ଷୟ\t-3011\n▁ସମାଧାନ\t-3012\nନାହାନ୍ତି\t-3013\n▁ଆସିଥିଲେ\t-3014\n▁କୋର୍ଟରେ\t-3015\n▁ଖେଳାଯିବ\t-3016\n▁ଘଟିଥିଲା\t-3017\n▁ତାଲିବାନ\t-3018\n▁ଦ୍ବିତୀୟ\t-3019\n▁ପେମେଣ୍ଟ\t-3020\n▁ପୋଲିସକୁ\t-3021\n▁ସମ୍ବନ୍ଧ\t-3022\n▁ସୁପ୍ରିମ\t-3023\n▁ସୁଶାନ୍ତ\t-3024\n▁ହୋଇପାରି\t-3025\nଭୁବନେଶ୍ବର\t-3026\n▁ନାବାଳିକା\t-3027\n▁ମହାପ୍ରଭୁ\t-3028\n▁ମୟୂରଭଞ୍ଜ\t-3029\n▁ଯୁବକଙ୍କୁ\t-3030\n▁ସଂସ୍କୃତି\t-3031\n▁ସମ୍ପତ୍ତି\t-3032\n▁ସେଥିପାଇଁ\t-3033\n▁କେନ୍ଦ୍ରରେ\t-3034\n▁ପରବର୍ତ୍ତୀ\t-3035\n▁ପ୍ରଶାସନିକ\t-3036\n▁ବିଦ୍ୟାଳୟର\t-3037\n▁ସରକାରଙ୍କୁ\t-3038\n▁ଆସନ୍ତାକାଲି\t-3039\n▁ଏପର୍ଯ୍ୟନ୍ତ\t-3040\n▁ପାଣିଗ୍ରାହୀ\t-3041\n▁ସେପ୍ଟେମ୍ବର\t-3042\n▁ପୂର୍ବାନୁମାନ\t-3043\n--\t-3044\nou\t-3045\nଂଜ\t-3046\nଉଳ\t-3047\nଖର\t-3048\nଷଧ\t-3049\nୟୀ\t-3050\n୨୩\t-3051\n▁w\t-3052\nଏନ୍\t-3053\nକେଶ\t-3054\nଟାଇ\t-3055\nଟିଆ\t-3056\nଣ୍ୟ\t-3057\nଦାୟ\t-3058\nଦିଆ\t-3059\nନାଲ\t-3060\nଭୂତ\t-3061\nମାଇ\t-3062\nମ୍ସ\t-3063\nରାୟ\t-3064\nର୍ଷ\t-3065\nଲେଇ\t-3066\nଲ୍ଲ\t-3067\nଶୀଳ\t-3068\nସନର\t-3069\nସିଏ\t-3070\nସିତ\t-3071\nସିନ\t-3072\nୁରା\t-3073\n୍ୱା\t-3074\n▁ଉଠ\t-3075\n▁ଋଷ\t-3076\n▁ଚୁ\t-3077\n▁ଡ଼\t-3078\n▁୩୮\t-3079\n▁୪୪\t-3080\nଆଁଳା\t-3081\nଆଇସି\t-3082\nଖାପା\t-3083\nଗାନି\t-3084\nଜୀବୀ\t-3085\nଞ୍ଚଳ\t-3086\nଣ୍ଣା\t-3087\nତ୍ରୀ\t-3088\nଦାତା\t-3089\nଧାନୀ\t-3090\nନ୍ଦୀ\t-3091\nପାଠୀ\t-3092\nପାଦନ\t-3093\nପିଟା\t-3094\nଫେସର\t-3095\nବସାଇ\t-3096\nଲ୍ଲା\t-3097\nଳଙ୍କ\t-3098\nହାରି\t-3099\n▁ଅଲି\t-3100\n▁ଓଜନ\t-3101\n▁ଔଷଧ\t-3102\n▁କବି\t-3103\n▁କଲା\t-3104\n▁କାଠ\t-3105\n▁କ୍ଲ\t-3106\n▁ଚାଇ\t-3107\n▁ଜବା\t-3108\n▁ଜମା\t-3109\n▁ଜରୁ\t-3110\n▁ଜାପ\t-3111\n▁ଜୁଲ\t-3112\n▁ଝୁଲ\t-3113\n▁ଥାଏ\t-3114\n▁ନୀତ\t-3115\n▁ନୀଳ\t-3116\n▁ନୃତ\t-3117\n▁ପଥର\t-3118\n▁ପଦ୍\t-3119\n▁ପହଁ\t-3120\n▁ପୋଷ\t-3121\n▁ବାବ\t-3122\n▁ବୋଲ\t-3123\n▁ମଶା\t-3124\n▁ମାଆ\t-3125\n▁ହକି\t-3126\nକମାନେ\t-3127\nଜାତୀୟ\t-3128\nଡ଼ିକୁ\t-3129\nଶଙ୍କର\t-3130\nସେନାର\t-3131\nାର୍ଜୀ\t-3132\n▁ଅଧୀନ\t-3133\n▁ଆକଳନ\t-3134\n▁କଣ୍ଠ\t-3135\n▁କପୁର\t-3136\n▁ଚୋରା\t-3137\n▁ଠକେଇ\t-3138\n▁ଥ୍ରୋ\t-3139\n▁ଦୁନି\t-3140\n▁ପୀଡ଼\t-3141\n▁ବଜ୍ର\t-3142\n▁ବଦଳି\t-3143\n▁ବୁଲି\t-3144\n▁ବେଆଇ\t-3145\n▁ମଣ୍ଡ\t-3146\n▁ମିରା\t-3147\n▁ମୋତେ\t-3148\n▁ମୋଦୀ\t-3149\n▁ରୂପେ\t-3150\n▁ସେମି\t-3151\n▁ସ୍ତର\t-3152\nକେନ୍ୟୁ\t-3153\nବ୍ୟୁରୋ\t-3154\nାରିଙ୍ଗ\t-3155\nୃଙ୍ଖଳା\t-3156\nେନ୍ଦ୍ର\t-3157\n▁ଆସିଲେ\t-3158\n▁ଏପ୍ରି\t-3159\n▁ଏମାନେ\t-3160\n▁କବିତା\t-3161\n▁କାଳରେ\t-3162\n▁କୋଭିଡ\t-3163\n▁ଚାହିଁ\t-3164\n▁ଧ୍ୟାନ\t-3165\n▁ନଥିବା\t-3166\n▁ନେଇଛି\t-3167\n▁ପଡ଼ିବ\t-3168\n▁ବାଧ୍ୟ\t-3169\n▁ବାରିକ\t-3170\n▁ବେଳକୁ\t-3171\n▁ବ୍ଲକର\t-3172\n▁ବୟସରେ\t-3173\n▁ମତଦାନ\t-3174\n▁ମାସରେ\t-3175\n▁ମିଳିତ\t-3176\n▁ମୋବାଇ\t-3177\n▁ଯାହାର\t-3178\n▁ଯୋଗ୍ୟ\t-3179\n▁ରିଲିଜ\t-3180\n▁ରେଡିଓ\t-3181\n▁ଲକ୍ଷଣ\t-3182\n▁ସଚେତନ\t-3183\n▁ସମିତି\t-3184\n▁ସ୍ଥିର\t-3185\n▁ଅନୁମତି\t-3186\n▁ଅପରେସନ\t-3187\n▁ଅଭିଯାନ\t-3188\n▁କାହାଣୀ\t-3189\n▁ଚତୁର୍ଥ\t-3190\n▁ଜିଲାରେ\t-3191\n▁ଜ୍ବାଇଁ\t-3192\n▁ଦେଖିବା\t-3193\n▁ନିଆଁକୁ\t-3194\n▁ପରିବର୍\t-3195\n▁ପୁରୁଣା\t-3196\n▁ମଲ୍ଲିକ\t-3197\n▁ଯିବାକୁ\t-3198\n▁ରାଉଣ୍ଡ\t-3199\n▁ରେଳବାଇ\t-3200\n▁ଲଗାଇବା\t-3201\n▁ଲେଖାଏଁ\t-3202\n▁ଶାହବାଜ\t-3203\n▁ସୋନପୁର\t-3204\n▁ସ୍ତରରେ\t-3205\n▁ସ୍ମୃତି\t-3206\n▁ସ୍ୱାଗତ\t-3207\n▁ହଜାରରୁ\t-3208\n▁ହେବାରେ\t-3209\n▁ୟୁକ୍ରେ\t-3210\n▁ଖାଇବାକୁ\t-3211\n▁ଜାନୁଆରୀ\t-3212\n▁ନିକଟସ୍ଥ\t-3213\n▁ନୂଆପଡ଼ା\t-3214\n▁ନେଇଥିବା\t-3215\n▁ପ୍ରଫେସର\t-3216\n▁ପ୍ରସନ୍ନ\t-3217\n▁ମନ୍ତବ୍ୟ\t-3218\n▁ଲକ୍ଷ୍ମୀ\t-3219\n▁ସାକ୍ଷାତ\t-3220\nମ୍ପିକ୍ସରେ\t-3221\n▁ଦିଲ୍ଲୀରେ\t-3222\n▁ଦେଉଛନ୍ତି\t-3223\n▁ପ୍ରତିଶ୍ର\t-3224\n▁ବାର୍ତ୍ତା\t-3225\n▁ବିଚାରପତି\t-3226\n▁ରଖାଯାଇଛି\t-3227\n▁ରାଜନୈତିକ\t-3228\n▁ଶୁଭେଚ୍ଛା\t-3229\n▁ସମ୍ମୁଖରେ\t-3230\n▁ସ୍ୱର୍ଣ୍ଣ\t-3231\n▁ଅଗ୍ନିକାଣ୍ଡ\t-3232\n▁ଅଷ୍ଟ୍ରେଲିଆ\t-3233\n▁ଉପନିର୍ବାଚନ\t-3234\n▁ମହାରାଷ୍ଟ୍ର\t-3235\n▁ପ୍ରତିଯୋଗିତା\t-3236\n▁ଅଭିଯୁକ୍ତଙ୍କୁ\t-3237\n▁କର୍ମଚାରୀଙ୍କୁ\t-3238\nRA\t-3239\nal\t-3240\nas\t-3241\nଘନ\t-3242\nଚକ\t-3243\nଡୋ\t-3244\nପଟ\t-3245\nପଡ\t-3246\nଶନ\t-3247\nୋଲ\t-3248\nୌନ\t-3249\n୧୨\t-3250\n▁1\t-3251\nଅଛି\t-3252\nଖାଇ\t-3253\nଖୋଜ\t-3254\nଗୁଆ\t-3255\nଚୁଆ\t-3256\nଜନା\t-3257\nତିଲ\t-3258\nତ୍ବ\t-3259\nନିଂ\t-3260\nପଡ଼\t-3261\nରିବ\t-3262\nସାମ\t-3263\nସୁଣ\t-3264\nାସୀ\t-3265\n୍ରୁ\t-3266\n୭ରେ\t-3267\nୱାଲ\t-3268\n▁ଅଣ\t-3269\n▁ଅହ\t-3270\n▁ଆଖ\t-3271\n▁କୈ\t-3272\n▁ଚୀ\t-3273\n▁ଛକ\t-3274\n▁ଢା\t-3275\n▁ତୀ\t-3276\n▁ଦୋ\t-3277\n▁ପଇ\t-3278\n▁ଫୋ\t-3279\n▁୬୪\t-3280\n▁୬୬\t-3281\nକ୍ତା\t-3282\nଗଞ୍ଜ\t-3283\nଙ୍ଗ୍\t-3284\nଟରରେ\t-3285\nଠାତ୍\t-3286\nଡ଼କୁ\t-3287\nଡ଼ରେ\t-3288\nତାକୁ\t-3289\nତ୍ତର\t-3290\nତ୍ମା\t-3291\nଦେବା\t-3292\nନ୍ତେ\t-3293\nବଦ୍ଧ\t-3294\nଭଦ୍ର\t-3295\nରାନ୍\t-3296\nଲପୁର\t-3297\nସାରି\t-3298\nସେବୀ\t-3299\nାରଣା\t-3300\nୃତିକ\t-3301\n▁ଅଫ୍\t-3302\n▁ଉଦୟ\t-3303\n▁ଏଣୁ\t-3304\n▁କିଏ\t-3305\n▁କେଶ\t-3306\n▁ଗଡ଼\t-3307\n▁ଛୋଟ\t-3308\n▁ଜୋର\t-3309\n▁ତାମ\t-3310\n▁ଦମ୍\t-3311\n▁ଦର୍\t-3312\n▁ପନି\t-3313\n▁ପାଦ\t-3314\n▁ବଜେ\t-3315\n▁ବାୟ\t-3316\n▁ମନୋ\t-3317\n▁ରିସ\t-3318\n▁ଲାଲ\t-3319\n▁ଶତା\t-3320\n▁ଶଶି\t-3321\n▁ସଂଖ\t-3322\n▁ସଂସ\t-3323\n▁ସହର\t-3324\n▁ସହି\t-3325\n▁୨୦୦\t-3326\nଆନ୍ତି\t-3327\nଚିତ୍ର\t-3328\nଣ୍ଡିଂ\t-3329\nବାଡ଼ି\t-3330\nମୁହିଁ\t-3331\nଶୀଘ୍ର\t-3332\nଶ୍ମୀର\t-3333\nସବ୍ୟା\t-3334\nିତ୍ରୀ\t-3335\n▁ଅଭାବ\t-3336\n▁ଅଲଗା\t-3337\n▁ଆଥଲେ\t-3338\n▁ଆନ୍ତ\t-3339\n▁ଆଶ୍ର\t-3340\n▁ଆସିବ\t-3341\n▁ଉଚିତ\t-3342\n▁ଉତ୍ସ\t-3343\n▁ଉପାୟ\t-3344\n▁ଓଡିଆ\t-3345\n▁କଣ୍ଟ\t-3346\n▁କାଟି\t-3347\n▁କିଣି\t-3348\n▁ଖୋଲା\t-3349\n▁ଚଳାଇ\t-3350\n▁ଜ୍ୟୋ\t-3351\n▁ତାଲା\t-3352\n▁ଦାୟର\t-3353\n▁ନିଜେ\t-3354\n▁ନିରା\t-3355\n▁ନୀତି\t-3356\n▁ପଡିବ\t-3357\n▁ପାଠପ\t-3358\n▁ବାସି\t-3359\n▁ବିଧି\t-3360\n▁ଭଉଣୀ\t-3361\n▁ମୁନା\t-3362\n▁ଯୋଗା\t-3363\n▁ରସୁଣ\t-3364\n▁ଲଗାତ\t-3365\n▁ଶାଶୁ\t-3366\n▁ସଭ୍ୟ\t-3367\n▁ସରୋଜ\t-3368\n▁ସାର୍\t-3369\n▁ସୁପର\t-3370\n▁ସ୍ବର\t-3371\nତିଭୂମି\t-3372\nଧିକାରୀ\t-3373\nଧ୍ୟକ୍ଷ\t-3374\nପର୍ଯ୍ୟ\t-3375\nରାଖଣ୍ଡ\t-3376\nଲିକାରେ\t-3377\nସାଧାରଣ\t-3378\n▁ଅକ୍ଷର\t-3379\n▁ଅକ୍ଷୟ\t-3380\n▁ଅର୍ଜନ\t-3381\n▁ଉତ୍କଳ\t-3382\n▁ଏଥିରୁ\t-3383\n▁କରିନା\t-3384\n▁କିଶୋର\t-3385\n▁ଚଢ଼ାଉ\t-3386\n▁ଜରୁରୀ\t-3387\n▁ଜାମିନ\t-3388\n▁ଦର୍ଶନ\t-3389\n▁ଦିଗରେ\t-3390\n▁ନିଖୋଜ\t-3391\n▁ନିଜକୁ\t-3392\n▁ନିବେଶ\t-3393\n▁ନିୟୋଜ\t-3394\n▁ପହଂଚି\t-3395\n▁ପ୍ରଦୀ\t-3396\n▁ପ୍ରାଣ\t-3397\n▁ବାତିଲ\t-3398\n▁ବିଜ୍ଞ\t-3399\n▁ବିଜୟୀ\t-3400\n▁ବୋଝେଇ\t-3401\n▁ମାନ୍ୟ\t-3402\n▁ମୁଣ୍ଡ\t-3403\n▁ରହିବେ\t-3404\n▁ଲିଖିତ\t-3405\n▁ସଙ୍ଗେ\t-3406\n▁ସମଗ୍ର\t-3407\n▁ହଠାତ୍\t-3408\n▁ହିଂସା\t-3409\nଦ୍ରାବାଦ\t-3410\nବାସୀଙ୍କ\t-3411\nହାଁନ୍ତି\t-3412\n▁ଅନୁରୋଧ\t-3413\n▁ଅର୍ଜୁନ\t-3414\n▁ଆଇଆଇସି\t-3415\n▁ଇତିହାସ\t-3416\n▁ଉର୍ଦ୍ଧ\t-3417\n▁ଏକାଧିକ\t-3418\n▁କେମିତି\t-3419\n▁କ୍ଷମତା\t-3420\n▁ଖର୍ଚ୍ଚ\t-3421\n▁ଗୋଷ୍ଠୀ\t-3422\n▁ଗ୍ରୁପ୍\t-3423\n▁ଜାଣିବା\t-3424\n▁ଜେସିବି\t-3425\n▁ତାହାକୁ\t-3426\n▁ତାହେଲେ\t-3427\n▁ତ୍ରୁଟି\t-3428\n▁ବିକ୍ରମ\t-3429\n▁ମୁକ୍ତି\t-3430\n▁ଯାଆଁଳା\t-3431\n▁ଯାହାକି\t-3432\n▁ଯେଉଁଠି\t-3433\n▁ଶିମିଳି\t-3434\n▁ସ୍ଥାନା\t-3435\n▁ସ୍ଥାପନ\t-3436\n▁ହନୁମାନ\t-3437\n▁ଅତ୍ୟନ୍ତ\t-3438\n▁କରିପାରୁ\t-3439\n▁ଗୁଜରାଟର\t-3440\n▁ଗୋବିନ୍ଦ\t-3441\n▁ଚେନ୍ନାଇ\t-3442\n▁ନଭେମ୍ବର\t-3443\n▁ନିଶ୍ଚିତ\t-3444\n▁ପଜିଟିଭ୍\t-3445\n▁ପାଇଥିବା\t-3446\n▁ବାହାରକୁ\t-3447\n▁ବ୍ୟବସାୟ\t-3448\n▁ଭିଟାମିନ\t-3449\n▁ଯୋଜନାରେ\t-3450\n▁ରହୁଥିବା\t-3451\n▁ଶ୍ରଦ୍ଧା\t-3452\n▁ସାମାନ୍ୟ\t-3453\nଗାନିସ୍ତାନ\t-3454\n▁ଆଣିଛନ୍ତି\t-3455\n▁ଆସିଛନ୍ତି\t-3456\n▁ଓଡ଼ିଶାରେ\t-3457\n▁କାର୍ତ୍ତି\t-3458\n▁ତାଲିକାରେ\t-3459\n▁ପଞ୍ଚାୟତର\t-3460\n▁ମ୍ୟାଚ୍ରେ\t-3461\n▁ଯାଇଛନ୍ତି\t-3462\n▁ଯେଉଁମାନେ\t-3463\n▁ହସ୍ପିଟାଲ\t-3464\n▁କରିଥାନ୍ତି\t-3465\n▁କ୍ୟାସବ୍ୟା\t-3466\n▁ପରୀକ୍ଷାରେ\t-3467\n▁ପ୍ରକ୍ରିୟା\t-3468\n▁ବ୍ରହ୍ମପୁର\t-3469\n▁ଅନ୍ୟମାନଙ୍କ\t-3470\n▁ଦିଆଯାଇଥିଲା\t-3471\n▁ଦୁର୍ଘଟଣାରେ\t-3472\n▁କର୍ମକର୍ତ୍ତା\t-3473\n▁ମହାବିଦ୍ୟାଳୟ\t-3474\n▁ସଂକ୍ରମିତଙ୍କ\t-3475\n▁ଡାକ୍ତରଖାନାରେ\t-3476\nCH\t-3477\nct\t-3478\nଇଓ\t-3479\nଏଲ\t-3480\nଖେ\t-3481\nଗଲ\t-3482\nଟଫ\t-3483\nଣେ\t-3484\nଥ୍\t-3485\nଦର\t-3486\nଭ୍\t-3487\nମୂ\t-3488\nସମ\t-3489\nାଦ\t-3490\nୂଅ\t-3491\nୌପ\t-3492\nୱେ\t-3493\n▁N\t-3494\n▁o\t-3495\n▁t\t-3496\n▁॥\t-3497\n▁ଊ\t-3498\nଏହି\t-3499\nକିଆ\t-3500\nଡିଂ\t-3501\nପାଇ\t-3502\nବୁଲ\t-3503\nମୋଦ\t-3504\nରନ୍\t-3505\nାରଜ\t-3506\nାୟା\t-3507\nୀତା\t-3508\n▁ଅଜ\t-3509\n▁ଅଥ\t-3510\n▁ଅଶ\t-3511\n▁କଥ\t-3512\n▁ଘନ\t-3513\n▁ଘା\t-3514\n▁ଛୁ\t-3515\n▁ଥି\t-3516\n▁ଧି\t-3517\n▁ହସ\t-3518\n▁୩୧\t-3519\n▁୪୫\t-3520\n▁୯୦\t-3521\nକେଟ୍\t-3522\nକ୍ସଲ\t-3523\nଙ୍କି\t-3524\nଚୁଆଲ\t-3525\nଟିପି\t-3526\nତାରେ\t-3527\nତ୍ସବ\t-3528\nଦଣ୍ଡ\t-3529\nଧାରା\t-3530\nନାରେ\t-3531\nନିକ୍\t-3532\nପିକା\t-3533\nବନରେ\t-3534\nର୍ମେ\t-3535\nଲେଟ୍\t-3536\nସୁମୀ\t-3537\nସ୍ତର\t-3538\nାରରେ\t-3539\nାର୍ଡ\t-3540\nିତ୍ୟ\t-3541\n▁ଅପର\t-3542\n▁ଅସ୍\t-3543\n▁ଆମର\t-3544\n▁ଉପର\t-3545\n▁ଓହ୍\t-3546\n▁କଇଁ\t-3547\n▁କାଉ\t-3548\n▁କାନ\t-3549\n▁କୂଅ\t-3550\n▁ଖୋଜ\t-3551\n▁ଗରମ\t-3552\n▁ଗାଇ\t-3553\n▁ଗୌର\t-3554\n▁ଚରଣ\t-3555\n▁ପିଇ\t-3556\n▁ଫିଚ\t-3557\n▁ବରା\t-3558\n▁ବାଟ\t-3559\n▁ବାଧ\t-3560\n▁ବୀର\t-3561\n▁ଭାବ\t-3562\n▁ଭୋଗ\t-3563\n▁ମଝି\t-3564\n▁ମଧୁ\t-3565\n▁ମହୁ\t-3566\n▁ଯଥା\t-3567\n▁ଯୌନ\t-3568\n▁ରୁଷ\t-3569\n▁ରେଡ\t-3570\n▁ଶତକ\t-3571\n▁ସନି\t-3572\n▁ସମୁ\t-3573\n▁ହୁଅ\t-3574\nଚ୍ଚନା\t-3575\nଟ୍ରିକ\t-3576\nଣ୍ଟାଇ\t-3577\nନ୍ତାନ\t-3578\nନ୍ଦିର\t-3579\nପଲ୍ଲୀ\t-3580\nପ୍ରତି\t-3581\nବିଧାନ\t-3582\nମିତ୍ର\t-3583\nଲାଇନ୍\t-3584\nାର୍ଗେ\t-3585\nାର୍ଥୀ\t-3586\n▁ଅଙ୍ଗ\t-3587\n▁ଅବଗତ\t-3588\n▁ଆଜିର\t-3589\n▁ଆଫ୍ର\t-3590\n▁ଆବାସ\t-3591\n▁ଆମ୍ଭ\t-3592\n▁ଆଲୋକ\t-3593\n▁ଉଠାଇ\t-3594\n▁ଏକ୍ସ\t-3595\n▁କରୋନ\t-3596\n▁କୃଷକ\t-3597\n▁ଖାରଜ\t-3598\n▁ଗରିବ\t-3599\n▁ଗୋଟେ\t-3600\n▁ଗୌତମ\t-3601\n▁ଛତ୍ର\t-3602\n▁ଜୁନ୍\t-3603\n▁ଠାକୁ\t-3604\n▁ଡିସି\t-3605\n▁ଦୃଢ଼\t-3606\n▁ଧାରଣ\t-3607\n▁ନୂଆଁ\t-3608\n▁ପରାଜ\t-3609\n▁ପାନ୍\t-3610\n▁ବଜାର\t-3611\n▁ବିଏସ\t-3612\n▁ବିନା\t-3613\n▁ବେଶ୍\t-3614\n▁ବ୍ୟୟ\t-3615\n▁ମଞ୍ଜ\t-3616\n▁ମମତା\t-3617\n▁ମିଶନ\t-3618\n▁ମୟୁର\t-3619\n▁ରେଡ୍\t-3620\n▁ଲଣ୍ଡ\t-3621\n▁ଲାପଟ\t-3622\n▁ଲୋକା\t-3623\n▁ଶରୀର\t-3624\n▁ଶୀର୍\t-3625\n▁ସଂଘର\t-3626\n▁ସମାଜ\t-3627\n▁ହାତୀ\t-3628\n▁ହେତୁ\t-3629\n▁ହ୍ୟା\t-3630\nଖାପାଖି\t-3631\nଗ୍ରସ୍ତ\t-3632\nଙ୍ଗୁରୁ\t-3633\nଟ୍ରିକ୍\t-3634\nନ୍ଦ୍ରା\t-3635\nବର୍ଦ୍ଧ\t-3636\nବୃଦ୍ଧି\t-3637\n▁ଅଫିସର\t-3638\n▁ଆସିକା\t-3639\n▁ଆସୁଛି\t-3640\n▁କ୍ରମେ\t-3641\n▁ତାମିଲ\t-3642\n▁ତାଲିମ\t-3643\n▁ନକ୍ସଲ\t-3644\n▁ନଥିଲା\t-3645\n▁ପଞ୍ଜା\t-3646\n▁ପରିଣତ\t-3647\n▁ପହଞ୍ଚ\t-3648\n▁ପାଞ୍ଚ\t-3649\n▁ପାଲଟି\t-3650\n▁ପୁସ୍ତ\t-3651\n▁ବଦଳରେ\t-3652\n▁ବାସ୍ତ\t-3653\n▁ବୃଦ୍ଧ\t-3654\n▁ବୈଜ୍ଞ\t-3655\n▁ବ୍ଲଡ୍\t-3656\n▁ମହାରଥ\t-3657\n▁ଯେପରି\t-3658\n▁ସଞ୍ଜୟ\t-3659\n▁ସାହିତ\t-3660\n▁ସ୍ୱପ୍\t-3661\nନ୍ଦବାଲି\t-3662\nଷ୍ଟ୍ରୀୟ\t-3663\n▁\t-3664\nା\t-3665\n୍\t-3666\nର\t-3667\nି\t-3668\nକ\t-3669\nେ\t-3670\nନ\t-3671\nବ\t-3672\nତ\t-3673\nୁ\t-3674\nସ\t-3675\nପ\t-3676\nମ\t-3677\nହ\t-3678\nଲ\t-3679\nଦ\t-3680\nୟ\t-3681\n।\t-3682\nଇ\t-3683\nୋ\t-3684\nଜ\t-3685\nଟ\t-3686\nଗ\t-3687\nଥ\t-3688\nୀ\t-3689\nଣ\t-3690\nଏ\t-3691\nଛ\t-3692\nଶ\t-3693\nଆ\t-3694\nଭ\t-3695\nଳ\t-3696\nଡ\t-3697\nଯ\t-3698\nଙ\t-3699\nଅ\t-3700\nଚ\t-3701\nଷ\t-3702\nଧ\t-3703\nଖ\t-3704\nଉ\t-3705\n,\t-3706\nଁ\t-3707\nଂ\t-3708\nଓ\t-3709\nୂ\t-3710\n଼\t-3711\nଫ\t-3712\nଠ\t-3713\nୱ\t-3714\nଘ\t-3715\nୃ\t-3716\n୧\t-3717\n୨\t-3718\nଞ\t-3719\n୦\t-3720\n୩\t-3721\n୫\t-3722\n୪\t-3723\nଢ\t-3724\n'\t-3725\n-\t-3726\n.\t-3727\n୬\t-3728\nଃ\t-3729\n୮\t-3730\n୭\t-3731\nୌ\t-3732\n୯\t-3733\ni\t-3734\n:\t-3735\n)\t-3736\n(\t-3737\nଝ\t-3738\ne\t-3739\nୈ\t-3740\na\t-3741\nt\t-3742\ns\t-3743\no\t-3744\nn\t-3745\nr\t-3746\nl\t-3747\nd\t-3748\n/\t-3749\ng\t-3750\n\"\t-3751\nm\t-3752\nc\t-3753\n;\t-3754\nu\t-3755\n!\t-3756\n?\t-3757\nA\t-3758\nb\t-3759\nh\t-3760\n|\t-3761\nD\t-3762\ny\t-3763\nଋ\t-3764\nC\t-3765\nw\t-3766\nP\t-3767\n2\t-3768\n0\t-3769\nO\t-3770\nR\t-3771\nS\t-3772\np\t-3773\nଈ\t-3774\nk\t-3775\n1\t-3776\nN\t-3777\nz\t-3778\n@\t-3779\nI\t-3780\nj\t-3781\nf\t-3782\nଔ\t-3783\nH\t-3784\nv\t-3785\nF\t-3786\nT\t-3787\n॥\t-3788\n3\t-3789\n9\t-3790\nE\t-3791\nW\t-3792\n6\t-3793\nM\t-3794\n\\\t-3795\n+\t-3796\nB\t-3797\nଊ\t-3798\n%\t-3799\n5\t-3800\n7\t-3801\n°\t-3802\n8\t-3803\nG\t-3804\n୘\t-3805\n•\t-3806\n*\t-3807\nQ\t-3808\nଐ\t-3809\n4\t-3810\nô\t-3811\nU\t-3812\n~\t-3813\n●\t-3814\n&\t-3815\nJ\t-3816\nK\t-3817\nV\t-3818\nZ\t-3819\nx\t-3820\n<\t-3821\nL\t-3822\n©\t-3823\nà\t-3824\n#\t-3825\n[\t-3826\nq\t-3827\n®\t-3828\n·\t-3829\nË\t-3830\n÷\t-3831\nଽ\t-3832\nୖ\t-3833\n‡\t-3834\n④\t-3835\n", 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shhUAEIzFChAKCeCsleCth+CsshUAGIzFChAKCeKWgeCsheCsrxUAIIzFChMKDOCsn+CsvuCssOCtgRUAKIzFChMKDOKWgeCtp+Ctr+CtrhUAMIzFChYKD+CsreCsvuCsl+CtjeCtnxUAOIzFChYKD+KWgeCsk+CsoeCsv+CshhUAQIzFChYKD+KWgeCspuCsvuCtn+CtgBUASIzFChYKD+KWgeCsquCspuCtjeCspxUAUIzFChkKEuKWgeCsl+CtgeCsquCtjeCspBUAWIzFChkKEuKWgeCsquCtjeCssOCsruCtixUAYIzFChwKFeKWgeCspuCsvuCsuOCsmeCtjeCslRUAaIzFChwKFeKWgeCstuCsv+CsleCtjeCst+CtnxUAcIzFCiIKG+KWgeCsquCtjeCssOCstuCsvuCsqOCtjeCspBUAeIzFCjEKKuKWgeCsrOCsv+CstuCtjeCtseCsrOCsv+CspuCtjeCtn+CsvuCss+CtnxUAgIzFChAKCeCtseCsvuCsshUAiIzFChAKCeKWgeCtqeCtqRUAkIzFChMKDOCsrOCtjeCssOCsvhUAmIzFChMKDOKWgeCsleCss+CsvxUAoIzFChMKDOKWgeCsp+Css+CsvhUAqIzFChMKDOKWgeCsq+CtjeCssBUAsIzFChMKDOKWgeCsruCsueCtixUAuIzFChYKD+CsleCtjeCssOCsruCthxUAwIzFChYKD+KWgeCsj+CsruCsv+CspBUAyIzFChYKD+KWgeCsl+CsqOCtjeCspxUA0IzFCh8KGOKWgeCsuOCsmuCth+CspOCsqOCspOCsvhUA2IzFCiUKHuKWgeCsquCssOCsv+CsrOCssOCtjeCspOCtjeCspBUA4IzFChAKCeCsj+CsqOCtjRUA6IzFChAKCeCsleCssOCtgRUA8IzFChAKCeCsluCssOCtgBUA+IzFChMKDOCsleCtjeCst+CtgRUAAI3FChMKDOCsp+CsvuCssOCsvhUACI3FChMKDOKWgeCsj+CsuOCsvxUAEI3FChYKD+CsvuCsqOCsqOCtjeCsphUAGI3FChkKEuKWgeCsleCspeCsvuCsleCtgRUAII3FChkKEuKWgeCsrOCtg+CspOCtjeCspBUAKI3FChkKEuKWgeCsruCsvuCssOCtjeCslxUAMI3FChkKEuKWgeCsuOCsguCsluCtjeCtnxUAOI3FChkKEuKWgeCsuOCsnuCtjeCsnOCtnxUAQI3FCh8KGOKWgeCsrOCtjeCssOCsueCtjeCsruCsvhUASI3FCh8KGOKWgeCssOCti+Csl+CtgOCsmeCtjeCslRUAUI3FCiIKG+KWgeCsquCtjeCssOCspOCsv+CsleCsvuCssBUAWI3FChMKDOCsleCtjeCst+CsvxUAYI3FChMKDOCsvuCssOCtjeCsnBUAaI3FChMKDOKWgeCsrOCti+CsiRUAcI3FChYKD+CsuOCtjeCsquCspOCsvxUAeI3FChYKD+KWgeCsleCti+CssuCslRUAgI3FChYKD+KWgeCsq+Cth+CsrOCtgxUAiI3FChkKEuKWgeCssOCti+CsueCsv+CspBUAkI3FChkKEuKWgeCssuCsruCtjeCsrOCsvhUAmI3FChwKFeKWgeCssuCsvuCsl+Csv+CssuCthxUAoI3FCiIKG+KWgeCsuOCsguCsluCtjeCtn+CsvuCssOCthxUAqI3FCiUKHuKWgeCstuCtjeCssOCtgOCsleCtg+Cst+CtjeCsoxUAsI3FCigKIeKWgeCsp+CssOCtjeCsruCth+CsqOCtjeCspuCtjeCssBUAuI3FChAKCeCsuOCsruCtjRUAwI3FChAKCeKWgeCspOCtgxUAyI3FChAKCeKWgeCsqOCsnBUA0I3FChMKDOCsr+Csv+CsrOCthxUA2I3FChYKD+KWgeCsoeCssuCsvuCssBUA4I3FChkKEuKWgeCspuCsv+CsrOCtjeCtnxUA6I3FChMKDOCsleCtjeCst+CtgBUA8I3FChMKDOKWgeCsheCssuCsvxUA+I3FChYKD+CsheCsqOCtjeCspOCsvxUAAI7FChYKD+CsruCtgOCsmeCtjeCslRUACI7FChYKD+KWgeCsheCsrOCsueCthxUAEI7FChYKD+KWgeCsmuCsouCsvOCsvhUAGI7FChYKD+KWgeCssuCsvuCsh+CslRUAII7FChkKEuCstuCsv+CsleCtjeCst+CsvhUAKI7FChkKEuKWgeCsruCtgeCsueCsvuCsgRUAMI7FChkKEuKWgeCsuOCtjeCsleCti+CssBUAOI7FChwKFeKWgeCsleCtjeCssOCsv+CsleCthxUAQI7FCh8KGOKWgeCsqOCsv+Csr+CtgeCsleCtjeCspBUASI7FCiIKG+KWgeCspuCsv+CssuCtjeCssuCtgOCssOCthxUAUI7FCigKIeKWgeCsnOCsl+CspOCsuOCsv+CsguCsueCsquCtgeCssBUAWI7FCg0KBuCsneCsvhUAYI7FCgsKBOKWgVsVAGiOxQoQCgngrKrgrL7grLIVAHCOxQoQCgngrLfgrL7grLAVAHiOxQoTCgzgrKzgrKjgrY3grKYVAICOxQoTCgzgrLbgrYHgrKPgrL4VAIiOxQoWCg/grKjgrKXgrL/grLLgrL4VAJCOxQoWCg/grKjgrY3grKjgrL7grIcVAJiOxQoWCg/grY3grZ/grL7grKjgrY0VAKCOxQoWCg/iloHgrIXgrKzgrJfgrKQVAKiOxQoWCg/iloHgrLbgrYvgrK3grL4VALCOxQoZChLiloHgrLjgrJXgrL7grLbgrYcVALiOxQocChXgrLDgrL7grLfgrY3grJ/grY3grLAVAMCOxQocChXiloHgrIXgrK3grY3grZ/grL7grLgVAMiOxQocChXiloHgrIngrKTgrY3grLjgrL7grLkVANCOxQocChXiloHgrK3grL7grLDgrKTgrJXgrYEVANiOxQofChjiloHgrLngrYfgrIngrKXgrL/grLLgrYcVAOCOxQoiChviloHgrIbgrLjgrYHgrJvgrKjgrY3grKTgrL8VAOiOxQorCiTiloHgrJXgrLDgrY3grK7grJrgrL7grLDgrYDgrJngrY3grJUVAPCOxQoJCgJpchUA+I7FCgsKBOKWgUcVAACPxQoQCgngrJXgrL/grIIVAAiPxQoQCgngrKXgrLLgrYcVABCPxQoQCgngrKjgrL7grLYVABiPxQoQCgngrLfgrL/grKQVACCPxQoQCgniloHgrangrawVACiPxQoTCgzgrJvgrKjgrY3grKQVADCPxQoTCgzgrKzgrL7grJrgrKgVADiPxQoTCgziloHgrLLgrYHgrLkVAECPxQoWCg/iloHgrLLgrJfgrL7grKQVAEiPxQofChjiloHgrKzgrL/grJXgrY3grLfgrYvgrK0VAFCPxQoiChviloHgrKrgrLngrJ7grY3grJrgrL/grKzgrL4VAFiPxQooCiHiloHgrLLgrYvgrJXgrK7grL7grKjgrJngrY3grJXgrYEVAGCPxQoQCgngrJfgrYHgrIYVAGiPxQoQCgngrLjgrY3grK4VAHCPxQoTCgzgrJbgrL7grKjgrL4VAHiPxQoTCgziloHgrJzgrYfgrLIVAICPxQoWCg/iloHgrJ/grY3grZ/grL4VAIiPxQoWCg/iloHgrLbgrL7grLjgrJUVAJCPxQoWCg/iloHgrLjgrKrgrY3grKQVAJiPxQoWCg/iloHgrajgrabgrafgra4VAKCPxQoZChLiloHgrIbgrLjgrL/grKzgrYcVAKiPxQoZChLiloHgrKzgrYvgrLDgrY3grKEVALCPxQooCiHiloHgrLngrYvgrIfgrK/grL7grIfgrKXgrL/grLLgrL4VALiPxQoNCgbgrajgrakVAMCPxQoQCgngrYLgrLPgrJUVAMiPxQoTCgzgrKrgrKHgrLzgrL8VANCPxQoTCgzgrKzgrLDgrY3grKQVANiPxQoTCgzgrLngrL7grKTgrL8VAOCPxQoWCg/grLbgrJXgrY3grKTgrL8VAOiPxQoWCg/iloHgrIXgrJ/grJXgrL8VAPCPxQoWCg/iloHgrKrgrYHgrKjgrIMVAPiPxQoWCg/iloHgrK/grJzgrY3grJ4VAACQxQoZChLiloHgrJzgrL7grKrgrL7grKgVAAiQxQocChXiloHgrKzgrL/grLjgrY3grKvgrYsVABCQxQooCiHiloHgrLngrL/grKTgrL7grKfgrL/grJXgrL7grLDgrYAVABiQxQoQCgniloHgrI/grLIVACCQxQoTCgzgrKPgrY3grJ/grY0VACiQxQoTCgzgrL7grKzgrLPgrYAVADCQxQoTCgziloHgrIngrKjgrY0VADiQxQoTCgziloHgrJXgrKrgrLAVAECQxQoTCgziloHgrKrgrL/grLIVAEiQxQoWCg/iloHgrKbgrLPgrYDgrZ8VAFCQxQoWCg/iloHgrKzgrL7grLjgrYEVAFiQxQoWCg/iloHgrK3grJngrY3grJcVAGCQxQoWCg/iloHgrK7grYvgrJ/grY0VAGiQxQoZChLiloHgrLDgrL7grKTgrY3grLAVAHCQxQocChXgrL7grKTgrY3grLDgrL7grLDgrYcVAHiQxQocChXiloHgrIXgrKjgrY3grKfgrL7grLAVAICQxQofChjiloHgrIXgrLjgrKjgrY3grKTgrYvgrLcVAIiQxQofChjiloHgrJXgrL7grLbgrY3grK7grYDgrLAVAJCQxQoiChviloHgrIngrKrgrLjgrY3grKXgrL7grKrgrKgVAJiQxQoQCgngrLDgrJXgrYcVAKCQxQoTCgziloHgrI/grJXgrLAVAKiQxQoTCgziloHgrKrgrYfgrJ8VALCQxQoTCgziloHgrKzgrLngrKgVALiQxQoTCgziloHgrLLgrJjgrYEVAMCQxQoWCg/grLLgrJXgrY3grLfgrY0VAMiQxQoWCg/iloHgrIXgrK7grYPgrKQVANCQxQoWCg/iloHgrJXgrKDgrL/grKgVANiQxQoWCg/iloHgrJfgrYzgrLDgrKwVAOCQxQoWCg/iloHgrKzgrYfgrIbgrIcVAOiQxQoZChLiloHgrKXgrKPgrY3grKHgrL4VAPCQxQoZChLiloHgrLbgrYDgrLDgrY3grLcVAPiQxQocChXiloHgrIngrKrgrLjgrY3grKXgrL8VAACRxQocChXiloHgrKrgrJzgrL/grJ/grL/grK0VAAiRxQocChXiloHgrK7grY3grZ/grL7grKjgrYcVABCRxQocChXiloHgrbHgrYfgrKzgrLjgrL7grIcVABiRxQofChjiloHgrKrgrJzgrL/grJ/grL/grK3grY0VACCRxQoJCgJuZBUAKJHFCg0KBuCsj+CsqxUAMJHFCg0KBuCsuOCslRUAOJHFCgoKA2lzaBUAQJHFChAKCeCsr+CtgeCslxUASJHFChAKCeCsvuCsrOCtjRUAUJHFChMKDOCsj+Cso+CtjeCsnxUAWJHFChMKDOCsl+CsoeCsvOCsvhUAYJHFChMKDOCsreCtguCspOCsvxUAaJHFChMKDOKWgeCsn+CtgeCshxUAcJHFChMKDOKWgeCssOCti+CstxUAeJHFChYKD+KWgeCsqOCth+CssuCthxUAgJHFChYKD+KWgeCsrOCsv+CsqOCsvxUAiJHFChkKEuKWgeCsleCtgeCsleCtgeCssBUAkJHFChkKEuKWgeCspuCth+CsieCsm+CsvxUAmJHFChkKEuKWgeCssOCti+Csl+CssOCthxUAoJHFCiIKG+KWgeCsqOCsvuCsrOCsvuCss+Csv+CsleCsvhUAqJHFCiIKG+KWgeCsruCtn+CtguCssOCsreCsnuCtjeCsnBUAsJHFCiUKHuKWgeCsleCssOCsv+CsrOCsvuCsquCsvuCsh+CsgRUAuJHFCiUKHuKWgeCssuCti+CsleCsruCsvuCsqOCsmeCtjeCslRUAwJHFCiUKHuKWgeCsuOCtjeCsrOCsvuCsp+CtgOCsqOCspOCsvhUAyJHFCg0KBuCsmuCtgBUA0JHFChMKDOCtjeCssOCsv+CslRUA2JHFChMKDOKWgeCshuCsquCthxUA4JHFChMKDOKWgeCspuCtn+CsvhUA6JHFChMKDOKWgeCsqOCtgOCssxUA8JHFChMKDOKWgeCsrOCssOCsvhUA+JHFChkKEuCsnuCtjeCsmuCss+Csv+CslRUAAJLFChkKEuKWgeCsp+CssOCtjeCsruCssBUACJLFChkKEuKWgeCsrOCssOCsl+CsoeCsvBUAEJLFChwKFeKWgeCsl+CsruCtjeCsreCtgOCssBUAGJLFCiUKHuKWgeCsquCtjeCssOCsvuCsleCtg+CspOCsv+CslRUAIJLFCgkKAnVyFQAoksUKDQoG4K2n4K2sFQAwksUKEAoJ4Ky+4Kys4K2AFQA4ksUKEAoJ4paB4Kyd4KywFQBAksUKEwoM4KyV4K2N4Ky34KywFQBIksUKEwoM4Kyw4Ky+4Kyw4K2HFQBQksUKEwoM4Ky44Ky/4Kys4Ky/FQBYksUKEwoM4Ky+4Kyu4Ky/4KyoFQBgksUKEwoM4paB4KyF4Kyn4Ky+FQBoksUKFgoP4paB4KyV4K2H4Kys4KyyFQBwksUKFgoP4paB4KyX4K2L4Kyf4Ky/FQB4ksUKFgoP4paB4Kya4K2L4Kyw4Ky/FQCAksUKFgoP4paB4Kyu4Ky+4KyC4Ky4FQCIksUKHwoY4paB4Kyq4K2N4Kyw4Kys4Kyo4K2N4KynFQCQksUKHwoY4paB4Kyu4Ky54Ky+4Kyk4K2N4Kyu4Ky+FQCYksUKIgob4paB4KyX4Kyj4Kyu4Ky+4Kyn4K2N4K2f4KyuFQCgksUKIgob4paB4Ky44Kyu4K2N4Kyu4Ky+4Kyo4Ky/4KykFQCoksUKEAoJ4paB4Kyq4KygFQCwksUKEAoJ4paB4K2p4K2qFQC4ksUKEwoM4Kyr4Kyz4Kyw4K2HFQDAksUKEwoM4Ky44Kyo4Kyw4K2HFQDIksUKEwoM4paB4KyX4K2L4KyyFQDQksUKFgoP4Ky24K2N4K2x4Ky+4Ky4FQDYksUKFgoP4paB4Kyc4Ky+4KyX4Ky+FQDgksUKGQoS4paB4Kyf4K2N4Kyw4K2H4KyoFQDoksUKGQoS4paB4Kyq4Kyh4Ky/4Kyb4Ky/FQDwksUKHAoV4paB4Kyc4K2A4Kys4Kyo4KyV4K2BFQD4ksUKHwoY4paB4KyJ4Kyk4K2N4Ky44Kys4Kyw4K2HFQAAk8UKIgob4paB4Kyu4Ky+4Kyk4K2N4Kyw4Ky+4Kyw4K2HFQAIk8UKLgon4paB4Kyh4Ky+4KyV4K2N4Kyk4Kyw4KyW4Ky+4Kyo4Ky+4Kyw4K2HFQAQk8UKEAoJ4Kyo4Ky+4KyuFQAYk8UKEwoM4K2f4Ky+4Kyw4K2AFQAgk8UKEwoM4paB4KyF4Kyn4K2BFQAok8UKEwoM4paB4KyG4Kyq4K2NFQAwk8UKEwoM4paB4Kyq4Kyi4Ky/FQA4k8UKFgoP4paB4Ky24K2B4KyW4Ky/FQBAk8UKFgoP4paB4Ky44Ky+4Kyc4Ky/FQBIk8UKGQoS4paB4KyW4Kyj4K2N4Kyh4K2HFQBQk8UKGQoS4paB4Kyn4KyV4K2N4KyV4Ky+FQBYk8UKGQoS4paB4Kyq4Kya4Kyw4Ky+4KyJFQBgk8UKGQoS4paB4Kys4Kyi4Ky84Ky+4KyHFQBok8UKHAoV4paB4KyF4Kyn4Ky/4Kys4K2H4Ky2FQBwk8UKKwok4paB4KyV4Kyl4Ky+4Kys4Ky+4Kyw4K2N4Kyk4K2N4Kyk4Ky+FQB4k8UKEAoJ4K2L4Ky24K2AFQCAk8UKEAoJ4paB4Kyb4KyFFQCIk8UKCwoELi4uLhUAkJPFChYKD+Cso+CtjeCsn+CsvuCshxUAmJPFChYKD+KWgeCshuCsuOCsvuCsrhUAoJPFChYKD+KWgeCsleCsueCsv+CsshUAqJPFChkKEuCsquCtjeCssOCsuOCsvuCsphUAsJPFChkKEuCst+CtjeCsn+CtjeCssOCsvxUAuJPFChwKFeKWgeCsq+Cth+CssOCsv+CsrOCsvhUAwJPFCiIKG+KWgeCsquCssOCtjeCsr+CtjeCtn+Csn+CsqBUAyJPFCiIKG+KWgeCstuCtgeCso+Csv+CsrOCsvuCsleCtgRUA0JPFChAKCeCsj+Csq+CtjRUA2JPFChAKCeCsoeCsv+CsghUA4JPFChAKCeCsuOCsruCsvhUA6JPFChMKDOCsv+CsleCtjeCsuBUA8JPFChYKD+KWgeCsr+CspuCsv+CskxUA+JPFChwKFeKWgeCsqOCsv+CsrOCth+CspuCsqBUAAJTFChwKFeKWgeCsuOCsruCtgeCspuCsvuCtnxUACJTFCh8KGOKWgeCshuCsnuCtjeCsmuCss+Csv+CslRUAEJTFCh8KGOKWgeCsuOCtjeCtseCsvuCsp+CtgOCsqBUAGJTFCg0KBuCtq+CtphUAIJTFCgoKA3RlchUAKJTFChAKCeCsguCstuCtgRUAMJTFChMKDOKWgeCsuOCspuCssBUAOJTFChYKD+CsrOCsv+CstuCth+CstxUAQJTFChYKD+KWgeCsn+Csv+CsreCsvxUASJTFChYKD+KWgeCsrOCsv+CssuCtjRUAUJTFChYKD+KWgeCsreCti+Csn+CtjRUAWJTFChkKEuCsrOCtjeCssOCsueCtjeCsrhUAYJTFChkKEuKWgeCshuCsluCsv+CssOCthxUAaJTFChwKFeKWgeCsheCssOCtjeCspeCsvuCspBUAcJTFChwKFeKWgeCsheCsuOCsruCtjeCsreCsrBUAeJTFCh8KGOKWgeCsj+CsleCsruCsvuCspOCtjeCssBUAgJTFCh8KGOKWgeCsmuCsruCspOCtjeCsleCsvuCssBUAiJTFCigKIeKWgeCspuCsv+CshuCsr+CsvuCsh+CspeCsv+CsrOCsvhUAkJTFChAKCeCsreCti+CslxUAmJTFChMKDOCsuOCth+CsrOCsvhUAoJTFChMKDOKWgeCshuCssuCthxUAqJTFChMKDOKWgeCsp+Cti+CshxUAsJTFChMKDOKWgeCsruCsvuCslxUAuJTFChYKD+KWgeCsheCsqOCtjeCsqBUAwJTFChYKD+KWgeCsmuCsvuCsieCssxUAyJTFChYKD+KWgeCsquCsoeCsvOCthxUA0JTFChYKD+KWgeCsq+Cth+CssOCtgRUA2JTFChYKD+KWgeCsueCth+CssuCtjRUA4JTFCh8KGOCspeCsv+CsrOCsvuCsrOCth+Css+CthxUA6JTFCh8KGOKWgeCsquCtgeCso+Csv+CspeCssOCthxUA8JTFCh8KGOKWgeCstuCspOCsvuCsrOCtjeCspuCtgBUA+JTFCiIKG+KWgeCsruCsqOCtjeCspuCsv+CssOCssOCthxUAAJXFCg0KBuKWgeKAohUACJXFChAKCeCsleCssuCthxUAEJXFChAKCeCsleCth+CsthUAGJXFChAKCeCso+CsvuCssBUAIJXFChMKDOCssuCsvuCssOCthxUAKJXFChMKDOKWgeCsleCth+CspBUAMJXFChYKD+CsruCsv+CspOCtjeCspBUAOJXFChkKEuKWgeCspuCth+CsrOCsvuCssBUAQJXFChkKEuKWgeCsrOCtn+CsuOCtjeCslRUASJXFChwKFeKWgeCsleCtgeCshuCsoeCsvOCthxUAUJXFChwKFeKWgeCsuOCsguCstuCti+Csp+CsqBUAWJXFChMKDOCsquCsvuCso+CsvxUAYJXFChMKDOKWgeCsmuCsvuCsqhUAaJXFChMKDOKWgeCtquCsn+CsvxUAcJXFChYKD+KWgeCsruCsp+CtgeCssBUAeJXFChkKEuCsqOCtjeCspuCtjeCssOCsvhUAgJXFChkKEuKWgeCsheCsqOCtjeCspOCsvxUAiJXFChkKEuKWgeCsqOCsv+CsruCtjeCsqBUAkJXFChkKEuKWgeCsreCtguCsruCsv+CslRUAmJXFChkKEuKWgeCsruCso+CtjeCsoeCssxUAoJXFChwKFeKWgeCsheCsp+CtgOCsqOCssOCthxUAqJXFChwKFeKWgeCsn+CtjeCssOCth+CsqOCtjRUAsJXFChwKFeKWgeCsquCsvuCsueCsvuCsoeCsvBUAuJXFChwKFeKWgeCsrOCtjeCtn+CsvuCsquCsvxUAwJXFCh8KGOKWgeCsrOCsv+CsreCsvuCsl+CtgOCtnxUAyJXFCiIKG+KWgeCspuCtgeCst+CtjeCsleCssOCtjeCsrhUA0JXFCiIKG+KWgeCsquCsv+CssuCsvuCsruCsvuCsqOCthxUA2JXFCiIKG+KWgeCsquCtjeCssOCsvuCsquCtjeCspOCsvxUA4JXFCigKIeKWgeCsquCsvuCso+Csv+Csl+CtjeCssOCsvuCsueCtgBUA6JXFChAKCeCsguCsnOCsqBUA8JXFChAKCeCsrOCssOCsoxUA+JXFChAKCeCss+CsvuCssBUAAJbFChAKCeKWgeCtqeCtrhUACJbFChMKDOCsv+CsleCsvuCsshUAEJbFChMKDOKWgeCstuCti+CstxUAGJbFChMKDOKWgeCsueCsleCsvxUAIJbFChYKD+CssuCsvuCsh+CsqOCtjRUAKJbFChYKD+KWgeCsquCsvuCsoOCsqhUAMJbFCiUKHuKWgeCsleCssOCtjeCspOCtjeCspOCsrOCtjeCtnxUAOJbFCg0KBuCsleCtghUAQJbFChAKCeCsmOCssOCthxUASJbFChAKCeCspOCsv+CsthUAUJbFChAKCeCsrOCssOCsphUAWJbFChAKCeCssOCth+CsthUAYJbFChAKCeCsueCth+CssBUAaJbFChMKDOCso+CtjeCsn+CthxUAcJbFChYKD+KWgeCsleCsoOCti+CssBUAeJbFChYKD+KWgeCsleCsquCtgeCssBUAgJbFChYKD+KWgeCsl+Cso+CtjeCsoRUAiJbFChYKD+KWgeCsm+CspOCtjeCssBUAkJbFChYKD+KWgeCsruCsqOCtjeCsphUAmJbFChwKFeKWgeCsuOCsueCsr+Cti+Csl+CtgBUAoJbFCisKJOKWgeCsheCsqOCtjeCspOCssOCtjeCsnOCsvuCspOCtgOCtnxUAqJbFChAKCeKWgeCsoOCthxUAsJbFChMKDOKWgeCsueCsvuCsiRUAuJbFChYKD+CsnOCtjeCsnuCspOCsvhUAwJbFChYKD+CsnuCtjeCsnOCss+CsvxUAyJbFChYKD+KWgeCsqOCsvuCsh+CsgRUA0JbFChYKD+KWgeCsr+Csv+CsrOCthxUA2JbFChkKEuCsrOCtg+CspuCtjeCsp+CsvxUA4JbFChkKEuKWgeCshuCstuCtjeCssOCtnxUA6JbFChkKEuKWgeCsleCsvuCsruCsqOCsvhUA8JbFChkKEuKWgeCsquCtjeCssOCso+CsvhUA+JbFChkKEuKWgeCsruCspOCspuCsvuCsqBUAAJfFChwKFeKWgeCsnOCsvuCsl+CsvuCssOCthxUACJfFChAKCeKWgeCshuCsqBUAEJfFChAKCeKWgeCsqOCsghUAGJfFChMKDOCsueCth+CspOCtgRUAIJfFChYKD+CsquCsvuCsn+Cso+CsvhUAKJfFChYKD+KWgeCsheCspeCsrOCsvhUAMJfFChYKD+KWgeCsruCsvuCsqOCsuBUAOJfFChwKFeKWgeCsleCtjeCtn+CsvuCssOCsvxUAQJfFCh8KGOKWgeCsqOCsv+CsleCsn+CsuOCtjeCspRUASJfFCh8KGOKWgeCsquCsvuCsh+CspeCsv+CssuCsvhUAUJfFCh8KGOKWgeCsuOCtjeCsrOCtgOCsleCsvuCssBUAWJfFCiIKG+KWgeCsqOCsv+CshuCsr+CsvuCsh+Csm+CsvxUAYJfFChAKCeCss+CsvuCsuBUAaJfFChMKDOCsm+CsoeCsvOCsvhUAcJfFChMKDOCsrOCsvuCsqOCtgRUAeJfFChYKD+CsqOCsv+Cst+CtjeCsoBUAgJfFChYKD+CsvuCsruCtguCss+CslRUAiJfFChYKD+KWgeCsqOCti+Csn+CsvxUAkJfFChYKD+KWgeCsrOCsvuCss+CslRUAmJfFChkKEuKWgeCsuOCssOCtjeCsreCthxUAoJfFChwKFeKWgeCsruCsv+Css+Csv+CsrOCsvhUAqJfFCiIKG+KWgeCsrOCsv+Css+CsruCtjeCsrOCsv+CspBUAsJfFCiUKHuKWgeCsouCth+CsmeCtjeCsleCsvuCsqOCsvuCssxUAuJfFChAKCeCsl+Ctg+CsuRUAwJfFChAKCeCspuCsvuCsrhUAyJfFChAKCeKWgeCsueCshxUA0JfFChMKDOCsreCspuCtjeCssBUA2JfFChMKDOCsruCsvuCsqOCsvhUA4JfFChMKDOCsv+CspOCtjeCsrBUA6JfFChMKDOKWgeCsrOCti+CsnRUA8JfFChYKD+KWgeCstuCsvuCssOCtgBUA+JfFChkKEuKWgeCshuCso+CtjeCsn+CsvxUAAJjFCh8KGOCsrOCsvuCsuOCtgOCsmeCtjeCsleCtgRUACJjFCh8KGOKWgeCsuOCtjeCsruCsvuCssOCtjeCsnxUAEJjFCiIKG+KWgeCsrOCsv+CsquCsleCtjeCst+CssOCthxUAGJjFCiIKG+KWgeCsuOCsruCsvuCssuCti+CsmuCsqOCsvhUAIJjFCigKIeKWgeCsuOCsruCtjeCsruCsv+Css+CsqOCtgOCssOCthxUAKJjFCg0KBuCsouCthxUAMJjFChAKCeCsrOCsv+CslRUAOJjFChAKCeKWgeCsl+CsshUAQJjFChMKDOKWgeCshuCssuCtixUASJjFChMKDOKWgeCsmOCti+CssBUAUJjFChMKDOKWgeCsrOCsvuCsqBUAWJjFChMKDOKWgeCsruCsvuCsshUAYJjFChYKD+CsrOCsuOCtjeCspOCtgRUAaJjFChYKD+KWgeCsheCsquCsoeCthxUAcJjFChYKD+KWgeCsheCsruCtjeCssxUAeJjFChkKEuCsvuCsh+CsrOCsvuCssOCthxUAgJjFChkKEuKWgeCsh+Cso+CtjeCsoeCsvxUAiJjFCiIKG+KWgeCsuOCsquCtjeCspOCsvuCsueCssOCthxUAkJjFCisKJOKWgeCsreCtgeCsrOCsqOCth+CstuCtjeCtseCssOCssOCthxUAmJjFChAKCeCshuCsuOCtgRUAoJjFChAKCeCssOCtjeCsnxUAqJjFChAKCeCth+Csh+CsgRUAsJjFChAKCeKWgeCtqeCtrxUAuJjFChMKDOCsnOCtjeCsnOCsvhUAwJjFChMKDOCsqOCsvuCsueCsgRUAyJjFChMKDOKWgeCsheCsqOCsvxUA0JjFChMKDOKWgeCsheCsuOCsvxUA2JjFChYKD+KWgeCsheCsuOCtjeCspBUA4JjFChYKD+KWgeCshuCsruCtjeCsrBUA6JjFChYKD+KWgeCsruCsqOCti+CsnBUA8JjFChYKD+KWgeCsruCsvuCsuOCssBUA+JjFChkKEuCtgeCsm+CsqOCtjeCspOCsvxUAAJnFChkKEuKWgeCsnOCst+CtjeCsn+CsvxUACJnFChkKEuKWgeCsquCsnuCtjeCsmuCsrhUAEJnFChwKFeKWgeCsreCtguCspOCsvuCso+CtgRUAGJnFChwKFeKWgeCsruCsv+CspeCtjeCtn+CsvhUAIJnFChwKFeKWgeCsuOCspOCtjeCspOCtjeCtsRUAKJnFChwKFeKWgeCsuOCth+CspeCsv+CssOCtgRUAMJnFCh8KGOCsl+CtgeCsoeCsvOCsv+CsleCssOCthxUAOJnFCh8KGOKWgeCstuCsvuCssOCtgOCssOCsv+CslRUAQJnFCiIKG+KWgeCsrOCsv+CsuOCtjeCsq+Cti+CssOCsoxUASJnFCg0KBuCsrOCtiBUAUJnFChAKCeCsp+CsvuCsuBUAWJnFChAKCeCsvuCtn+CsvhUAYJnFChAKCeKWgeCsruCsoBUAaJnFChMKDOKWgeCsieCspuCtnxUAcJnFChMKDOKWgeCsmuCsouCsvhUAeJnFChMKDOKWgeCsquCsvuCsnxUAgJnFChMKDOKWgeCsruCsueCthxUAiJnFChYKD+CstuCtgOCsmOCtjeCssBUAkJnFChYKD+KWgeCsqOCsvuCsueCsvxUAmJnFChwKFeKWgeCsr+Cth+CspOCsv+CsleCsvxUAoJnFChwKFeKWgeCsuOCtjeCtseCsvuCsl+CspBUAqJnFCh8KGOKWgeCsr+Cth+CsleCtjOCso+CsuOCsvxUAsJnFCiIKG+KWgeCsquCssOCsv+CsrOCsvuCssOCssOCthxUAuJnFCiIKG+KWgeCsrOCssuCsvuCsmeCtjeCsl+CtgOCssBUAwJnFCisKJOKWgeCsheCsp+CtjeCtn+CsleCtjeCst+CspOCsvuCssOCthxUAyJnFCi4KJ+KWgeCsleCssOCtjeCsruCsmuCsvuCssOCtgOCsmeCtjeCsleCtgRUA0JnFCg0KBuCsueCsshUA2JnFChAKCeCsoOCsqOCssBUA4JnFChAKCeCsvuCtn+CsqBUA6JnFChAKCeKWgeCspOCtiBUA8JnFChAKCeKWgeCsuOCtgxUA+JnFChMKDOCsq+CssOCtjeCsrhUAAJrFChMKDOCssuCth+Cso+CsvxUACJrFChMKDOCsuOCtguCspuCsqBUAEJrFChMKDOKWgeCsquCti+CsshUAGJrFChYKD+CspuCtjeCssOCti+CsuRUAIJrFChYKD+KWgeCspuCssOCsruCsvhUAKJrFChYKD+KWgeCsrOCtgeCssuCtgRUAMJrFChkKEuKWgeCsrOCssuCsv+CsieCsoRUAOJrFChkKEuKWgeCsuOCsvuCsueCth+CsrBUAQJrFChwKFeKWgeCsheCsqOCtgeCsl+CtgeCssxUASJrFCgkKAnJpFQBQmsUKCwoE4paBVxUAWJrFChAKCeKWgeCtquCtqBUAYJrFChAKCeKWgeCtquCtqhUAaJrFChAKCeKWgeCtr+CtphUAcJrFChMKDOCsk+CtjeCsrOCsvhUAeJrFChMKDOKWgeCsi+Cst+CsvxUAgJrFChMKDOKWgeCsruCspOCtjRUAiJrFChMKDOKWgeCsruCsvuCsoRUAkJrFChMKDOKWgeCsruCsvuCssxUAmJrFChYKD+KWgeCssOCsvuCsnOCsvxUAoJrFChwKFeKWgeCshuCsqOCtjeCspOCssOCtjRUAqJrFCiIKG+KWgeCsrOCsv+CsquCssOCtjeCsr+CtjeCtnxUAsJrFCiUKHuKWgeCssOCsvuCst+CtjeCsn+CtjeCssOCtgOCtnxUAuJrFCisKJOKWgeCsuOCsguCsleCtjeCssOCsruCsv+CspOCsmeCtjeCslRUAwJrFCg0KBuCsueCtghUAyJrFCgsKBOKWgSoVANCaxQoQCgngrLngrL7grZ8VANiaxQoQCgngrL7grK7grYcVAOCaxQoTCgziloHgrLjgrYvgrKgVAOiaxQoWCg/iloHgrJzgrYfgrKjgrYcVAPCaxQoWCg/iloHgrKrgrL7grIfgrLIVAPiaxQoWCg/iloHgrK3grYHgrLLgrY0VAACbxQoZChLiloHgrIbgrK7grY3grKzgrYEVAAibxQoZChLiloHgrKbgrL7grKjgrY3grKQVABCbxQoZChLiloHgrKrgrY3grLDgrKbgrYIVABibxQocChXiloHgrK7grLngrL7grJXgrL7grLYVACCbxQocChXiloHgrLjgrY3grKrgrLDgrY3grLYVACibxQofChjiloHgrJ/grL/grJXgrL7grJXgrLDgrKMVADCbxQofChjiloHgrKTgrL7grLDgrL/grJbgrLDgrYEVADibxQoiChviloHgrJXgrLDgrL/grKrgrL7grLDgrL/grKwVAECbxQooCiHiloHgrKHgrL7grJXgrY3grKTgrLDgrJbgrL7grKjgrL4VAEibxQoQCgniloHgrJXgrJ8VAFCbxQoTCgzgrKHgrL7grIngrKgVAFibxQoTCgzgrLDgrY3grJfgrKQVAGCbxQoTCgziloHgrK7grLDgrYEVAGibxQoTCgziloHgrLLgrL7grLIVAHCbxQoTCgziloHgrafgra/graoVAHibxQoWCg/iloHgrLbgrY3grLDgrK4VAICbxQoZChLiloHgrJrgrKLgrLzgrL7grIkVAIibxQocChXiloHgrJXgrL7grKjgrY3grKbgrL8VAJCbxQocChXiloHgrKrgrLDgrK7grL7grKPgrYEVAJibxQofChjiloHgrJbgrY3grLDgrYDgrLfgrY3grJ8VAKCbxQolCh7iloHgrKjgrL/grLDgrY3grKzgrL7grJrgrL/grKQVAKibxQolCh7iloHgrK7grLngrL/grLPgrL7grK7grL7grKjgrYcVALCbxQoNCgbgrLfgrYEVALibxQoNCgbgrLngrYsVAMCbxQoQCgngrJjgrL7grJ8VAMibxQoQCgngrKbgrL/grI8VANCbxQoTCgzgrKzgrLDgrY3grJcVANibxQoTCgziloHgrKfgrLDgrL4VAOCbxQoTCgziloHgrLbgrKTgrL4VAOibxQoWCg/iloHgrIXgrLDgrYHgrKMVAPCbxQoWCg/iloHgrI/grLjgrKrgrL8VAPibxQoWCg/iloHgrLDgrYfgrJbgrL4VAACcxQoWCg/iloHgrajgrabgrafgra0VAAicxQoZChLiloHgrKrgrLDgrL/grKzgrYcVABCcxQoZChLiloHgrLngrL7grKTgrJXgrYEVABicxQocChXgrKbgrY3grLDgrL7grKzgrL7grKYVACCcxQocChXiloHgrIngrLDgrY3grKbgrY3grKcVACicxQoiChviloHgrKrgrY3grLDgrK3grL7grKzgrLDgrYcVADCcxQooCiHiloHgrK7grKfgrY3grZ/grKrgrY3grLDgrKbgrYfgrLYVADicxQoJCgJhYxUAQJzFChAKCeKWgeCtquCtrhUASJzFChMKDOCsrOCti+Csp+CsqBUAUJzFChMKDOCti+CssOCtjeCsuBUAWJzFChMKDOKWgeCsh+CsoeCsvxUAYJzFChMKDOKWgeCsk+CtjeCsrBUAaJzFChYKD+KWgeCsleCssOCtjeCslRUAcJzFChYKD+KWgeCsquCtjeCssOCspBUAeJzFChYKD+KWgeCssuCsvuCsl+CthxUAgJzFChYKD+KWgeCsuOCsvuCsueCsuBUAiJzFChYKD+KWgeCsueCsvuCssOCsvxUAkJzFChwKFeKWgeCsrOCtiOCst+CtjeCso+CsrBUAmJzFCh8KGOKWgeCsmuCsvuCst+CtgOCsmeCtjeCslRUAoJzFCh8KGOKWgeCsruCtg+CspOCsleCsmeCtjeCslRUAqJzFCiIKG+KWgeCspOCsvuCssuCsv+CsleCsvuCssOCthxUAsJzFCiUKHuKWgeCsquCtjeCssOCspOCtjeCtn+CsleCtjeCstxUAuJzFCiUKHuKWgeCsrOCsv+CspuCtjeCtn+CsvuCss+Ctn+CssBUAwJzFChAKCeCsquCtgOCsoBUAyJzFChAKCeCsruCtjeCtsRUA0JzFChAKCeCsuOCtjeCsnxUA2JzFChAKCeKWgeCspOCsqhUA4JzFChAKCeKWgeCtq+CtqxUA6JzFChMKDOCsmuCsvuCssuCsvxUA8JzFChMKDOCsquCsvuCsoOCtgBUA+JzFChMKDOCsrOCtjeCssOCthxUAAJ3FChMKDOCth+Csh+CsrOCsvhUACJ3FChMKDOKWgeCsreCssOCspBUAEJ3FChMKDOKWgeCsuOCsleCssxUAGJ3FChYKD+KWgeCsuOCspOCtjeCspBUAIJ3FChkKEuKWgeCsquCsvuCsqOCtgOCtnxUAKJ3FChkKEuKWgeCsr+Cti+CsoeCsvOCsvxUAMJ3FChkKEuKWgeCssOCsueCsv+CssuCsvhUAOJ3FChkKEuKWgeCstuCssOCtgOCssOCssBUAQJ3FChwKFeKWgeCsrOCsvuCsueCsvuCsmOCssBUASJ3FCh8KGOKWgeCsheCsrOCssuCsruCtjeCsrOCsqBUAUJ3FCh8KGOKWgeCsreCsv+CsoeCsv+Csk+CssOCthxUAWJ3FCiIKG+KWgeCsquCssOCsv+CsruCsvuCso+CssOCthxUAYJ3FCgoKA2V3cxUAaJ3FChAKCeCsleCsn+CslRUAcJ3FChAKCeCtjeCtseCsvhUAeJ3FChAKCeKWgeCsheCsmhUAgJ3FChAKCeKWgeCsueCsnxUAiJ3FChMKDOCsleCti+Csn+CsvxUAkJ3FChMKDOCsqOCtjeCsp+CsvhUAmJ3FChMKDOKWgeCsruCsv+CsmxUAoJ3FChYKD+CsueCsqOCtjeCspOCtgRUAqJ3FChYKD+KWgeCsoeCsv+CsnOCthxUAsJ3FChYKD+KWgeCsrOCss+CssOCthxUAuJ3FCiUKHuKWgeCstuCtjeCssOCtgOCsr+CtgeCsleCtjeCspBUAwJ3FCjQKLeKWgeCsleCth+CsqOCtjeCspuCtjeCssOCsruCsqOCtjeCspOCtjeCssOCtgBUAyJ3FChAKCeCsq+CtjeCsnxUA0J3FChAKCeCsrOCtjeCsshUA2J3FChAKCeCsv+CsleCssBUA4J3FChAKCeKWgeCsk+CsrRUA6J3FChMKDOCsleCsv+Csm+CsvxUA8J3FChMKDOCsn+CsvuCsleCtgRUA+J3FChMKDOCsquCtjeCsn+CssBUAAJ7FChMKDOKWgeCsreCsnOCsqBUACJ7FChMKDOKWgeCsruCsnOCsvhUAEJ7FChMKDOKWgeCst+CsoeCsvBUAGJ7FChYKD+KWgeCsk+CsleCsv+CsshUAIJ7FChYKD+KWgeCsuOCsguCsuOCsphUAKJ7FChkKEuKWgeCspOCtgeCsruCsleCtgRUAMJ7FChkKEuKWgeCsruCsvuCssOCtjeCslRUAOJ7FChwKFeKWgeCsq+Csv+CssuCtjeCsruCssBUAQJ7FCh8KGOKWgeCsj+CspeCsv+CsuOCsueCsv+CspBUASJ7FCh8KGOKWgeCsleCssOCsv+CsquCsvuCssOCtgRUAUJ7FCh8KGOKWgeCsqOCth+CspOCsvuCsmeCtjeCslRUAWJ7FCiIKG+KWgeCsquCtjeCssOCsruCsvuCso+Csv+CspBUAYJ7FCigKIeKWgeCsmuCsvuCsueCtgeCsgeCsm+CsqOCtjeCspOCsvxUAaJ7FCg0KBuCsp+CsqBUAcJ7FChAKCeCsq+CsvuCshxUAeJ7FChAKCeCssuCsvuCsrhUAgJ7FChAKCeKWgeCspOCssxUAiJ7FChMKDOCsleCss+CtjeCsqhUAkJ7FChMKDOCspuCsvuCtn+CslRUAmJ7FChMKDOCsqOCtjeCsp+CtgRUAoJ7FChMKDOCsuOCsv+CsquCsvxUAqJ7FChMKDOKWgeCsh+CssuCthxUAsJ7FChMKDOKWgeCsieCsquCtgRUAuJ7FChMKDOKWgeCsqOCsvuCspRUAwJ7FChMKDOKWgeCstuCsvuCsuRUAyJ7FChYKD+CsleCssOCtjeCsruCtgBUA0J7FChkKEuKWgeCsquCsvuCss+Csv+CspBUA2J7FChkKEuKWgeCsuOCsv+Csp+CsvuCsuBUA4J7FChwKFeKWgeCspuCth+Csh+CspeCsvuCsjxUA6J7FCh8KGOKWgeCsleCsv+Csm+Csv+CspuCsv+CsqBUA8J7FCiIKG+KWgeCsrOCsvuCsuOCsv+CsqOCtjeCspuCsvhUA+J7FCgkKAjAwFQAAn8UKEAoJ4paB4KyH4KywFQAIn8UKEwoM4Kys4Kyo4K2N4KykFQAQn8UKEwoM4paB4KyF4Kyc4K2fFQAYn8UKEwoM4paB4KyX4K2N4KyyFQAgn8UKEwoM4paB4Kyo4Ky/4KykFQAon8UKEwoM4paB4Kyy4K2B4KyaFQAwn8UKFgoP4paB4Kyr4Ky+4Kyw4K2NFQA4n8UKFgoP4paB4Kyv4K2B4Kys4KykFQBAn8UKFgoP4paB4Ky54K2N4K2f4Ky+FQBIn8UKHAoV4paB4KyF4Kyw4K2N4Kyc4K2B4KyoFQBQn8UKHAoV4paB4KyT4Ky54K2N4Kyy4Ky+4KyHFQBYn8UKKwok4paB4Kyu4Ky54Ky+4Kyq4K2N4Kyw4Kyt4K2B4KyZ4K2N4KyVFQBgn8UKCwoE4paBeRUAaJ/FChMKDOCspOCth+CsrOCthxUAcJ/FChMKDOCsqOCtjeCtn+CtgRUAeJ/FChMKDOCsuOCsmeCtjeCslxUAgJ/FChMKDOKWgeCsieCsoOCsvhUAiJ/FChMKDOKWgeCsreCsvuCsnBUAkJ/FChYKD+CssuCsvuCspuCth+CsthUAmJ/FChYKD+KWgeCsheCsreCtjeCtnxUAoJ/FChYKD+KWgeCsieCsquCssOCtgRUAqJ/FChYKD+KWgeCsnOCsvuCso+CthxUAsJ/FChYKD+KWgeCsqOCsv+CstuCsvhUAuJ/FChYKD+KWgeCsruCsvuCsqOCsvxUAwJ/FChkKEuKWgeCsleCsueCtgeCsm+CsvxUAyJ/FChkKEuKWgeCstuCsrOCtjeCspuCssBUA0J/FChwKFeKWgeCsleCssOCsv+Csr+CsvuCshxUA2J/FChwKFeKWgeCsl+CsuOCtjeCspOCssOCthxUA4J/FChwKFeKWgeCsuOCtjeCtseCsvuCsh+CsgRUA6J/FCh8KGOKWgeCsruCsp+CtgeCsuOCtguCspuCsqBUA8J/FCiIKG+KWgeCsquCtjeCssOCsvuCssOCsruCtjeCsrRUA+J/FCg0KBuCtp+CtrRUAAKDFChAKCeKWgeCsrOCsoxUACKDFChMKDOCsl+CtjeCssOCthxUAEKDFChMKDOCsruCtjeCsquCssBUAGKDFChMKDOCsuOCtjeCssOCthxUAIKDFChMKDOKWgeCsuOCsvuCsqhUAKKDFChYKD+KWgeCssOCsvuCsnOCtgRUAMKDFChYKD+KWgeCsuOCsv+Csp+CsvhUAOKDFChkKEuCspuCth+CsrOCsmeCtjeCslRUAQKDFChkKEuKWgeCsleCsvuCsqOCtjeCspxUASKDFChkKEuKWgeCsquCsvuCtseCsvuCssBUAUKDFChkKEuKWgeCsrOCsvuCsqOCtjeCspxUAWKDFChkKEuKWgeCsr+CsvuCsieCsm+CtgRUAYKDFChwKFeKWgeCsheCsqOCtjeCspOCsv+CsrhUAaKDFCh8KGOKWgeCssOCsluCsv+CspeCsv+CsrOCsvhUAcKDFCh8KGOKWgeCssuCti+CsleCsmeCtjeCsleCssBUAeKDFCh8KGOKWgeCsuOCsv+Csp+CsvuCsuOCss+CslhUAgKDFCg0KBuCsluCtgBUAiKDFCgsKBOKWgUsVAJCgxQoQCgngrKPgrL/grKQVAJigxQoQCgngrKrgrKjgrL4VAKCgxQoQCgngrKzgrL/grKQVAKigxQoQCgniloHgrKjgrYwVALCgxQoTCgziloHgrKzgrL7grJoVALigxQoWCg/iloHgrLjgrKzgrYHgrJwVAMCgxQoWCg/iloHgrLjgrK7grKjgrY0VAMigxQoZChLiloHgrJrgrKjgrY3grKbgrKgVANCgxQoZChLiloHgrbHgrL7grLDgrY3grKEVANigxQocChXiloHgrLjgrY3grJXgrYHgrLLgrY0VAOCgxQofChjgrKjgrL/grLDgrY3grKzgrL7grJrgrKgVAOigxQoiChviloHgrKrgrJ7grY3grJrgrL7grZ/grKTgrLAVAPCgxQoNCgbgrIXgrKgVAPigxQoQCgngrLLgrKTgrL4VAAChxQoQCgniloHgrKrgrKEVAAihxQoTCgziloHgrIXgrKXgrJoVABChxQoTCgziloHgrJrgrYzgrKcVABihxQoTCgziloHgrKvgrLPgrL4VACChxQoTCgziloHgrKzgrL7grIIVACihxQoWCg/iloHgrIXgrK7grL/grKQVADChxQoWCg/iloHgrJzgrKzgrL7grKwVADihxQoWCg/iloHgrJzgrL7grLngrL4VAEChxQoWCg/iloHgrK7grY3grZ/grYEVAEihxQoWCg/iloHgrLjgrL/grLLgrL8VAFChxQoZChLiloHgrKjgrL7grJ/grY3grZ8VAFihxQocChXiloHgrIXgrILgrJrgrLPgrLDgrYcVAGChxQocChXiloHgrIXgrKfgrY3grZ/grZ/grKgVAGihxQocChXiloHgrKjgrL/grJXgrJ/grJXgrYEVAHChxQocChXiloHgrLjgrYfgrKPgrY3grJ/grLAVAHihxQocChXiloHgrLjgrY3grKXgrL7grZ/grYAVAIChxQofChjiloHgrJrgrYfgrKjgrY3grKjgrL7grIcVAIihxQooCiHiloHgrJXgrLDgrL7grK/grL7grIngrKXgrL/grKzgrL4VAJChxQooCiHiloHgrLngrYvgrIfgrKrgrL7grLDgrKjgrY3grKTgrL8VAJihxQoNCgbgrYLgrLkVAKChxQoQCgniloHgrargrakVAKihxQoTCgzgrKbgrYfgrKzgrYAVALChxQoTCgzgrK7grYLgrLPgrJUVALihxQoTCgzgrLDgrYLgrKrgrYcVAMChxQoTCgziloHgrIbgrLDgrL4VAMihxQoTCgziloHgrKrgrYzgrLAVANChxQoWCg/iloHgrKzgrY3grLDgrJwVANihxQoWCg/iloHgrKzgrY3grLDgrKQVAOChxQoZChLiloHgrIngrKrgrLngrL7grLAVAOihxQoZChLiloHgrJfgrL7grIHgrJXgrYEVAPChxQoZChLiloHgrJzgrK7grL/grLDgrYcVAPihxQoZChLiloHgrKvgrL7grIfgrKbgrL4VAACixQoZChLiloHgrLjgrK7grY3grK3grL4VAAiixQocChXiloHgrKzgrL7grLngrL/grKjgrYAVABCixQocChXiloHgrK/grL7grIfgrKXgrL7grI8VABiixQofChjiloHgrKzgrL/grLjgrY3grKTgrL7grLAVACCixQofChjiloHgrKzgrY3grZ/grL7grKrgrL7grLAVACiixQoJCgJpZBUAMKLFCg0KBuCsguCsnxUAOKLFCg0KBuCsj+CsrBUAQKLFCg0KBuCsr+CtixUASKLFChAKCeCsuOCssOCtjRUAUKLFChAKCeCsvuCspOCtgRUAWKLFChMKDOCspuCso+CtjeCsoRUAYKLFChMKDOKWgeCsleCsoeCsvhUAaKLFChMKDOKWgeCsueCsqOCtgRUAcKLFChYKD+CsrOCsv+Csp+CsvuCsqBUAeKLFChYKD+KWgeCsnOCsoeCsv+CspBUAgKLFChYKD+KWgeCsnOCsrOCssOCsphUAiKLFChYKD+KWgeCsruCsvuCssOCtgRUAkKLFChYKD+KWgeCsruCtgeCsuOCsshUAmKLFChkKEuKWgeCsquCti+Cst+CsvuCslRUAoKLFChkKEuKWgeCstuCth+Ctn+CsvuCssBUAqKLFChwKFeKWgeCsquCtguCssOCtjeCsrOCthxUAsKLFCh8KGOKWgeCsrOCsv+CstuCtjeCtseCssOCthxUAuKLFCh8KGOKWgeCsueCti+Csh+CsuOCsvuCssOCsvxUAwKLFCiIKG+KWgeCspuCsv+CshuCsr+CsvuCsieCsm+CsvxUAyKLFCiIKG+KWgeCsquCsmuCssOCsvuCsieCsmuCssOCsvhUA0KLFChAKCeCsleCsv+CshhUA2KLFChMKDOCsleCtjeCsn+CsvxUA4KLFChMKDOCsrOCsueCsvuCssBUA6KLFChMKDOKWgeCspuCtg+CsohUA8KLFChMKDOKWgeCsrOCsvuCslxUA+KLFChMKDOKWgeCsrOCth+CsshUAAKPFChkKEuKWgeCshuCsuOCsqOCssOCthxUACKPFChkKEuKWgeCsleCssOCsv+CsrOCsvxUAEKPFChwKFeCsnOCsl+CsqOCtjeCsqOCsvuCspRUAGKPFChwKFeKWgeCsj+CsueCsv+CsreCss+CsvxUAIKPFChwKFeKWgeCsr+CtgeCsleCtjeCspOCsvxUAKKPFCh8KGOKWgeCsqOCtgeCsueCsqOCtjeCspOCsvxUAMKPFCh8KGOKWgeCsuOCsguCssOCsleCtjeCst+CsoxUAOKPFCiIKG+KWgeCsruCsueCth+CsqOCtjeCspuCtjeCssBUAQKPFCgkKAmFkFQBIo8UKDQoG4Kyn4K2AFQBQo8UKEAoJ4Kyh4Ky+4KyHFQBYo8UKEwoM4paB4KyV4Kyw4KyjFQBgo8UKEwoM4paB4Kyq4K2A4KygFQBoo8UKFgoP4Ky24Ky/4Kyz4K2N4KyqFQBwo8UKFgoP4paB4KyF4Kyc4Kyj4Ky+FQB4o8UKFgoP4paB4Kyy4Kyj4K2N4KyhFQCAo8UKGQoS4paB4KyF4Kyq4Kyu4Ky+4KyoFQCIo8UKGQoS4paB4Kyq4Ky/4KyH4Kys4Ky+FQCQo8UKHAoV4Ky44K2N4KyV4K2D4Kyk4Ky/4KyVFQCYo8UKHAoV4paB4Kys4Ky/4Kyc4K2B4Kyz4Ky/FQCgo8UKHwoY4paB4KyV4Kyw4Ky/4Kym4K2H4Kyy4K2HFQCoo8UKHwoY4paB4Kyq4K2N4Kyw4K2H4Kyw4Kyj4Ky+FQCwo8UKIgob4paB4Kyq4Kyh4Ky84Ky/4Kyl4Ky/4Kyy4Ky+FQC4o8UKJQoe4paB4Kyo4Kys4Kyw4KyZ4K2N4KyX4Kyq4K2B4KywFQDAo8UKEAoJ4Kyw4Ky/4KyCFQDIo8UKEAoJ4paB4Kyo4KyIFQDQo8UKEwoM4Kys4Ky+4Kyo4K2AFQDYo8UKEwoM4K2N4K2x4Ky+4KyoFQDgo8UKFgoP4paB4KyG4Kya4Kyw4KyjFQDoo8UKFgoP4paB4KyX4K2B4Kyc4K2BFQDwo8UKFgoP4paB4Kys4Ky+4Kyw4Ky/FQD4o8UKFgoP4paB4Kyv4Kyk4K2N4KyoFQAApMUKFgoP4paB4Kyw4Kyk4K2N4KyoFQAIpMUKFgoP4paB4Ky44K2H4KyV4K2HFQAQpMUKGQoS4paB4KyV4K2N4K2f4Ky+4KysFQAYpMUKHAoV4paB4KyG4Ky44Ky/4Kyl4Ky+4KyPFQAgpMUKHAoV4paB4KyV4K2N4Kyw4Ky+4KyH4KyuFQAopMUKHAoV4paB4Kyu4Kyk4K2N4Ky44K2N4K2fFQAwpMUKHwoY4paB4KyT4Kyh4Ky84Ky/4KyG4Kyw4K2HFQA4pMUKIgob4paB4KyF4Kyo4K2B4Kyn4K2N4K2f4Ky+4KyoFQBApMUKIgob4paB4KyG4Kya4Ky+4Kyw4K2N4Kyv4K2N4K2fFQBIpMUKJQoe4paB4Kyq4K2N4Kyw4Kyk4Ky/4Kys4Kyw4K2N4Ky3FQBQpMUKDQoG4Kyi4K2AFQBYpMUKEAoJ4paB4Kyy4KyiFQBgpMUKEwoM4paB4KyV4K2B4KyzFQBopMUKEwoM4paB4Kyo4Ky+4Ky2FQBwpMUKEwoM4paB4Kyu4Ky+4KynFQB4pMUKFgoP4paB4Kyf4Ky+4Kyj4Ky/FQCApMUKFgoP4paB4Kys4Ky+4Ky54K2BFQCIpMUKFgoP4paB4Kyy4K2B4Ky54Ky+FQCQpMUKGQoS4paB4KyF4Kys4Kym4Ky+4KyoFQCYpMUKGQoS4paB4Kyo4Ky/4Kys4K2H4Ky2FQCgpMUKGQoS4paB4Kyq4KyV4K2N4Ky34K2AFQCopMUKGQoS4paB4Kyq4Ky54KyC4Kya4Ky/FQCwpMUKGQoS4paB4Ky24K2N4Kyw4K2A4KyyFQC4pMUKIgob4paB4Kyv4K2H4KyJ4KyB4Kyg4Ky+4Kyw4K2HFQDApMUKEwoM4Kya4K2N4Kyb4Ky/FQDIpMUKFgoP4paB4Kyb4Kyk4Ky/4Ky2FQDQpMUKFgoP4paB4Kyw4Kyu4K2H4Ky2FQDYpMUKGQoS4paB4KyJ4Kyq4KyV4K2C4KyzFQDgpMUKGQoS4paB4Kym4K2N4K2x4Ky+4KywFQDopMUKGQoS4paB4Kyq4K2N4Kyw4Kyt4K2DFQDwpMUKGQoS4paB4Kys4Kyc4K2H4Kyf4K2NFQD4pMUKGQoS4paB4Kyw4KyZ4K2N4KyX4KywFQAApcUKGQoS4paB4Ky24K2N4K2f4Ky+4KyuFQAIpcUKHAoV4paB4Kyq4Kyh4Ky84Ky/4Kyy4Ky+FQAQpcUKHAoV4paB4Kyw4KyY4K2B4Kyo4Ky+4KylFQAYpcUKHwoY4paB4KyF4Kys4Ky44K2N4Kyl4Ky/4KykFQAgpcUKHwoY4paB4Ky44Ky+4KyZ4K2N4KyX4KyV4K2BFQAopcUKJQoe4paB4Kys4K2N4K2f4Kys4Ky44Ky+4K2f4Kyw4K2HFQAwpcUKJQoe4paB4Ky44K2B4Kyw4K2H4Kyo4K2N4Kym4K2N4KywFQA4pcUKKAoh4paB4Ky44K2N4Kyl4Ky+4Kyo4Ky+4Kyo4K2N4Kyk4KywFQBApcUKCQoCdXMVAEilxQoQCgniloHgrLDgrYIVAFClxQoTCgziloHgrIbgrLLgrYEVAFilxQoTCgziloHgrKzgrL/grKoVAGClxQoWCg/grIbgrKjgrY3grKTgrYEVAGilxQoWCg/grLbgrY3grJrgrL/grKQVAHClxQoWCg/iloHgrKjgrY3grZ/grL4VAHilxQoWCg/iloHgrKrgrLPgrL7grIcVAIClxQoWCg/iloHgrK7grL/grLbgrKgVAIilxQoWCg/iloHgrLjgrJ7grY3grJoVAJClxQoWCg/iloHgrLngrL7grJzgrLAVAJilxQoZChLiloHgrKrgrL7grLDgrY3grKUVAKClxQocChXiloHgrJXgrY3grZ/grL7grK7grYcVAKilxQocChXiloHgrLDgrL7grIngrLDgrJXgrYcVALClxQofChjiloHgrJXgrL7grLDgrY3grKTgrY3grKQVALilxQofChjiloHgrJXgrY3grZ/grL7grK7grY3grKoVAMClxQofChjiloHgrK/grL7grIngrKXgrL/grLLgrYcVAMilxQofChjiloHgrLjgrKTgrY3grKTgrY3grbHgrYcVANClxQofChjiloHgrLngrL/grLjgrL7grKzgrLDgrYcVANilxQolCh7iloHgrLbgrJXgrY3grKTgrL/grLbgrL7grLPgrYAVAOClxQolCh7iloHgrLjgrK7grY3grKrgrLDgrY3grJXgrYDgrZ8VAOilxQooCiHiloHgrLjgrL7grILgrLjgrY3grJXgrYPgrKTgrL/grJUVAPClxQorCiTiloHgrIXgrKjgrY3grKTgrLDgrY3grK3grYHgrJXgrY3grKQVAPilxQorCiTiloHgrLngrYvgrIfgrKXgrL/grKzgrL7grKzgrYfgrLPgrYcVAACmxQoNCgbgrafgraoVAAimxQoNCgbgra/gra8VABCmxQoQCgngrKfgrY3grKwVABimxQoQCgngrKrgrY3grLMVACCmxQoQCgngrLLgrL7grLIVACimxQoQCgngrLPgrKTgrL4VADCmxQoQCgniloHgrLjgrKsVADimxQoTCgzgrK7grYHgrKjgrL4VAECmxQoTCgziloHgrI/grKXgrYEVAEimxQoTCgziloHgrK3grYvgrLAVAFCmxQoWCg/iloHgrJfgrKLgrLzgrL8VAFimxQocChXiloHgrIbgrKbgrL/grKTgrY3grZ8VAGCmxQofChjiloHgrKTgrL7grK7grL/grLLgrKjgrL4VAGimxQofChjiloHgrK/grYHgrKbgrY3grKfgrLDgrYcVAHCmxQofChjiloHgrLjgrYLgrKTgrY3grLDgrLDgrYEVAHimxQorCiTiloHgrIXgrKfgrL/grJXgrL7grLDgrYDgrK7grL7grKjgrYcVAICmxQoJCgJlYxUAiKbFCg0KBuCsnOCssxUAkKbFChYKD+KWgeCsrOCsnOCsvuCtnxUAmKbFChYKD+KWgeCsr+CtgeCsl+CssBUAoKbFChYKD+KWgeCsr+Cti+CsoeCsvxUAqKbFChwKFeKWgeCsleCth+CspOCti+Csn+CsvxUAsKbFChwKFeKWgeCspOCsvuCsueCth+CssuCthxUAuKbFCh8KGOKWgeCsquCtjeCssOCspuCssOCtjeCsthUAwKbFCiIKG+KWgeCsieCsquCth+CsqOCtjeCspuCtjeCssBUAyKbFCg0KBuCstuCtghUA0KbFChAKCeCsluCsvuCshxUA2KbFChAKCeCsluCsv+CshhUA4KbFCgwKBeKWgXx8FQDopsUKEwoM4paB4KyV4K2L4KygFQDwpsUKFgoP4paB4Kyu4K2L4Ky54KywFQD4psUKGQoS4paB4Kya4Ky+4Kyy4Ky/4KysFQAAp8UKGQoS4paB4Kyf4Ky/4KyV4K2H4KyfFQAIp8UKGQoS4paB4Ky44KyC4Kyv4K2L4KycFQAQp8UKHAoV4paB4Ky44K2N4Kys4Kyw4K2N4KyXFQAYp8UKHwoY4paB4KyV4Kyw4Ky/4Ky44Ky+4Kyw4Ky/FQAgp8UKIgob4paB4Ky44Ky+4Kyu4K2N4Kyo4Ky+4Kyw4K2HFQAop8UKJQoe4paB4Kyu4K2B4Ky54K2C4Kyw4K2N4Kyk4K2N4KykFQAwp8UKDQoG4K2n4K2pFQA4p8UKEAoJ4Kyh4Ky84K2BFQBAp8UKEAoJ4Kyu4Ky+4KyyFQBIp8UKDAoF4paBT2QVAFCnxQoQCgniloHgrKrgrKQVAFinxQoTCgzgrKjgrY3grKjgrL4VAGCnxQoTCgzgrK7grYHgrJbgrYcVAGinxQoWCg/grJXgrK7grL7grKjgrYcVAHCnxQoWCg/grJbgrY3grZ/grL7grKQVAHinxQoWCg/iloHgrLjgrYHgrKfgrL4VAICnxQoWCg/iloHgrbHgrL7grLDgrY0VAIinxQoZChLiloHgrLjgrL7grKzgrY3grZ8VAJCnxQocChXiloHgrLngrYfgrLLgrL7grKPgrL8VAJinxQofChjiloHgrKjgrKXgrL/grKzgrL7grLDgrYEVAKCnxQofChjiloHgrKrgrY3grLDgrKbgrYfgrLbgrLAVAKinxQofChjiloHgrKrgrY3grLDgrLbgrY3grKjgrLAVALCnxQofChjiloHgrK7grKjgrY3grKbgrL/grLDgrLAVALinxQofChjiloHgrK7grYvgrKbgrL/grJngrY3grJUVAMCnxQofChjiloHgrLDgrLngrYHgrKXgrL/grLLgrYcVAMinxQoiChviloHgrKrgrL/grLLgrL7grJngrY3grJXgrYEVANCnxQoJCgJSQRUA2KfFCg0KBuCshuCsnBUA4KfFChAKCeCsleCtgOCssBUA6KfFChAKCeCsreCssOCthxUA8KfFChAKCeKWgeCsoeCsgxUA+KfFChAKCeKWgeCsueCsnBUAAKjFChMKDOCsn+CtjeCssOCtixUACKjFChMKDOCstuCsleCtjeCspBUAEKjFChMKDOCsuOCtjeCsrOCsvhUAGKjFChMKDOKWgeCsleCssuCsvxUAIKjFChYKD+CtjeCtn+CsvuCssuCthxUAKKjFChYKD+KWgeCsieCspuCtjeCspxUAMKjFChkKEuKWgeCsquCtjeCssOCsvuCshxUAOKjFChkKEuKWgeCsruCtjOCss+Csv+CslRUAQKjFChkKEuKWgeCst+CtjeCsn+CsvuCssBUASKjFChwKFeKWgeCsquCsoeCsvOCsv+CssuCthxUAUKjFChwKFeKWgeCsuOCsmeCtjeCsl+CssOCthxUAWKjFCh8KGOKWgeCstuCtjeCssOCth+Cso+CtgOCssBUAYKjFCiIKG+KWgeCsnOCso+CsvuCsh+CspeCsv+CssuCthxUAaKjFCiUKHuKWgeCsqOCsvuCsueCsvuCsgeCsqOCtjeCspOCsvxUAcKjFChAKCeCshuCsnOCsvxUAeKjFChAKCeCsleCspOCsvhUAgKjFChAKCeCsqOCssOCtgRUAiKjFChAKCeCssOCsvuCslxUAkKjFChAKCeCssOCtgeCsoxUAmKjFChAKCeCsv+CssuCtjRUAoKjFCg0KBuKWgTIwMhUAqKjFChMKDOKWgeCsrOCsvuCsuRUAsKjFChYKD+Csr+Csv+CsrOCsvuCssBUAuKjFChkKEuKWgeCsh+CspOCsv+CsueCsvhUAwKjFChkKEuKWgeCsmuCth+CspOCsvuCsrBUAyKjFChkKEuKWgeCsneCsv+CsheCsleCtgRUA0KjFChkKEuKWgeCsrOCsv+CsrOCth+CslRUA2KjFChkKEuKWgeCstuCsv+Cst+CtjeCtnxUA4KjFChwKFeKWgeCsnOCti+CssOCspuCsvuCssBUA6KjFChwKFeKWgeCsruCtgeCspuCtjeCssOCsvhUA8KjFChwKFeKWgeCsuOCtjeCtseCsquCtjeCsqBUA+KjFChwKFeKWgeCsueCti+Csh+CsheCsm+CsvxUAAKnFCh8KGOKWgeCsleCssOCti+CsqOCsvuCssOCthxUACKnFCiIKG+KWgeCskOCspOCsv+CsueCsvuCsuOCsv+CslRUAEKnFCiUKHuKWgeCsoeCsvuCsleCtjeCspOCssOCsmeCtjeCslRUAGKnFCiUKHuKWgeCsquCtjeCssOCstuCsvuCsuOCsqOCsv+CslRUAIKnFCigKIeKWgeCsueCsvuCsh+CspuCtjeCssOCsvuCsrOCsvuCsphUAKKnFCisKJOKWgeCsquCsn+CtjeCsn+CsqOCsvuCtn+CsleCsmeCtjeCslRUAMKnFCgsKBOKWgWcVADipxQoQCgngrYDgrJXgrL4VAECpxQoQCgniloHgrIXgrKYVAEipxQoQCgniloHgrargrawVAFCpxQoTCgziloHgrIbgrKvgrJcVAFipxQoTCgziloHgrLjgrL7grIkVAGCpxQoWCg/grKrgrYvgrLDgrY3grJ8VAGipxQoWCg/iloHgrK7grL7grKTgrYMVAHCpxQoZChLiloHgrIXgrKzgrKTgrL7grLAVAHipxQoZChLiloHgrJbgrKPgrY3grKHgrL8VAICpxQocChXiloHgrKrgrY3grLDgrKTgrYDgrJUVAIipxQocChXiloHgrLngrL/grKjgrY3grKbgrYAVAJCpxQofChjiloHgrIXgrJfgrY3grKjgrL/grLbgrK4VAJipxQofChjiloHgrIXgrKTgrL/grJXgrY3grLDgrK4VAKCpxQoiChviloHgrJXgrYfgrKjgrY3grKbgrY3grLDgrLAVAKipxQolCh7iloHgrJXgrLDgrL/grIXgrJvgrKjgrY3grKTgrL8VALCpxQoNCgbgrYvgrKYVALipxQoKCgNpZ2gVAMCpxQoQCgniloHgrLjgrJUVAMipxQoTCgzgrJzgrY3grZ/grLAVANCpxQoWCg/iloHgrLDgrYfgrKHgrY0VANipxQoZChLgrKXgrL7grKjgrY3grKTgrYcVAOCpxQoZChLgrK7grKfgrY3grZ/grLDgrYcVAOipxQoZChLiloHgrIXgrKjgrYHgrK7grYsVAPCpxQoZChLiloHgrKzgrKPgrY3grJ/grKgVAPipxQoiChviloHgrLjgrL7grKzgrY3grZ/grLjgrY3grKQVAACqxQooCiHiloHgrKrgrL7grJXgrL/grLjgrY3grKTgrL7grKjgrLAVAAiqxQorCiTiloHgrIXgrKfgrL/grJXgrL7grLDgrYDgrJngrY3grJXgrYEVABCqxQoJCgJheRUAGKrFCg0KBuCshuCsiRUAIKrFCg0KBuCti+CsoxUAKKrFChAKCeCssuCsvuCsrRUAMKrFChAKCeKWgeCshuCssxUAOKrFChAKCeKWgeCsq+CssBUAQKrFChMKDOKWgeCsqOCsv+CslhUASKrFChMKDOKWgeCsqOCtgOCssBUAUKrFChYKD+CsoOCsvuCsleCsvuCssBUAWKrFChkKEuKWgeCsl+CspOCsv+CssOCthxUAYKrFChkKEuKWgeCsq+Css+CsvuCsq+CssxUAaKrFChkKEuKWgeCsr+CtgeCsl+CssOCthxUAcKrFChwKFeKWgeCsheCssOCtjeCspuCtjeCspxUAeKrFCh8KGOKWgeCsquCssOCsv+CsruCsvuCso+CssBUAgKrFCh8KGOKWgeCsrOCsv+CstuCtjeCtseCsvuCssxUAiKrFCiIKG+KWgeCsieCspOCtjeCspOCth+CsnOCsqOCsvhUAkKrFCiUKHuKWgeCsuOCsruCsuOCtjeCspOCsmeCtjeCsleCssBUAmKrFCjEKKuKWgeCsrOCsv+CstuCtjeCsrOCsrOCsv+CspuCtjeCtn+CsvuCss+CtnxUAoKrFCg0KBuCsl+CsmxUAqKrFCg0KBuCstuCsgxUAsKrFChMKDOCsoeCsvOCth+CshxUAuKrFChMKDOCssuCtjeCssuCsrRUAwKrFChMKDOKWgeCsleCsqOCslRUAyKrFChMKDOKWgeCsruCth+CsmBUA0KrFChMKDOKWgeCssOCsvuCskxUA2KrFChYKD+CsmuCtjeCtn+CtgeCspBUA4KrFChYKD+KWgeCsleCtg+CsquCsvhUA6KrFChYKD+KWgeCsrOCtgeCsneCsvhUA8KrFChYKD+KWgeCstuCspOCsquCspRUA+KrFChkKEuCspeCsv+CsquCsvuCsh+CsgRUAAKvFChkKEuKWgeCsmuCsqOCtjeCspOCsvhUACKvFChkKEuKWgeCsrOCth+CshuCsh+CsqBUAEKvFChkKEuKWgeCsuOCth+CsuOCsrOCtgRUAGKvFChkKEuKWgeCsuOCtjeCsrOCtn+CsghUAIKvFChwKFeKWgeCsleCssOCsvuCsl+CssuCsvhUAKKvFChwKFeKWgeCsnOCso+CsvuCsr+CsvuCsjxUAMKvFChwKFeKWgeCsr+CspeCsvuCssOCtjeCspRUAOKvFCh8KGOKWgeCsrOCsvuCsquCsvuCsmeCtjeCslRUAQKvFCh8KGOKWgeCsrOCsvuCsrOCtgeCsmeCtjeCslRUASKvFCiUKHuKWgeCsuOCsruCtjeCsrOCsqOCtjeCsp+CtgOCtnxUAUKvFCg0KBuCsm+CthxUAWKvFCgwKBeKWgW9uFQBgq8UKEAoJ4paB4Kyk4KyoFQBoq8UKEAoJ4paB4Ky24K2MFQBwq8UKEwoM4Kys4Ky+4K2f4K2BFQB4q8UKEwoM4Ky/4KyV4Kyw4KyjFQCAq8UKEwoM4paB4Kyh4Ky84Ky/FQCIq8UKFgoP4paB4Kys4Ky+4Kym4K2NFQCQq8UKFgoP4paB4Ky44Kyu4K2N4KytFQCYq8UKFgoP4paB4Ky54Ky/4Kyu4Ky+FQCgq8UKGQoS4paB4Kys4K2B4Kyi4Ky84Ky+FQCoq8UKGQoS4paB4Ky44Ky54Ky44K2N4KywFQCwq8UKHAoV4paB4KyK4Kyw4K2N4Kym4K2N4KynFQC4q8UKHAoV4paB4Ky44KyC4KyW4K2N4K2f4KyVFQDAq8UKHAoV4paB4Ky44K2B4Kyq4K2N4Kyw4Ky/FQDIq8UKHwoY4paB4KyG4Ky44Ky/4Kys4Ky+4KyV4K2BFQDQq8UKHwoY4paB4Kyc4Ky+4Kyj4Kyo4K2N4Kyk4Ky/FQDYq8UKHwoY4paB4Kyc4Ky+4Kyj4Kyo4K2N4Kyk4K2BFQDgq8UKHwoY4paB4Kym4Ky/4Kyy4K2N4Kyy4K2A4KywFQDoq8UKHwoY4paB4Kym4K2H4KyJ4Kyl4Ky/4Kys4Ky+FQDwq8UKHwoY4paB4Kyo4Ky/4Kyw4K2N4Kym4K2H4Ky2FQD4q8UKHwoY4paB4Kyq4Ky+4Kyw4K2N4Ky24K2N4KysFQAArMUKHwoY4paB4Kyq4K2N4Kyw4Kyk4Ky/4Kyt4Ky+FQAIrMUKIgob4paB4KyV4K2N4K2f4Ky+4Kys4Ky/4Kyo4K2HFQAQrMUKIgob4paB4Kyk4Ky54Ky44Ky/4Kyy4Kym4Ky+4KywFQAYrMUKIgob4paB4Kys4Ky+4Ky44K2N4Kyk4Kys4Kyw4K2HFQAgrMUKDQoG4K2M4KyqFQAorMUKEAoJ4KyG4Kyh4K2HFQAwrMUKEAoJ4KyX4Kyh4Ky+FQA4rMUKEAoJ4paB4Kys4KynFQBArMUKEAoJ4paB4Kyy4Ky5FQBIrMUKEwoM4Kyu4K2N4Kyq4Ky+FQBQrMUKEwoM4Ky+4Kyk4K2N4K2fFQBYrMUKEwoM4paB4KyV4K2L4KywFQBgrMUKEwoM4paB4Kyl4Ky/4KysFQBorMUKEwoM4paB4Kyq4Kym4Ky+FQBwrMUKEwoM4paB4Kyt4Ky/4KyfFQB4rMUKEwoM4paB4Kyu4K2B4KykFQCArMUKEwoM4paB4K2n4K2v4K2sFQCIrMUKFgoP4KyW4K2N4K2f4Ky+4KyoFQCQrMUKFgoP4paB4Kyf4Ky/4KyV4Ky4FQCYrMUKFgoP4paB4Ky34K2N4Kyf4Ky/FQCgrMUKGQoS4paB4Kyy4Ky+4KyX4Ky/4KysFQCorMUKHAoV4paB4KyX4Kyk4Kys4Kyw4K2N4Ky3FQCwrMUKHAoV4paB4Kyt4Ky/4Kyh4Ky84Ky/4KyTFQC4rMUKIgob4paB4KyP4KyV4K2N4Ky44Kyq4K2N4Kyw4K2HFQDArMUKIgob4paB4KyX4K2L4Kyq4Kys4Kyo4K2N4Kyn4K2BFQDIrMUKJQoe4paB4Ky24K2N4Kyw4Kym4K2N4Kyn4Ky+4Kyz4K2BFQDQrMUKCQoCZ2UVANisxQoJCgJseRUA4KzFCgoKA2lzdBUA6KzFChAKCeCsheCsqOCtgRUA8KzFChAKCeCsrOCtgeCsshUA+KzFCgwKBeKWgWlzFQAArcUKEAoJ4paB4K2q4K2nFQAIrcUKEwoM4Kyy4Ky+4KyH4KyoFQAQrcUKEwoM4paB4KyV4Ky+4Ky2FQAYrcUKEwoM4paB4Kya4K2L4KywFQAgrcUKEwoM4paB4Kyo4K2H4KysFQAorcUKEwoM4paB4Kyt4K2L4KyHFQAwrcUKEwoM4paB4Ky24Kyw4KykFQA4rcUKEwoM4paB4K2n4K2v4K2rFQBArcUKEwoM4paB4K2r4K2m4K2mFQBIrcUKFgoP4paB4Kyg4Ky/4KyV4Ky+FQBQrcUKFgoP4paB4Kyn4Ky+4KyH4KyBFQBYrcUKFgoP4paB4Kyw4Ky/4K2f4Ky+FQBgrcUKGQoS4paB4KyV4Ky+4Kyo4K2N4KymFQBorcUKGQoS4paB4Kyq4Ky+4Kyg4K2N4K2fFQBwrcUKHAoV4KyV4K2H4Kyo4K2N4Kym4K2N4KywFQB4rcUKHAoV4Kyv4Ky+4KyJ4Kyl4Ky/4Kyy4Ky+FQCArcUKHAoV4paB4Ky44Ky+4Kyu4K2N4Kyo4Ky+FQCIrcUKHwoY4paB4Kyn4Ky+4Kyw4K2N4Kyv4K2N4K2fFQCQrcUKHwoY4paB4Kyq4Ky+4KyH4Kys4Ky+4KyV4K2BFQCYrcUKKAoh4paB4Ky54K2L4KyH4Kyv4Ky+4KyH4Kyl4Ky/4Kyy4K2HFQCgrcUKKwok4paB4Kyq4K2N4Kyw4Kyk4Ky/4Kyv4K2L4KyX4Ky/4Kyk4Ky+FQCorcUKDQoG4Ky34K2AFQCwrcUKEAoJ4Kym4KyV4K2BFQC4rcUKEAoJ4Kyo4K2A4KykFQDArcUKEAoJ4paB4KyP4KyrFQDIrcUKEAoJ4paB4Kyc4KyyFQDQrcUKEwoM4KyV4K2D4Kyk4Ky/FQDYrcUKEwoM4Kyn4Ky+4Kyw4K2AFQDgrcUKEwoM4Kys4Ky+4Kym4KywFQDorcUKEwoM4Kyu4Ky+4Kyw4K2NFQDwrcUKEwoM4paB4KyG4KyW4K2BFQD4rcUKEwoM4paB4KyG4Ky24K2AFQAArsUKEwoM4paB4Kyb4Kyh4Ky+FQAIrsUKEwoM4paB4Kyk4K2H4KycFQAQrsUKEwoM4paB4Kys4Kya4KyoFQAYrsUKEwoM4paB4Kyv4Ky+4KykFQAgrsUKFgoP4paB4Kyq4Kyb4KyV4K2BFQAorsUKFgoP4paB4Kys4Ky+4Kyj4K2AFQAwrsUKFgoP4paB4Kyy4K2B4KyX4Ky+FQA4rsUKGQoS4Kys4Ky/4Kym4K2N4K2f4Ky+FQBArsUKGQoS4Kyu4K2B4Kyj4K2N4Kyh4Ky/FQBIrsUKGQoS4paB4KyT4Kyt4Kyw4Kyw4K2HFQBQrsUKGQoS4paB4Kym4Kyw4K2N4Ky24Ky+FQBYrsUKGQoS4paB4Kyq4Ky+4KyW4Kyw4K2BFQBgrsUKGQoS4paB4Kyr4K2H4Kyw4Ky+4KyHFQBorsUKGQoS4paB4Kyu4Ky54Ky/4Kyu4Ky+FQBwrsUKGQoS4paB4Ky44K2B4Kyo4K2N4KymFQB4rsUKHAoV4paB4KyT4Kyh4Ky/4Ky24Ky+4KywFQCArsUKHAoV4paB4Kym4KyV4K2N4Ky34Kyk4Ky+FQCIrsUKHwoY4Kyu4K2C4Kyw4K2N4Kyk4K2N4Kyk4Ky/FQCQrsUKHwoY4paB4Kya4K2H4Kyk4Ky+4Kys4Kyo4K2AFQCYrsUKHwoY4paB4Kyf4Ky+4Kyw4K2N4KyX4K2H4KyfFQCgrsUKHwoY4paB4Kyq4Ky+4KyH4Kyl4Ky/4Kys4Ky+FQCorsUKJQoe4paB4KyP4Ky54Ky+4Kym4K2N4K2x4Ky+4Kyw4Ky+FQCwrsUKJQoe4paB4Kya4Ky/4KyV4Ky/4Kyk4K2N4Ky44Ky/4KykFQC4rsUKJQoe4paB4Kyw4Ky+4Kyc4Kyo4K2A4Kyk4Ky/4Kyw4K2HFQDArsUKKAoh4paB4Kym4K2B4Kyw4K2N4KyY4Kyf4Kyj4Ky+4Kyw4K2HFQDIrsUKCQoCSU4VANCuxQoNCgbgrILgrJUVANiuxQoNCgbgrJ3grL8VAOCuxQoNCgbgrajgrasVAOiuxQoQCgngrLbgrL/grKQVAPCuxQoQCgngrLjgrL/grLAVAPiuxQoTCgzgrJ/grLDgrLDgrYcVAACvxQoTCgzgrKPgrL/grJXgrL8VAAivxQoTCgziloHgrKjgrL7grJoVABCvxQoTCgziloHgrKjgrL/grKYVABivxQoTCgziloHgrK7grL7grIkVACCvxQoTCgziloHgrLjgrYfgrKUVACivxQoWCg/grKbgrYfgrLbgrL/grJUVADCvxQoWCg/iloHgrJXgrL/grK7grL8VADivxQoWCg/iloHgrLDgrL7grKPgrYAVAECvxQoWCg/iloHgrLjgrYfgrKDgrYAVAEivxQoWCg/iloHgrLngrYDgrLDgrL4VAFCvxQoZChLiloHgrJfgrY3grLDgrYfgrJcVAFivxQoZChLiloHgrKrgrYHgrKPgrY3grZ8VAGCvxQocChXiloHgrKrgrLDgrL7grLjgrY3grKQVAGivxQocChXiloHgrKzgrLjgrY3grKTgrYHgrLAVAHCvxQofChjiloHgrJjgrKPgrY3grJ/grL7grLDgrYcVAHivxQofChjiloHgrKjgrL/grLfgrY3grKrgrKTgrL8VAICvxQoiChviloHgrKzgrL7grJrgrLjgrY3grKrgrKTgrL8VAIivxQoNCgbgrYDgrKwVAJCvxQoNCgbgrZ/grYsVAJivxQoTCgzgrJzgrKjgrL/grKQVAKCvxQoTCgzgrLbgrY3grLDgrYcVAKivxQoTCgziloHgrK3grYDgrK4VALCvxQoWCg/iloHgrJfgrKDgrL/grKQVALivxQoWCg/iloHgrKjgrL/grLDgrYAVAMCvxQoWCg/iloHgrKzgrJzgrY3grLAVAMivxQoZChLiloHgrIXgrKjgrY3grKTgrLAVANCvxQoZChLiloHgrKvgrL7grIfgrKjgrL4VANivxQoZChLiloHgrKzgrY3grLLgrJXgrY0VAOCvxQocChXiloHgrKrgrL7grKPgrY3grKDgrL8VAOivxQoiChviloHgrKrgrY3grLDgrKTgrL/grLDgrYvgrKcVAPCvxQoNCgbgrKrgrYMVAPivxQoLCgTiloE2FQAAsMUKEAoJ4KyV4Ky+4KyuFQAIsMUKEAoJ4Ky54K2H4KyJFQAQsMUKEwoM4Kyh4Ky/4Kyq4Ky/FQAYsMUKEwoM4Ky54Ky+4Kyw4K2BFQAgsMUKEwoM4paB4Kyq4Kyk4Ky/FQAosMUKEwoM4paB4Kyq4Ky+4KyPFQAwsMUKEwoM4paB4Kyu4Kym4KyoFQA4sMUKFgoP4KyV4K2N4Ky34Kyw4K2HFQBAsMUKFgoP4paB4KyP4Kyk4Kyy4Ky+FQBIsMUKFgoP4paB4Kyy4Ky/4Kyf4KywFQBQsMUKGQoS4paB4Kyq4K2D4Kyl4K2N4KysFQBYsMUKHAoV4paB4KyV4Kyw4K2L4Kyo4Ky+4KywFQBgsMUKHAoV4paB4Kyo4K2N4K2f4K2B4Kyc4Ky/FQBosMUKHwoY4paB4Kyq4K2N4Kyw4Kym4K2C4Ky34KyjFQBwsMUKHwoY4paB4Kyq4K2N4Kyw4K2H4Kyu4Ky/4KyVFQB4sMUKIgob4paB4Kyc4Kyj4Ky+4Kyv4Ky+4KyH4Kyb4Ky/FQCAsMUKIgob4paB4Ky44Ky/4Kyy4Ky/4Kyj4K2N4Kyh4KywFQCIsMUKJQoe4paB4KyF4Kyw4K2N4Kyl4Kyo4K2I4Kyk4Ky/4KyVFQCQsMUKJQoe4paB4KyV4Kyw4K2N4Kyj4K2N4Kyj4Ky+4Kyf4KyVFQCYsMUKEAoJ4Kyj4K2N4KykFQCgsMUKEAoJ4Kyq4K2B4KyFFQCosMUKEAoJ4Ky24Ky/4Ky3FQCwsMUKEAoJ4paB4Kyw4K2NFQC4sMUKEwoM4Kyy4K2B4Kyw4K2BFQDAsMUKEwoM4paB4Kya4Kyi4Ky8FQDIsMUKEwoM4paB4Kya4Ky/4KywFQDQsMUKEwoM4paB4Kym4Ky24KyuFQDYsMUKFgoP4Kym4K2D4Ky24K2N4K2fFQDgsMUKFgoP4paB4KyF4KyX4K2N4KyoFQDosMUKFgoP4paB4Ky24Ky/4KyW4Ky/FQDwsMUKGQoS4Kys4K2D4Kyk4K2N4Kyk4Ky/FQD4sMUKHAoV4paB4KyG4Kyw4K2N4Kyv4K2N4K2fFQAAscUKHAoV4paB4Kyq4K2N4Kyw4Kys4Ky+4Ky5FQAIscUKHAoV4paB4Kyu4Ky54KyZ4K2N4KyX4Ky+FQAQscUKHwoY4paB4Ky24K2D4KyZ4K2N4KyW4Kyz4Ky+FQAYscUKIgob4paB4Kyq4K2N4Kyw4K2L4Kyk4K2N4Ky44Ky+FQAgscUKDQoG4K2o4K2uFQAoscUKCwoE4paBShUAMLHFCgsKBOKWgVEVADixxQoKCgNvdXIVAECxxQoQCgngrJjgrY3grKgVAEixxQoQCgniloHgrK7grIkVAFCxxQoTCgzgrKHgrL7grLDgrYcVAFixxQoTCgziloHgrIbgrLngrY0VAGCxxQoTCgziloHgrKzgrKbgrLMVAGixxQoTCgziloHgrazgrJ/grL8VAHCxxQoWCg/iloHgrIXgrKTgrYDgrKQVAHixxQoWCg/iloHgrIbgrZ/grYvgrJcVAICxxQoWCg/iloHgrJXgrY3grLfgrZ8VAIixxQoWCg/iloHgrLjgrL7grKjgrL8VAJCxxQoZChLgrKzgrY3grZ/grL7grKrgrYAVAJixxQoZChLiloHgrIXgrLjgrLngrL7grZ8VAKCxxQoZChLiloHgrKjgrL/grJbgrYvgrJwVAKixxQoiChviloHgrIXgrLDgrY3grKXgrKjgrYDgrKTgrL8VALCxxQoiChviloHgrKrgrL7grIfgrJvgrKjgrY3grKTgrL8VALixxQolCh7iloHgrKjgrYLgrIbgrKbgrL/grLLgrY3grLLgrYAVAMCxxQoQCgngrLjgrILgrJgVAMixxQoTCgzgrKjgrY3grKXgrL4VANCxxQoTCgziloHgrIbgrKzgrL8VANixxQoTCgziloHgrJXgrL7grIkVAOCxxQoTCgziloHgrKzgrKjgrL4VAOixxQoTCgziloHgrKzgrYfgrJcVAPCxxQoWCg/grJzgrY3grZ/grLDgrYcVAPixxQoWCg/grKPgrY3grJ/grLDgrYcVAACyxQoWCg/grK7grYHgrJXgrY3grKQVAAiyxQoWCg/grLDgrY3grKzgrL7grKYVABCyxQoWCg/iloHgrIbgrKHgrJXgrYEVABiyxQoWCg/iloHgrIbgrKrgrKPgrL4VACCyxQoWCg/iloHgrJbgrL7grKjgrY0VACiyxQoWCg/iloHgrJfgrYDgrKTgrL4VADCyxQoWCg/iloHgrKbgrL7grLDgrY0VADiyxQoWCg/iloHgrKrgrL7grLjgrY0VAECyxQoWCg/iloHgrKvgrYHgrJ/grL8VAEiyxQoZChLiloHgrIXgrKfgrY3grKzgrJUVAFCyxQoZChLiloHgrJfgrYPgrLngrLDgrYcVAFiyxQoZChLiloHgrJzgrK7grY3grK7grYEVAGCyxQoZChLiloHgrKjgrLngrYfgrLLgrYcVAGiyxQoZChLiloHgrLLgrL/grJngrY3grJcVAHCyxQocChXiloHgrKbgrLDgrY3grLbgrL7grIcVAHiyxQocChXiloHgrKrgrILgrJrgrL7grZ/grKQVAICyxQocChXiloHgrKzgrL/grJXgrLPgrY3grKoVAIiyxQofChjiloHgrKrgrL7grLLgrJ/grL/grJvgrL8VAJCyxQolCh7iloHgrIbgrK7grY3grK3grYfgrK7grL7grKjgrYcVAJiyxQooCiHiloHgrKzgrL/grKfgrL7grKjgrLjgrK3grL7grLDgrYcVAKCyxQoJCgJuZxUAqLLFChAKCeCsuOCsvuCsqBUAsLLFChAKCeKWgeCsheCsiRUAuLLFChAKCeKWgeCspuCsiRUAwLLFChMKDOCsn+CtjeCsn+CsvhUAyLLFChMKDOCspuCsvuCsqOCtgBUA0LLFChMKDOKWgeCspuCsruCtjRUA2LLFChMKDOKWgeCspuCth+CsshUA4LLFChYKD+KWgeCspuCtjeCsrOCsvxUA6LLFChYKD+KWgeCsp+Cti+CsqOCtgBUA8LLFChYKD+KWgeCsuOCsguCsmOCssBUA+LLFChkKEuCsuOCtjeCspOCtjeCssOCtgBUAALPFChkKEuKWgeCsmuCss+CsvuCsmuCssxUACLPFChkKEuKWgeCsruCtg+CspOCtjeCtnxUAELPFChwKFeKWgeCsj+CsleCsvuCsqOCtjeCspBUAGLPFChwKFeKWgeCspOCtgeCsruCtjeCsreCthxUAILPFCh8KGOKWgeCspuCth+CsieCspeCsv+CssuCthxUAKLPFCiIKG+KWgeCshuCspOCsmeCtjeCsleCsrOCsvuCsphUAMLPFCiIKG+KWgeCsleCtjeCssOCsv+CsleCth+Csn+CtjRUAOLPFCiIKG+KWgeCssuCth+CsluCsv+CspeCsv+CssuCthxUAQLPFCgoKA3ZlchUASLPFChAKCeCspeCsvuCshxUAULPFChAKCeCsrOCth+CssxUAWLPFChAKCeCsruCtguCssxUAYLPFChAKCeCsvuCsuOCthxUAaLPFCgwKBeKWgVBQFQBws8UKEwoM4K2L4KyX4K2N4K2fFQB4s8UKDQoG4paBZm9yFQCAs8UKEwoM4paB4Kys4K2B4KyyFQCIs8UKEwoM4paB4Kyy4K2B4KyjFQCQs8UKFgoP4Kyl4Ky/4Kys4K2A4KywFQCYs8UKFgoP4Ky+4Kyw4K2N4Kyc4K2AFQCgs8UKFgoP4paB4KyV4K2B4KyV4K2BFQCos8UKFgoP4paB4Kyo4Ky+4KyP4KyVFQCws8UKFgoP4paB4Kyt4K2B4Kyu4K2HFQC4s8UKFgoP4paB4Kyu4K2M4Ky44K2BFQDAs8UKFgoP4paB4Kyw4Ky+4Kyn4Ky+FQDIs8UKFgoP4paB4Kyy4K2N4K2f4Ky+FQDQs8UKFgoP4paB4Ky44K2H4Kys4KyVFQDYs8UKGQoS4Kyu4K2B4Kyj4K2N4Kyh4Ky+FQDgs8UKGQoS4paB4KyV4Kyw4K2N4KyV4KyfFQDos8UKGQoS4paB4Kys4K2B4Kym4K2N4KynFQDws8UKHAoV4paB4KyF4Kyo4K2B4Kyt4K2C4KykFQD4s8UKHAoV4paB4Kya4Ky+4Kyu4K2N4Kyq4Ky/FQAAtMUKHAoV4paB4Kyq4K2L4Kyy4Ky/4Ky44KywFQAItMUKHAoV4paB4Ky44KyC4Kyq4Ky+4Kym4KyVFQAQtMUKHwoY4paB4Kyc4Kyj4Ky+4Ky24K2B4Kyj4Ky+FQAYtMUKHwoY4paB4Kyl4Ky+4KyG4Kyo4K2N4Kyk4Ky/FQAgtMUKIgob4paB4KyJ4Kyk4K2N4Ky44Ky+4Ky54Ky/4KykFQAotMUKIgob4paB4Kyk4K2N4Kyw4Ky/4Kyq4Ky+4Kyg4K2AFQAwtMUKIgob4paB4Ky44Kyo4K2N4Kyk4K2B4Ky34K2N4KyfFQA4tMUKKAoh4paB4Kyt4Ky/4Kyk4K2N4Kyk4Ky/4Kyt4K2C4Kyu4Ky/FQBAtMUKKwok4paB4KyF4Kyw4K2N4Kyl4Kyu4Kyo4K2N4Kyk4K2N4Kyw4K2AFQBItMUKEAoJ4K2N4K2x4K2HFQBQtMUKEwoM4Kyw4Ky+4Kyc4K2AFQBYtMUKEwoM4paB4Ky44Kyw4K2BFQBgtMUKDAoFYXRpb24VAGi0xQoWCg/iloHgrIbgrLjgrL/grLIVAHC0xQoWCg/iloHgrK3grYvgrJzgrKgVAHi0xQoWCg/iloHgrLjgrYfgrKXgrYEVAIC0xQoZChLiloHgrKbgrYHgrLDgrY3grJcVAIi0xQocChXiloHgrIXgrKvgrL/grLjgrLDgrYcVAJC0xQocChXiloHgrK7grLngrL7grLDgrKPgrL4VAJi0xQofChjiloHgrKzgrL7grLjgrYHgrKbgrYfgrKwVAKC0xQofChjiloHgrK7grYHgrLjgrLLgrK7grL7grKgVAKi0xQofChjiloHgrK/grL7grIngrKXgrL/grLLgrL4VALC0xQoiChviloHgrIXgrILgrLbgrJfgrY3grLDgrLngrKMVALi0xQolCh7iloHgrLjgrK7grY3grKzgrLDgrY3grKbgrY3grKcVAMC0xQooCiHiloHgrKrgrY3grLDgrKTgrY3grZ/grL7grLngrL7grLAVAMi0xQoJCgJDSBUA0LTFChAKCeCsn+CtjeCsuBUA2LTFChAKCeCss+CsvuCsqhUA4LTFChAKCeKWgeCsn+CtixUA6LTFChMKDOCsruCtgeCsluCtgBUA8LTFChMKDOKWgeCshuCssOCthxUA+LTFChMKDOKWgeCsleCsn+CsvhUAALXFChMKDOKWgeCsleCssOCshxUACLXFChMKDOKWgeCsrOCsv+CsmhUAELXFChYKD+KWgeCsheCstuCtjeCsrBUAGLXFChYKD+KWgeCsrOCsv+CsquCtgRUAILXFChYKD+KWgeCstuCsvuCsluCsvhUAKLXFChkKEuKWgeCsquCsj+Cso+CtjeCsnxUAMLXFChkKEuKWgeCsruCtgeCspOCtn+CsqBUAOLXFChkKEuKWgeCsuOCth+CsqOCth+CshxUAQLXFChwKFeKWgeCshuCsueCtjeCtseCsvuCsqBUASLXFChwKFeKWgeCsleCsruCsv+Csn+Csv+CssBUAULXFChwKFeKWgeCspeCsvuCsqOCtjeCspOCsvxUAWLXFChwKFeKWgeCsrOCsv+CsrOCssOCso+CtgBUAYLXFChwKFeKWgeCsruCsvuCspOCtjeCssOCthxUAaLXFChwKFeKWgeCssOCti+CsleCsv+CsrOCsvhUAcLXFChwKFeKWgeCsueCsnOCsvuCssOCssOCtgRUAeLXFCh8KGOKWgeCsqOCsv+CsheCsqOCtjeCspOCtgRUAgLXFCiUKHuKWgeCsrOCsv+CsnOCtjeCsnuCsquCtjeCspOCsvxUAiLXFCg0KBuCsqOCtjBUAkLXFCg0KBuCtgOCsoxUAmLXFCg0KBuCtn+CthxUAoLXFChAKCeCsq+CtgeCsshUAqLXFChAKCeCsr+CsvuCspBUAsLXFChAKCeCstuCsvuCsshUAuLXFChAKCeCsuOCsqOCssBUAwLXFChAKCeKWgeCsluCssBUAyLXFChAKCeKWgeCsreCtgxUA0LXFChMKDOCsmuCsqOCsvuCssBUA2LXFChMKDOKWgeCsl+CtjOCssBUA4LXFChMKDOKWgeCsuOCth+CsshUA6LXFChYKD+KWgeCsleCtjOCstuCssxUA8LXFChYKD+KWgeCsluCth+Css+CtgRUA+LXFChYKD+KWgeCsl+Csv+CssOCsvxUAALbFChYKD+KWgeCsmuCsouCsvuCsiRUACLbFChYKD+KWgeCspuCsvuCsrOCtgBUAELbFChYKD+KWgeCsquCtgeCst+CtjRUAGLbFChYKD+KWgeCssuCsnuCtjeCsmhUAILbFChkKEuCsruCsv+CsqOCsv+Csn+CtjRUAKLbFChkKEuKWgeCstuCtgeCspuCtjeCspxUAMLbFChwKFeKWgeCsruCsv+Css+Csv+CssuCsvhUAOLbFCiIKG+KWgeCsmuCsvuCssuCsv+CspeCsv+CssuCsvhUAQLbFCiIKG+KWgeCsr+CsvuCsueCsvuCsq+Css+CssOCthxUASLbFCiUKHuKWgeCsquCssOCtgOCsleCtjeCst+CsvuCssOCthxUAULbFCigKIeKWgeCsquCtjeCssOCspOCsv+Cst+CtjeCsoOCsv+CspBUAWLbFCgkKAm93FQBgtsUKCQoCdWwVAGi2xQoMCgXiloHCtxUAcLbFChAKCeCsr+CsvuCslRUAeLbFChAKCeKWgeCsruCsuBUAgLbFChMKDOCsl+Css+CtjeCsqhUAiLbFChMKDOKWgeCsquCssuCsvxUAkLbFChMKDOKWgeCsrOCsl+CsvxUAmLbFChMKDOKWgeCsruCsv+CsqBUAoLbFChYKD+CspuCtjeCtseCsvuCssBUAqLbFChYKD+CtjeCssOCsvuCss+CtnxUAsLbFChYKD+KWgeCsieCssuCtjeCsshUAuLbFChYKD+KWgeCsuOCsguCsuOCtjRUAwLbFChYKD+KWgeCtqOCtpuCtqOCtqRUAyLbFChkKEuKWgeCsquCtgeCsheCsleCtgRUA0LbFChkKEuKWgeCsrOCsvuCssOCsv+CslRUA2LbFChkKEuKWgeCsuOCtgOCsruCsv+CspBUA4LbFChwKFeKWgeCsieCspOCtjeCspOCssOCsvhUA6LbFChwKFeKWgeCsieCsreCtn+CsmeCtjeCslRUA8LbFChwKFeKWgeCssuCsl+CsvuCsh+CsrOCsvhUA+LbFCh8KGOKWgeCsheCsreCsv+CsruCtgeCsluCthxUAALfFCh8KGOKWgeCsquCtjeCssOCsrOCsvuCsuOCtgBUACLfFCh8KGOKWgeCsrOCsv+CsnOCth+CsoeCsv+CssBUAELfFCh8KGOKWgeCsuOCsruCtjeCsquCsqOCtjeCsqBUAGLfFCiIKG+KWgeCsleCsruCtjeCsquCsvuCsqOCtgOCssBUAILfFCi4KJ+KWgeCsleCssOCtjeCsruCsmuCsvuCssOCtgOCsruCsvuCsqOCthxUAKLfFCgkKAj8/FQAwt8UKCQoCdXQVADi3xQoNCgbgrIfgrJMVAEC3xQoLCgTiloEvFQBIt8UKCgoDZXNzFQBQt8UKEAoJ4Kyq4K2C4KywFQBYt8UKEAoJ4Ky+4Kyu4KykFQBgt8UKDAoF4paBU08VAGi3xQoQCgniloHgravgrawVAHC3xQoTCgzgrJngrY3grJXgrKgVAHi3xQoTCgziloHgrJjgrYfgrKgVAIC3xQoTCgziloHgrKzgrL7grIgVAIi3xQoWCg/grKvgrYHgrLLgrY3grLIVAJC3xQoWCg/grLLgrL/grJngrY3grJcVAJi3xQoWCg/iloHgrKTgrY3grLDgrYEVAKC3xQoWCg/iloHgrLjgrKjgrY3grKgVAKi3xQoWCg/iloHgrLjgrL7grKfgrKgVALC3xQoZChLiloHgrJXgrKPgrY3grJ/grL4VALi3xQoZChLiloHgrK3grL7grKzgrKjgrL4VAMC3xQoZChLiloHgrK3grL/grKTgrY3grKQVAMi3xQoZChLiloHgrK7grKfgrY3grZ/grK4VANC3xQoZChLiloHgrLDgrJbgrL/grKzgrYcVANi3xQocChXiloHgrLbgrYHgrKPgrL/grKzgrL4VAOC3xQoiChviloHgrKzgrL7grILgrLLgrL7grKbgrYfgrLYVAOi3xQoiChviloHgrLjgrLDgrY3grKzgrYvgrJrgrY3grJoVAPC3xQolCh7iloHgrJrgrLDgrY3grJrgrY3grJrgrL7grLDgrYcVAPi3xQooCiHiloHgrJXgrY3grLDgrL7grIfgrK7grKzgrY3grLDgrL4VAAC4xQorCiTiloHgrLbgrL/grJXgrY3grLfgrZ/grL/grKTgrY3grLDgrYAVAAi4xQoNCgbgrZ/grKQVABC4xQoQCgngrLjgrYfgrKgVABi4xQoQCgngrYvgrJXgrL8VACC4xQoQCgniloHgrKrgrIgVACi4xQoQCgniloHgrKrgrLYVADC4xQoTCgzgrK3grYLgrLfgrKMVADi4xQoTCgziloHgrK7grJXgrLAVAEC4xQoWCg/iloHgrIbgrKvgrY3grLAVAEi4xQoWCg/iloHgrJXgrLngrL/grKwVAFC4xQoWCg/iloHgrJXgrY3grLfgrKQVAFi4xQoWCg/iloHgrJXgrY3grLfgrYEVAGC4xQoWCg/iloHgrKjgrYHgrLngrYcVAGi4xQoWCg/iloHgrKzgrY3grLLgrL4VAHC4xQoWCg/iloHgrK7grL7grJfgrYEVAHi4xQoWCg/iloHgrLjgrKHgrLzgrJUVAIC4xQoZChLgrLjgrY3grKrgrLDgrY3grLYVAIi4xQoZChLiloHgrKzgrL7grJ/grLDgrYcVAJC4xQoZChLiloHgrKzgrYvgrIfgrLLgrYcVAJi4xQocChXiloHgrIngrKTgrY3grKrgrL7grKYVAKC4xQocChXiloHgrKrgrY3grLDgrK7grYvgrKYVAKi4xQofChjiloHgrJXgrY3grLDgrYDgrKHgrLzgrL4VALC4xQofChjiloHgrKrgrYvgrLLgrL/grLjgrJXgrYEVALi4xQofChjiloHgrKzgrL/grLbgrY3grLDgrL7grK4VAMC4xQolCh7iloHgrJfgrL7grKjgrY3grKfgrYDgrJngrY3grJUVAMi4xQoJCgIsIhUA0LjFCg0KBuCtqOCtqhUA2LjFChAKCeCsn+CssOCtgRUA4LjFChAKCeCsreCth+CsphUA6LjFChAKCeCsuOCssOCsoxUA8LjFChAKCeCsuOCsv+CsqBUA+LjFChAKCeKWgeCsuOCsnBUAALnFChAKCeKWgeCtq+CtqhUACLnFChMKDOCsquCssOCsv+CslRUAELnFChMKDOKWgeCsoeCsleCsvhUAGLnFChMKDOKWgeCsruCsv+CsshUAILnFChYKD+CsruCso+CtjeCsoeCssxUAKLnFChYKD+KWgeCsn+CsvuCsn+CsvhUAMLnFChYKD+KWgeCsp+Csv+CssOCthxUAOLnFChYKD+KWgeCsquCti+CssOCtjRUAQLnFChkKEuCsuOCsv+CsuOCsv+CshuCshxUASLnFChkKEuKWgeCsquCtgeCsqOCssOCtjRUAULnFChwKFeKWgeCsuOCsrOCtgeCspuCsv+CsqBUAWLnFChwKFeKWgeCsuOCsvuCsmeCtjeCsl+CthxUAYLnFCh8KGOKWgeCshuCso+Csv+CsrOCsvuCsleCtgRUAaLnFCh8KGOKWgeCsl+CtjeCssOCtgOCst+CtjeCsrhUAcLnFCh8KGOKWgeCspuCth+CsluCsqOCtjeCspOCtgRUAeLnFCigKIeKWgeCshuCsruCtjeCsrOCtgeCssuCsvuCsqOCtjeCsuBUAgLnFCg0KBuCsleCtgxUAiLnFChAKCeCsueCsvuCsoxUAkLnFCgwKBeKWgWJlFQCYucUKEAoJ4paB4Kyo4K2IFQCgucUKEwoM4paB4KyF4Kym4K2NFQCoucUKEwoM4paB4KyW4Kyw4Ky+FQCwucUKEwoM4paB4Kyn4Ky+4KyuFQC4ucUKEwoM4paB4Kys4Ky/4KyyFQDAucUKEwoM4paB4Ky44Ky/4KyVFQDIucUKFgoP4paB4Kya4Ky/4Ky54K2NFQDQucUKFgoP4paB4Kyf4K2H4KyV4K2NFQDYucUKFgoP4paB4Kym4K2L4Ky34K2AFQDgucUKFgoP4paB4Kyo4KyV4Kyy4Ky/FQDoucUKFgoP4paB4Kyq4K2N4Kyw4KysFQDwucUKGQoS4Kyn4K2N4K2f4KyV4K2N4Ky3FQD4ucUKGQoS4paB4Kya4Ky+4Kyo4K2N4KymFQAAusUKGQoS4paB4Kya4K2H4Kyk4Kyo4Ky+FQAIusUKGQoS4paB4Kys4K2D4Kyo4K2N4KymFQAQusUKHAoV4KyV4Ky+4Kyw4K2A4KyZ4K2N4KyVFQAYusUKHAoV4KyV4K2L4Kyw4K2N4Kyf4Kyw4K2HFQAgusUKHAoV4paB4Kyq4Kyw4Kyg4Ky+4Kyw4K2BFQAousUKHAoV4paB4Kyv4K2L4Kym4K2N4Kyn4Ky+FQAwusUKHwoY4paB4Kyq4Kyh4Ky/4Kyl4Ky/4Kyy4Ky+FQA4usUKHwoY4paB4Kyq4K2D4Kyl4Ky/4Kys4K2A4KywFQBAusUKHwoY4paB4Ky44Kyq4KyV4K2N4Ky34Kyw4K2HFQBIusUKCQoCaW0VAFC6xQoQCgngrJzgrYDgrKwVAFi6xQoQCgngrK7grLngrL4VAGC6xQoQCgngrLfgrK7grL4VAGi6xQoQCgniloHgrJ3grL8VAHC6xQoQCgniloHgravgra0VAHi6xQoLCgRDSElOFQCAusUKEwoM4Kyz4Ky/4Kys4Ky+FQCIusUKEwoM4Kyz4K2H4Ky34KyjFQCQusUKDQoG4paBU09BFQCYusUKEwoM4paB4KyF4KyW4Ky/FQCgusUKEwoM4paB4KyF4Kyt4K2fFQCousUKEwoM4paB4Kyd4Kyh4Ky8FQCwusUKEwoM4paB4Kym4K2B4KywFQC4usUKEwoM4paB4Kyu4Kyh4K2HFQDAusUKDgoH4paBUFBSQRUAyLrFChYKD+KWgeCsl+Cti+CsquCtgBUA0LrFChYKD+KWgeCsquCsn+Cth+CsshUA2LrFChYKD+KWgeCsrOCsvuCsm+CsvxUA4LrFChYKD+KWgeCsrOCsvuCspOCsvxUA6LrFChYKD+KWgeCsreCtn+CssOCthxUA8LrFChkKEuCsleCtjeCst+CtgOCssOCthxUA+LrFChkKEuKWgeCsrOCsqOCtjeCspuCtgBUAALvFChkKEuKWgeCsrOCsv+CstuCsvuCssxUACLvFChkKEuKWgeCsreCtjeCssOCsruCsoxUAELvFChkKEuKWgeCsuOCsv+CsqOCtjeCsphUAGLvFChwKFeKWgeCsmuCtgeCsleCtjeCspOCsvxUAILvFChwKFeKWgeCsn+CtjeCssOCsruCtjeCsqhUAKLvFChwKFeKWgeCsqOCsv+CshuCsr+Csv+CsrBUAMLvFChwKFeKWgeCsruCsueCsvuCsqOCspuCtgBUAOLvFChwKFeKWgeCssuCsvuCsl+CtgeCsm+CsvxUAQLvFChwKFeKWgeCsuOCsvuCsrOCsp+CsvuCsqBUASLvFCh8KGOKWgeCsheCsnuCtjeCsmuCss+CsleCtgRUAULvFCh8KGOKWgeCsl+CtjeCssOCsvuCsruCtjeCtnxUAWLvFCh8KGOKWgeCsruCtgeCsluCtjeCtn+CspOCsgxUAYLvFChIKC+KWgVBQUkFDSElOFQBou8UKIgob4paB4Kyq4Kyh4Ky84Ky/4Kyl4Ky/4Kys4Ky+FQBwu8UKKAoh4paB4Kys4Kyw4K2N4Kyk4K2N4Kyk4Kyu4Ky+4Kyo4KywFQB4u8UKKwok4paB4Kyu4Ky54Ky+4Kys4Ky/4Kym4K2N4K2f4Ky+4Kyz4K2fFQCAu8UKDQoG4KyP4KyHFQCIu8UKDQoG4Kys4K2fFQCQu8UKEAoJ4KyG4Kyq4KyjFQCYu8UKEAoJ4KyG4Kyu4K2HFQCgu8UKEAoJ4Kyt4Kyw4K2NFQCou8UKEAoJ4Ky+4Kyo4KyVFQCwu8UKEAoJ4Ky+4Kyo4KywFQC4u8UKEwoM4KyX4K2L4Kyf4Ky/FQDAu8UKEwoM4Kyy4K2B4KyV4Ky+FQDIu8UKEwoM4paB4Kys4Ky+4KyzFQDQu8UKEwoM4paB4Kys4Ky/4KyoFQDYu8UKEwoM4paB4Ky44K2M4KywFQDgu8UKFgoP4Kyf4K2N4Kyw4Ky/4KyVFQDou8UKFgoP4paB4KyG4KyH4Kyo4K2NFQDwu8UKFgoP4paB4KyV4Kyo4K2N4KymFQD4u8UKFgoP4paB4KyV4K2N4Kyy4Ky/FQAAvMUKFgoP4paB4Kyu4Kyw4K2N4KyuFQAIvMUKGQoS4paB4Kyu4Kyk4Ky+4Kyu4KykFQAQvMUKHAoV4Ky24Ky+4Ky44K2N4Kyk4K2N4KywFQAYvMUKHAoV4paB4Kys4Ky/4Kyq4KyV4K2N4Ky3FQAgvMUKIgob4paB4KyG4Kyj4Ky/4Kyb4Kyo4K2N4Kyk4Ky/FQAovMUKKwok4paB4KyV4Kyw4K2N4Kyu4KyV4Kyw4K2N4Kyk4K2N4Kyk4Ky+FQAwvMUKCwoEJ+ClpBUAOLzFChAKCeCspOCtgeCssBUAQLzFChAKCeCsruCsv+CssBUASLzFChMKDOCsruCtjeCssOCthxUAULzFChMKDOKWgeCsleCsn+CsvxUAWLzFChYKD+CsrOCssOCtjeCst+CssBUAYLzFChYKD+KWgeCsleCth+CsuOCtjRUAaLzFChYKD+KWgeCsrOCtn+CsuOCssBUAcLzFChYKD+KWgeCsuOCsvuCsruCsshUAeLzFChYKD+KWgeCsuOCtjeCspeCssxUAgLzFChkKEuKWgeCsheCsleCtjeCst+CssBUAiLzFChkKEuKWgeCsquCti+CsluCssOCtgBUAkLzFChwKFeKWgeCsieCsmuCtjeCsm+Cth+CsphUAmLzFChwKFeKWgeCsleCssOCtjeCsruCssOCthxUAoLzFChwKFeKWgeCsmuCtjeCtn+CsvuCssuCthxUAqLzFChwKFeKWgeCsrOCsv+CsreCsqOCtjeCsqBUAsLzFChwKFeKWgeCsuOCtgOCsruCsvuCssOCthxUAuLzFCh8KGOKWgeCshuCsquCso+CsruCsvuCsqOCthxUAwLzFCh8KGOKWgeCsr+CtgeCsrOCsleCsmeCtjeCslRUAyLzFCh8KGOKWgeCssOCsvuCsnOCtjeCtn+CsleCtgRUA0LzFCiUKHuKWgeCsleCsvuCssOCtjeCspOCtjeCspOCsv+CslRUA2LzFCiUKHuKWgeCspuCtgeCssOCtjeCsrOCtg+CspOCtjeCspBUA4LzFCjEKKuKWgeCsleCsvuCssOCtjeCsr+CtjeCtn+CsleCtjeCssOCsruCsleCtgRUA6LzFCg0KBuCtqOCtrRUA8LzFChAKCeCtjeCsrOCsvxUA+LzFChAKCeKWgeCsj+CsuRUAAL3FChAKCeKWgeCsueCsshUACL3FChMKDOCssOCsvuCssuCtjRUAEL3FChMKDOCsv+CsleCspOCsvhUAGL3FChMKDOKWgeCspOCtgeCssxUAIL3FChMKDOKWgeCsruCsueCtgRUAKL3FChYKD+KWgeCsheCsp+Csv+CsqBUAML3FChYKD+KWgeCsieCsquCsvuCsuBUAOL3FChYKD+KWgeCsleCtjeCssOCsrhUAQL3FChYKD+KWgeCsleCtjeCssuCsvhUASL3FChYKD+KWgeCsmuCsvuCsueCsvxUAUL3FChYKD+KWgeCsruCsvuCsueCsvhUAWL3FChkKEuKWgeCsleCsvuCso+CtjeCsoRUAYL3FChkKEuKWgeCsluCsvuCsh+CssuCthxUAaL3FChkKEuKWgeCsq+CsvuCst+CtjeCsnxUAcL3FChkKEuKWgeCssOCti+Cst+Cth+CshxUAeL3FChkKEuKWgeCsuOCtgeCssOCth+CsthUAgL3FChwKFeKWgeCsleCssOCsvuCsh+CsrOCsvhUAiL3FChwKFeKWgeCsquCtjeCssOCsmuCtgeCssBUAkL3FCh8KGOKWgeCsquCssOCtgOCsleCtjeCst+CsoxUAmL3FCigKIeKWgeCsieCsquCsqOCsv+CssOCtjeCsrOCsvuCsmuCsqBUAoL3FCg0KBuCtgOCsqhUAqL3FChAKCeKWgeCtquCtrxUAsL3FChMKDOKWgeCsk+CsreCssBUAuL3FChMKDOKWgeCsleCsvuCsshUAwL3FChMKDOKWgeCsleCth+CsnBUAyL3FChMKDOKWgeCsl+CsvuCspxUA0L3FChMKDOKWgeCspOCtiOCssxUA2L3FChMKDOKWgeCsrOCssuCtjRUA4L3FChMKDOKWgeCstuCsv+CslhUA6L3FChYKD+Csv+CspOCsvuCssOCthxUA8L3FChYKD+KWgeCsreCsvuCssuCtgRUA+L3FChYKD+KWgeCsuOCtgeCspuCtgxUAAL7FChkKEuKWgeCshuCsp+CtjeCtn+CsvhUACL7FChkKEuKWgeCsrOCsv+Csp+CsvuCtnxUAEL7FChkKEuKWgeCsrOCti+CssuCsv+CsghUAGL7FChkKEuKWgeCsreCsmeCtjeCsl+CsvhUAIL7FCh8KGOKWgeCsrOCsv+CsreCsvuCsl+CssOCthxUAKL7FCh8KGOKWgeCsueCsvuCsh+CsuOCtjeCsleCtgRUAML7FCiIKG+KWgeCssuCsvuCsl+Csv+CspeCsv+CssuCsvhUAOL7FCiIKG+KWgeCstuCtgeCsreCsvuCssOCsruCtjeCsrRUAQL7FCiUKHuKWgeCsleCssOCsv+CspeCsv+CsrOCsvuCssOCtgRUASL7FCigKIeKWgeCsrOCsv+CspuCtjeCtn+CsvuCss+Ctn+CssOCthxUAUL7FCi4KJ+KWgeCsleCth+CsqOCtjeCspuCtjeCssOCsvuCsquCsoeCsvOCsvhUAWL7FCjQKLeKWgeCsm+CsvuCspOCtjeCssOCsm+CsvuCspOCtjeCssOCtgOCsmeCtjeCslRUAYL7FCg0KBuCtg+CsthUAaL7FChAKCeKWgeCsmuCtiBUAcL7FChAKCeKWgeCtquCtrRUAeL7FChMKDOKWgeCsuOCtjeCssBUAgL7FChYKD+CsleCsvuCss+CssOCthxUAiL7FChYKD+KWgeCsheCsnOCtjeCsnhUAkL7FChYKD+KWgeCsoeCth+Csh+CsgRUAmL7FChYKD+KWgeCsuOCtgeCsqOCsvxUAoL7FChkKEuKWgeCsleCsruCsv+CstuCsqBUAqL7FChkKEuKWgeCsmuCsleCtjeCst+CtgRUAsL7FChkKEuKWgeCsreCtgeCsruCtjeCsrRUAuL7FChkKEuKWgeCstuCsmeCtjeCsleCsvhUAwL7FChkKEuKWgeCtseCsv+CsleCth+CsnxUAyL7FChwKFeKWgeCsh+CsguCssOCsvuCsnOCtgBUA0L7FChwKFeKWgeCsuOCsruCsvuCsquCtjeCspBUA2L7FCh8KGOCsluCth+CsruCtgeCso+CtjeCsoeCsvxUA4L7FCh8KGOKWgeCsheCspOCtjeCtn+Csp+Csv+CslRUA6L7FCh8KGOKWgeCspuCtgeCsqOCsv+CshuCssOCthxUA8L7FCh8KGOKWgeCsrOCsv+CsrOCsvuCspuCtgOCtnxUA+L7FCiUKHuKWgeCsquCtjeCssOCsv+Ctn+CsmeCtjeCsleCsvhUAAL/FCiUKHuKWgeCssOCsv+CsquCti+CssOCtjeCsn+CssOCthxUACL/FCjEKKuKWgeCsleCssOCtjeCsruCsleCtjeCst+Cth+CspOCtjeCssOCssOCthxUAEL/FChAKCeCtjeCtn+CssBUAGL/FChAKCeKWgeCspeCsshUAIL/FChMKDOCsleCsvuCssOCsvxUAKL/FChMKDOCstuCth+CsluCssBUAML/FChMKDOKWgeCshuCsreCsvxUAOL/FChMKDOKWgeCspuCsv+CsrBUAQL/FChYKD+CsrOCsv+Csp+CsvuCssBUASL/FChkKEuKWgeCsheCso+CtjeCsoeCsvhUAUL/FChkKEuKWgeCsj+CsoOCsvuCsleCtgRUAWL/FChkKEuKWgeCsleCsruCtjeCsrOCsvhUAYL/FChkKEuKWgeCsquCtgeCssOCtjeCsrBUAaL/FChkKEuKWgeCsrOCsv+CspuCsvuCtnxUAcL/FChwKFeKWgeCsmuCth+CsueCth+CssOCsvhUAeL/FChwKFeKWgeCsquCsvuCssOCsv+CssuCthxUAgL/FChwKFeKWgeCsrOCsqOCtjeCsp+CtgeCslRUAiL/FChwKFeKWgeCsruCtjOCsuOCtgeCsruCtgBUAkL/FCh8KGOCsvuCsqOCtjeCspOCtjeCssOCsv+CslRUAmL/FCh8KGOKWgeCsheCsnuCtjeCsmuCss+CssOCtgRUAoL/FCh8KGOKWgeCsqOCsv+CssOCsnuCtjeCsnOCsqBUAqL/FCh8KGOKWgeCstuCtjeCssOCtgOCsruCspOCtgBUAsL/FCiUKHuKWgeCsleCsvuCssOCtjeCsr+CtjeCtn+CsleCtgRUAuL/FCiUKHuKWgeCstuCtjeCssOCtgOCssuCsmeCtjeCsleCsvhUAwL/FCiUKHuKWgeCsuOCsguCsleCtjeCssOCsvuCsqOCtjeCspBUAyL/FChAKCeCspeCtgeCsqBUA0L/FChAKCeKWgeCsueCsiRUA2L/FChMKDOCtg+CsmeCtjeCslhUA4L/FChMKDOCtjeCtn+CssOCthxUA6L/FChMKDOKWgeCshuCsl+CthxUA8L/FChMKDOKWgeCsnOCsv+CskxUA+L/FChMKDOKWgeCsreCth+CsphUAAMDFChMKDOKWgeCsr+CtjOCsqBUACMDFChMKDOKWgeCssuCspOCsvhUAEMDFChYKD+CsleCsvuCssOCssOCthxUAGMDFChYKD+KWgeCshuCsh+CshuCshxUAIMDFChYKD+KWgeCsnOCtgOCsrOCsvhUAKMDFChYKD+KWgeCspuCtgOCsquCslRUAMMDFChYKD+KWgeCsreCsvuCsueCsvhUAOMDFChYKD+KWgeCsuOCtjeCsqOCsvhUAQMDFChkKEuKWgeCsl+CtgeCso+CtjeCsoRUASMDFChkKEuKWgeCsp+CsvuCsoeCsvOCsvxUAUMDFChkKEuKWgeCsrOCtgeCsneCsvuCshxUAWMDFChkKEuKWgeCsr+Cth+CsruCsv+CspBUAYMDFChkKEuKWgeCsuOCtgeCsp+CsvuCssBUAaMDFChwKFeKWgeCsheCsqOCtgeCsrOCsvuCsphUAcMDFChwKFeKWgeCshuCsq+Csl+CsvuCsqOCsvxUAeMDFChwKFeKWgeCshuCssOCti+Csl+CtjeCtnxUAgMDFChwKFeKWgeCsj+CsleCsvuCsp+Csv+CslRUAiMDFChwKFeKWgeCssOCsv+CssuCsv+CsnOCtjRUAkMDFChwKFeKWgeCstuCspOCsvuCsp+Csv+CslRUAmMDFCh8KGOKWgeCsr+CsvuCshuCsqOCtjeCspOCsvxUAoMDFCh8KGOKWgeCsuOCsguCsuOCtjeCsleCssOCsoxUAqMDFCh8KGOKWgeCsuOCspOCtjeCspOCtjeCsrOCthxUAsMDFCiIKG+KWgeCsruCsvuCsqOCsmeCtjeCsleCssOCthxUAuMDFCjEKKuKWgeCsleCtjeCssOCsvuCsh+CsruCsrOCtjeCssOCsvuCsnuCtjeCsmhUAwMDFCg0KBuCsueCssxUAyMDFChAKCeCsquCspeCssBUA0MDFChAKCeCsueCsv+CssBUA2MDFChAKCeCtguCsquCthxUA4MDFChMKDOCsqOCtjeCsp+CsvxUA6MDFChMKDOCssuCsquCtgeCssBUA8MDFChMKDOCssuCth+Csn+CtjRUA+MDFChMKDOKWgeCsnOCsn+CsvxUAAMHFChMKDOKWgeCsrOCsuOCtgRUACMHFChYKD+CsrOCsqOCtjeCspuCssBUAEMHFChYKD+KWgeCsheCsquCssOCsvxUAGMHFChYKD+KWgeCsueCsvuCssOCtjRUAIMHFChkKEuCsuOCsreCsvuCsquCspOCsvxUAKMHFChkKEuKWgeCsj+CsuOCtjeCsquCsvxUAMMHFChkKEuKWgeCsreCsvuCsnOCsquCsvhUAOMHFChkKEuKWgeCssOCsvuCsnOCsquCspRUAQMHFChwKFeKWgeCsleCtjeCtseCsvuCssOCthxUASMHFChwKFeKWgeCsl+CtgeCsquCtjeCspOCsvhUAUMHFChwKFeKWgeCsrOCtjeCssOCsv+Csn+CsvxUAWMHFCiIKG+KWgeCshuCstuCtgOCssOCtjeCsrOCsvuCsphUAYMHFCiIKG+KWgeCsreCtguCsruCsv+CsleCsvuCssOCthxUAaMHFCiIKG+KWgeCssuCsvuCsl+Csv+CspeCsv+CsrOCsvhUAcMHFCgkKAnRzFQB4wcUKDQoG4KyX4KyoFQCAwcUKDQoG4K2C4KykFQCIwcUKDAoF4paBMTkVAJDBxQoTCgzgrKjgrL7grK7grL4VAJjBxQoTCgzgrLDgrY3grK7grYEVAKDBxQoTCgziloHgrKzgrL7grKcVAKjBxQoWCg/iloHgrKDgrJXgrYfgrIcVALDBxQoWCg/iloHgrKzgrKLgrL7grIcVALjBxQoWCg/iloHgrLjgrYDgrKTgrL4VAMDBxQoZChLgrJzgrL/grLLgrY3grLLgrL4VAMjBxQoZChLgrK7grKfgrY3grZ/grLDgrYEVANDBxQoZChLiloHgrIXgrK7grL/grKTgrL4VANjBxQoZChLiloHgrIbgrJzgrY3grJ7grL4VAODBxQoZChLiloHgrIngrKrgrKbgrYfgrLYVAOjBxQoZChLiloHgrKbgrYHgrLngrL/grIEVAPDBxQoZChLiloHgrLbgrK/grY3grZ/grL4VAPjBxQoZChLiloHgrLjgrILgrJfgrYDgrKQVAADCxQocChXgrLLgrJXgrY3grLfgrY3grK7grYAVAAjCxQocChXiloHgrIngrKbgrY3grZ/grYvgrJcVABDCxQocChXiloHgrJXgrLLgrYfgrJzgrLDgrYcVABjCxQocChXiloHgrJXgrLfgrY3grJ/grJXgrLAVACDCxQocChXiloHgrK3grKPgrY3grKHgrL7grLAVACjCxQocChXiloHgrLjgrY3grKzgrL7grJfgrKQVADDCxQofChjiloHgrKrgrLDgrL/grKzgrYfgrLfgrKMVADjCxQofChjiloHgrK7grYPgrKTgrY3grZ/grYHgrLAVAEDCxQofChjiloHgrLbgrY3grLDgrYDgrLDgrL7grK4VAEjCxQoiChviloHgrK/grYHgrKzgrJXgrJngrY3grJXgrYEVAFDCxQolCh7iloHgrKrgrY3grLDgrKTgrL/grKzgrKjgrY3grKcVAFjCxQolCh7iloHgrLngrLjgrY3grKTgrL7grKjgrY3grKTgrLAVAGDCxQoQCgngrIXgrKrgrY0VAGjCxQoQCgngrJXgrL7grLIVAHDCxQoQCgniloHgrK7grLMVAHjCxQoQCgniloHgrLjgrKgVAIDCxQoTCgzgrJzgrL7grKPgrL8VAIjCxQoTCgzgrJ7grY3grJzgrLAVAJDCxQoTCgzgrKrgrL7grLDgrY0VAJjCxQoTCgzgrKzgrY3grLDgrL8VAKDCxQoTCgzgrK7grL7grKjgrLAVAKjCxQoTCgzgrLLgrL/grLDgrYcVALDCxQoTCgziloHgrJbgrL7grIkVALjCxQoTCgziloHgrKzgrYjgrKYVAMDCxQoTCgziloHgrKzgrYjgrLcVAMjCxQoTCgziloHgrLjgrYDgrKQVANDCxQoTCgziloHgrLjgrYHgrK0VANjCxQoWCg/grKrgrJXgrY3grLfgrYcVAODCxQoWCg/grKrgrYfgrJXgrY3grLcVAOjCxQoWCg/grK7grL7grKjgrY3grKQVAPDCxQoWCg/grL/grKjgrL/grZ/grLAVAPjCxQoWCg/iloHgrJ3grL/grIXgrLAVAADDxQoWCg/iloHgrJ/grYDgrJXgrL4VAAjDxQoWCg/iloHgrKrgrJXgrYfgrIcVABDDxQoWCg/iloHgrK7grYHgrKjgrL8VABjDxQoZChLgrKTgrL7grK7grYLgrLPgrJUVACDDxQoZChLiloHgrKbgrYLgrLDgrKTgrL4VACjDxQoZChLiloHgrKzgrYfgrJfgrLDgrYcVADDDxQoZChLiloHgrK7grL7grJngrY3grJUVADjDxQoZChLiloHgrK7grYfgrLngrYfgrLAVAEDDxQoZChLiloHgrLLgrL/grJngrY3grJUVAEjDxQocChXiloHgrLLgrJfgrL7grKTgrL7grLAVAFDDxQocChXiloHgrLbgrYHgrKPgrL/grLLgrYcVAFjDxQofChjgrJngrY3grJfgrL7grLLgrYHgrLDgrYEVAGDDxQofChjgrK7grYHgrK7grY3grKzgrL7grIfgrIMVAGjDxQofChjiloHgrJzgrL/grLLgrL7grKrgrL7grLMVAHDDxQofChjiloHgrKbgrYfgrJbgrL7grK/grL7grIkVAHjDxQofChjiloHgrK7grYvgrKzgrL7grIfgrLLgrY0VAIDDxQolCh7grKrgrY3grLDgrYfgrJXgrY3grLfgrYDgrLDgrYcVAIjDxQorCiTiloHgrLbgrY3grLDgrYDgrJzgrJfgrKjgrY3grKjgrL7grKUVAJDDxQoNCgbgrYvgrLMVAJjDxQoNCgbgrangrasVAKDDxQoQCgngrLLgrY3grJ8VAKjDxQoQCgniloHgrazgraoVALDDxQoTCgzgrKXgrYHgrLLgrL8VALjDxQoTCgzgrKjgrYfgrKTgrL4VAMDDxQoTCgzgrKrgrYvgrLDgrYcVAMjDxQoTCgzgrLLgrL/grZ/grKgVANDDxQoNCgbiloFjb24VANjDxQoTCgziloHgrJfgrYHgrLAVAODDxQoTCgziloHgrKTgrY3grLAVAOjDxQoWCg/grKTgrY3grZ/grL7grJcVAPDDxQoWCg/grK3grL7grKzgrLDgrYcVAPjDxQoWCg/grLDgrL7grJzgrL/grKQVAADExQoWCg/grLbgrYfgrLfgrLDgrYcVAAjExQoWCg/iloHgrKzgrYvgrLLgrLAVABDExQoZChLgrLjgrY3grKXgrL7grZ/grYAVABjExQoZChLiloHgrJrgrL/grKTgrY3grKQVACDExQoZChLiloHgrKrgrKHgrYHgrJvgrL8VACjExQoZChLiloHgrKrgrY3grLDgrK3grL4VADDExQoZChLiloHgrKzgrKjgrY3grKfgrL4VADjExQoZChLiloHgrK7grKfgrY3grZ/grL4VAEDExQocChXgrKrgrYLgrLDgrY3grKzgrLDgrYEVAEjExQocChXiloHgrKzgrY3grLDgrL/grJ/grYcVAFDExQocChXiloHgrLjgrK7grYPgrKbgrY3grKcVAFjExQocChXiloHgrLngrYPgrKbgrZ/grLDgrYcVAGDExQofChjiloHgrKzgrLjgrL/grKXgrL/grKzgrL4VAGjExQofChjiloHgrLjgrY3grKzgrL7grLDgrY3grKUVAHDExQoiChviloHgrLjgrYfgrJfgrYHgrKHgrLzgrL/grJUVAHjExQooCiHiloHgrKrgrY3grLDgrKTgrL/grLDgrJXgrY3grLfgrL4VAIDExQorCiTiloHgrJXgrLDgrL7grK/grL7grIfgrKrgrL7grLDgrL/grKwVAIjExQoJCgJvZBUAkMTFCgkKAm9wFQCYxMUKDQoG4K2M4KyVFQCgxMUKEAoJ4Kyr4Kyw4K2HFQCoxMUKEAoJ4paB4KyF4KyHFQCwxMUKEwoM4Kyq4K2N4Kyw4KymFQC4xMUKEwoM4Ky44Kyk4K2N4K2fFQDAxMUKEwoM4paB4KyG4Kyu4K2NFQDIxMUKEwoM4paB4Kyt4Kyw4K2NFQDQxMUKEwoM4paB4Ky24K2N4K2xFQDYxMUKFgoP4paB4Kyc4K2H4Kyw4Ky+FQDgxMUKFgoP4paB4Kym4K2N4K2x4Ky/FQDoxMUKFgoP4paB4Kyq4Kyl4Kyw4K2HFQDwxMUKFgoP4paB4Kyu4Ky/4Kyz4KyoFQD4xMUKFgoP4paB4Ky44K2N4Kyq4K2HFQAAxcUKGQoS4Kyq4K2H4KyV4K2N4Kyf4KywFQAIxcUKGQoS4paB4Kys4Kym4Kyz4Ky+4KyHFQAQxcUKHAoV4Kyv4Ky+4KyH4Kyq4Ky+4Kyw4K2HFQAYxcUKHAoV4paB4KyW4K2B4Ky44Ky/4Kyw4K2HFQAgxcUKHAoV4paB4KyW4K2L4Kyy4Ky/4Kys4Ky+FQAoxcUKHAoV4paB4Kya4K2M4Kyn4K2B4Kyw4K2AFQAwxcUKHAoV4paB4Kyq4Kyw4Ky/4Kyj4Ky+4KyuFQA4xcUKHAoV4paB4Kyu4Ky+4Kyk4K2N4Kyw4Ky+FQBAxcUKHwoY4paB4KyV4Ky+4Kyw4K2N4K2f4K2N4K2fFQBIxcUKHwoY4paB4Kyq4K2N4Kyw4Kyj4Ky+4Kyz4K2AFQBQxcUKIgob4paB4Kya4Ky/4Kyo4K2N4Kyk4Ky+4Kyw4K2HFQBYxcUKIgob4paB4Kyu4Ky/4Ky24K2N4Kyw4KyZ4K2N4KyVFQBgxcUKDQoG4KyT4K2NFQBoxcUKDQoG4K2o4K2sFQBwxcUKEAoJ4Ky44Kyw4K2BFQB4xcUKEwoM4Kyu4Ky/4Kyf4K2HFQCAxcUKEwoM4paB4KyX4Kym4Ky+FQCIxcUKEwoM4paB4Kya4Ky+4KyyFQCQxcUKEwoM4paB4Ky54Ky/4KywFQCYxcUKFgoP4KyV4K2N4Ky34K2N4KyuFQCgxcUKFgoP4Ky/4KyV4Ky+4Kyy4Ky/FQCoxcUKFgoP4paB4KyV4Kys4Ky+4KyfFQCwxcUKFgoP4paB4KyX4K2L4Kyw4K2BFQC4xcUKFgoP4paB4Kyo4KyV4K2N4Ky3FQDAxcUKFgoP4paB4Kyq4Ky/4Kyq4Ky/FQDIxcUKFgoP4paB4Kyr4KyV4K2A4KywFQDQxcUKGQoS4paB4KyJ4Kyq4KyV4Kyw4KyjFQDYxcUKGQoS4paB4KyJ4Kyq4KyV4K2D4KykFQDgxcUKGQoS4paB4KyV4Ky+4Kyo4K2N4KylFQDoxcUKGQoS4paB4Kyu4Ky/4Kyk4K2N4KywFQDwxcUKGQoS4paB4Kyw4KyW4Ky/4Kyy4K2HFQD4xcUKHAoV4paB4KyF4Kyo4K2B4Ky44Kyw4KyjFQAAxsUKHAoV4paB4KyJ4Kym4KyY4Ky+4Kyf4KyoFQAIxsUKHAoV4paB4Kys4Ky+4Ky44Kyt4Kys4KyoFQAQxsUKHAoV4paB4Kys4Ky+4Ky54Ky+4Kyw4K2BFQAYxsUKHAoV4paB4Kyy4KyY4K2B4Kya4Ky+4KyqFQAgxsUKHAoV4paB4Ky44Ky+4KyH4KyV4K2H4KyyFQAoxsUKHwoY4paB4KyF4Kyu4K2N4Kyz4Kyc4Ky+4KyoFQAwxsUKHwoY4paB4Kyo4K2H4KyH4Kyl4Ky/4Kyy4Ky+FQA4xsUKHwoY4paB4Kyq4Kyi4Ky84Kyo4K2N4Kyk4K2BFQBAxsUKHwoY4paB4Kyy4Ky+4KyX4Ky/4Kyl4Ky+4KyPFQBIxsUKIgob4paB4KyV4K2N4Kyw4Ky/4KyV4K2H4Kyf4KywFQBQxsUKJQoe4paB4Kyu4Kyo4K2N4Kyk4K2N4Kyw4Ky+4Kyz4K2fFQBYxsUKJQoe4paB4Ky44K2H4Kyk4K2H4Kys4K2H4Kyz4KyV4K2BFQBgxsUKLgon4paB4KyV4Ky+4Kyw4K2N4Kyv4K2N4K2f4Ky+4Kyz4K2f4Kyw4K2HFQBoxsUKEAoJ4K2H4Ky24Ky+FQBwxsUKEAoJ4K2N4Kyk4Ky+FQB4xsUKDAoF4paBcmUVAIDGxQoQCgniloHgrKfgrYgVAIjGxQoTCgzgrJfgrL7grLDgrYcVAJDGxQoTCgziloHgrKfgrKjgrYAVAJjGxQoWCg/grYvgrKTgrY3grKTgrK4VAKDGxQoWCg/iloHgrJfgrLDgrY3grKwVAKjGxQoWCg/iloHgrKTgrLngrL/grIEVALDGxQoWCg/iloHgrK3grL7grLfgrKMVALjGxQoWCg/iloHgrK7grYfgrLjgrYcVAMDGxQoWCg/iloHgrLDgrYvgrK7grL4VAMjGxQoZChLiloHgrJXgrY3grLDgrK7grL4VANDGxQoZChLiloHgrKzgrL/grKrgrYHgrLMVANjGxQoZChLiloHgrK7grL7grJfgrKPgrL4VAODGxQocChXgrJXgrY3grLfgrL/grKrgrY3grKQVAOjGxQocChXiloHgrIngrKrgrL7grKbgrL7grKgVAPDGxQocChXiloHgrJzgrYDgrKzgrKjgrY3grKQVAPjGxQocChXiloHgrK/grYvgrJzgrKjgrL7grLAVAADHxQocChXiloHgrLjgrJngrY3grJXgrYfgrKQVAAjHxQofChjiloHgrIXgrKjgrYHgrK7grYvgrKbgrKgVABDHxQoiChviloHgrKzgrL/grLjgrL/grLjgrL/grIbgrIcVABjHxQoQCgngrKzgrILgrLYVACDHxQoQCgngrL7grKzgrKgVACjHxQoTCgzgrKjgrY3grKbgrL8VADDHxQoTCgzgrK7grY3grKzgrYcVADjHxQoTCgzgrLPgrL/grLDgrYcVAEDHxQoTCgziloHgrKjgrL/grLYVAEjHxQoTCgziloHgrKvgrLjgrL8VAFDHxQoWCg/iloHgrIbgrIfgrLjgrL8VAFjHxQoWCg/iloHgrIngrJzgrY3grJwVAGDHxQoWCg/iloHgrJfgrJvgrLDgrYcVAGjHxQoWCg/iloHgrKzgrLDgrY3grKQVAHDHxQocChXgrLjgrL7grLngrL/grKTgrY3grZ8VAHjHxQocChXiloHgrIXgrKzgrLngrYfgrLPgrL4VAIDHxQocChXiloHgrIXgrLDgrY3grJrgrY3grJoVAIjHxQocChXiloHgrJXgrYfgrKTgrYfgrJzgrKMVAJDHxQocChXiloHgrJfgrJ7grY3grJzgrYfgrIcVAJjHxQocChXiloHgrKbgrYHgrLDgrY3grJfgrL4VAKDHxQocChXiloHgrKzgrL/grK3grJXgrY3grKQVAKjHxQocChXiloHgrLjgrK7grL7grJzgrLjgrYcVALDHxQofChjiloHgrIXgrJfgrY3grKjgrL/grJXgrL4VALjHxQoiChviloHgrKjgrYfgrKTgrL7grK7grL7grKjgrYcVAMDHxQolCh7iloHgrK7grYfgrKHgrL/grJXgrL7grLLgrLDgrYcVAMjHxQoJCgJvdBUA0MfFCg0KBuCsruCsuRUA2MfFCg0KBuCtgeCsgRUA4MfFChAKCeCsp+CsqOCsvhUA6MfFChAKCeCsrOCsoeCsvBUA8MfFChAKCeKWgeCsl+CtjRUA+MfFChAKCeKWgeCsqOCtnxUAAMjFChAKCeKWgeCtq+CtpxUACMjFChMKDOCssOCsvuCso+CtgBUAEMjFChMKDOCtguCssOCtjeCsrBUAGMjFChMKDOKWgeCsruCsqOCsvxUAIMjFChYKD+Cst+CtjeCsn+CsvuCssBUAKMjFChYKD+KWgeCsj+CsuOCsv+CshhUAMMjFChYKD+KWgeCsmuCsvuCss+CslRUAOMjFChYKD+KWgeCsuOCth+CssuCtjRUAQMjFChkKEuKWgeCsleCtguCss+CssOCthxUASMjFChkKEuKWgeCsmuCspOCtgeCssOCtjRUAUMjFChkKEuKWgeCsquCssOCsv+CsrOCsvhUAWMjFChkKEuKWgeCstuCti+Csh+CsrOCsvhUAYMjFChkKEuKWgeCsuOCtiOCsqOCsv+CslRUAaMjFChkKEuKWgeCsueCti+Csh+Csl+CsshUAcMjFChwKFeKWgeCssuCsleCtjeCst+CssOCtgRUAeMjFChwKFeKWgeCsuOCsqOCtjeCsp+CsvuCsqBUAgMjFChwKFeKWgeCsuOCtjeCsleCtgeCssuCssBUAiMjFChwKFeKWgeCsueCti+Csh+CsquCsoeCsvxUAkMjFCh8KGOCsr+CsvuCsh+Csm+CsqOCtjeCspOCsvxUAmMjFCh8KGOKWgeCsk+CsoeCsv+CstuCsvuCssOCthxUAoMjFCisKJOKWgeCshuCsq+Csl+CsvuCsqOCsv+CsuOCtjeCspOCsvuCsqBUAqMjFCg0KBuCsn+CtgBUAsMjFChAKCeCsieCsgeCsthUAuMjFChAKCeCssOCsvuCsoxUAwMjFChAKCeCst+Cth+CspxUAyMjFCgwKBeKWgSk6FQDQyMUKEwoM4Kys4Ky+4Ky54K2AFQDYyMUKEwoM4paB4KyG4Kym4KywFQDgyMUKEwoM4paB4Kyl4Ky/4KyyFQDoyMUKEwoM4paB4Kyn4K2N4KysFQDwyMUKFgoP4Ky+4Kyu4Ky/4Kyo4K2NFQD4yMUKFgoP4paB4Kys4K2L4Kyu4Ky+FQAAycUKFgoP4paB4Ky44K2H4Kyu4Ky/FQAIycUKGQoS4paB4KyP4Kyj4Ky/4KyV4Ky/FQAQycUKGQoS4paB4Kyq4K2B4Kyw4Ky+4KyjFQAYycUKHAoV4paB4KyV4Kys4Ky/4Kyk4Ky+4KywFQAgycUKHAoV4paB4KyV4K2N4Kyw4Ky/4K2f4Ky+FQAoycUKHAoV4paB4KyX4K2N4Kyw4Ky+4Kyu4Ky+FQAwycUKHAoV4paB4Kys4K2N4Kyy4KyV4Kyw4K2HFQA4ycUKHAoV4paB4Ky44K2N4Kyk4Kyu4K2N4KytFQBAycUKHwoY4paB4Kyq4Kyh4Ky84Ky/4Kyl4Ky+4KyPFQBIycUKHwoY4paB4Kyq4K2N4Kyw4KyV4Ky+4Kyw4K2HFQBQycUKHwoY4paB4Kyv4Kyo4K2N4Kyk4K2N4Kyw4K2AFQBYycUKCgoDaWxsFQBgycUKEAoJ4Ky+4K2f4K2BFQBoycUKEAoJ4paB4Kyg4K2BFQBwycUKEwoM4Kys4Ky44K2N4KykFQB4ycUKEwoM4Ky24K2B4Kyj4Ky/FQCAycUKEwoM4Ky44K2H4Kya4KyoFQCIycUKEwoM4paB4Kya4Ky+4KywFQCQycUKEwoM4paB4Kyk4K2L4KywFQCYycUKFgoP4Kyk4Ky+4Kyw4Ky/4KyWFQCgycUKFgoP4paB4KyJ4Kyg4Ky/4KyyFQCoycUKFgoP4paB4KyV4Kyw4K2B4KyjFQCwycUKFgoP4paB4Kyc4KyX4Kyk4KywFQC4ycUKFgoP4paB4Kys4Ky/4Ky34Ky+FQDAycUKGQoS4K2N4K2f4Ky+4Kyo4K2N4Ky4FQDIycUKGQoS4paB4KyJ4Kyq4KyV4Ky+4KywFQDQycUKGQoS4paB4Kyb4Ky/4Kyh4Ky84Ky+FQDYycUKGQoS4paB4Kyc4Kyo4K2N4Kyk4K2BFQDgycUKGQoS4paB4Kyl4Ky/4Kys4Ky+4KywFQDoycUKGQoS4paB4Kyq4Ky+4Ky24K2N4KyaFQDwycUKGQoS4paB4Ky24Kyk4Kyq4Kyl4K2AFQD4ycUKGQoS4paB4Ky44K2H4KyH4Kyg4Ky/FQAAysUKHAoV4paB4Ky44K2N4K2x4Kyw4K2C4KyqFQAIysUKHwoY4paB4KyV4K2L4Kyj4Ky+4Kyw4K2N4KyVFQAQysUKHwoY4paB4Kyr4K2H4Kys4K2D4KyG4Kyw4K2AFQAYysUKHwoY4paB4K2f4K2B4KyV4K2N4Kyw4K2H4KyoFQAgysUKIgob4paB4KyP4Kyu4Ky+4Kyo4KyZ4K2N4KyV4K2BFQAoysUKIgob4paB4Kyq4K2N4Kyw4Kyr4K2B4Kyy4K2N4KyyFQAwysUKCQoCZXAVADjKxQoNCgbgrKXgrYcVAEDKxQoNCgbgrLLgrK4VAEjKxQoQCgngrI/grLjgrL8VAFDKxQoQCgngrKzgrLPgrYAVAFjKxQoQCgniloHgrIngrLIVAGDKxQoQCgniloHgravgra8VAGjKxQoTCgzgrKTgrKjgrY3grZ8VAHDKxQoTCgzgrKfgrL7grKjgrL4VAHjKxQoTCgzgrK7grL7grKjgrYEVAIDKxQoTCgziloHgrIXgrLjgrIIVAIjKxQoWCg/grKTgrY3grZ/grL7grLAVAJDKxQoWCg/grK7grYHgrLngrL/grIEVAJjKxQoWCg/iloHgrJjgrYfgrLDgrL8VAKDKxQoWCg/iloHgrK7grL7grZ/grL4VAKjKxQoWCg/iloHgrK7grYPgrKTgrJUVALDKxQoWCg/iloHgrK7grZ/grYHgrLAVALjKxQoWCg/iloHgrLbgrL7grLbgrYEVAMDKxQoWCg/iloHgrLbgrYHgrJXgrY0VAMjKxQoZChLiloHgrJXgrYHgrKPgrY3grKEVANDKxQoZChLiloHgrJzgrL/grLLgrL7grLAVANjKxQoZChLiloHgrJ/grY3grLDgrJXgrY0VAODKxQoZChLiloHgrKbgrL/grKjgrLDgrYEVAOjKxQoZChLiloHgrKrgrLDgrY3grKzgrKQVAPDKxQoZChLiloHgrLDgrLngrYHgrJvgrL8VAPjKxQoZChLiloHgrLjgrK7grY3grKzgrLMVAADLxQoZChLiloHgrLjgrL/grLDgrL/grJwVAAjLxQocChXiloHgrIXgrKrgrKHgrYfgrJ/grY0VABDLxQocChXiloHgrKTgrY3grLDgrYHgrJ/grL8VABjLxQocChXiloHgrKrgrY3grLDgrK3grYPgrKQVACDLxQocChXiloHgrKzgrL/grJzgrY3grJ7grL4VACjLxQocChXiloHgrK7grLngrL7grKjgrY3grKQVADDLxQocChXiloHgrLDgrYfgrLPgrKzgrL7grIcVADjLxQofChjiloHgrKzgrJXgrY3grKTgrKzgrY3grZ8VAEDLxQofChjiloHgrKzgrL/grKbgrY3grLDgrYvgrLkVAEjLxQoiChviloHgrK7grKfgrY3grZ/grL7grLngrY3grKgVAFDLxQoiChviloHgrK7grYHgrJbgrKrgrL7grKTgrY3grLAVAFjLxQolCh7iloHgrKzgrY3grZ/grKzgrJrgrY3grJvgrYfgrKYVAGDLxQolCh7iloHgrLjgrL7grLngrL/grKTgrY3grZ/grL/grJUVAGjLxQoQCgngrJzgrYfgrKgVAHDLxQoQCgngrLDgrYDgrKMVAHjLxQoQCgniloHgrLfgrKEVAIDLxQoQCgniloHgravgrakVAIjLxQoTCgzgrKrgrYLgrLDgrKMVAJDLxQoTCgzgrK3grL/grJzgrKgVAJjLxQoTCgziloHgrJbgrL7grLAVAKDLxQoTCgziloHgrJzgrL7grLIVAKjLxQoTCgziloHgrKjgrYfgrIkVALDLxQoTCgziloHgrK3grYLgrKQVALjLxQoTCgziloHgrK7grYHgrLAVAMDLxQoTCgziloHgrLjgrKzgrY0VAMjLxQoWCg/grKTgrY3grK7grL/grJUVANDLxQoWCg/grKjgrY3grKbgrY3grbEVANjLxQoWCg/iloHgrKrgrL/grIbgrJwVAODLxQoWCg/iloHgrKzgrKjgrY3grZ8VAOjLxQoWCg/iloHgrK3grL/grKHgrLwVAPDLxQoZChLgrLbgrLjgrY3grKTgrY3grLAVAPjLxQoZChLiloHgrKzgrL/grKrgrY3grLMVAADMxQoZChLiloHgrLbgrL/grKzgrL/grLAVAAjMxQoZChLiloHgrLjgrL7grKfgrKjgrL4VABDMxQoZChLiloHgrLngrYvgrJ/grYfgrLIVABjMxQocChXiloHgrKjgrL/grJXgrJ/grLDgrYEVACDMxQofChjiloHgrLLgrJXgrY3grLfgrY3grK7grKMVACjMxQofChjiloHgrLfgrY3grJ/grL7grKHgrL/grZ8VADDMxQoiChviloHgrJXgrLDgrL7grIfgrKXgrL/grLLgrYcVADjMxQooCiHiloHgrKrgrLngrJ7grY3grJrgrL/grKXgrL/grLLgrYcVAEDMxQoQCgngrKbgrL7grKwVAEjMxQoQCgngrLbgrL7grKgVAFDMxQoQCgngrLfgrY3grJUVAFjMxQoQCgngrYDgrLbgrYEVAGDMxQoQCgniloHgrJXgrYAVAGjMxQoLCgRtZW50FQBwzMUKEwoM4Ky34Ky+4Kyw4K2HFQB4zMUKEwoM4Ky44K2N4Kys4K2AFQCAzMUKEwoM4paB4KyV4K2N4KyzFQCIzMUKEwoM4paB4KyX4Ky+4KyoFQCQzMUKEwoM4paB4Kys4Ky+4KycFQCYzMUKFgoP4Ky44Ky/4KyG4Kyy4K2NFQCgzMUKFgoP4Ky44K2H4Kyo4K2N4Ky4FQCozMUKFgoP4paB4KyG4KyJ4Kyf4K2NFQCwzMUKFgoP4paB4KyV4K2B4Kyo4Ky/FQC4zMUKFgoP4paB4Kyu4Ky+4Kyd4K2AFQDAzMUKFgoP4paB4Kyw4Ky+4KyX4Ky/FQDIzMUKFgoP4paB4Ky24Ky+4Ky54Ky+FQDQzMUKFgoP4paB4K2o4K2m4K2n4K2sFQDYzMUKGQoS4KyV4K2H4Kyo4K2N4K2f4K2BFQDgzMUKGQoS4paB4KyV4K2N4Kyw4K2L4KynFQDozMUKGQoS4paB4KyX4Kys4K2H4Ky34KyVFQDwzMUKGQoS4paB4Kyf4K2N4Kyw4Ky+4KyrFQD4zMUKGQoS4paB4Kym4K2H4KyW4Ky/4KyyFQAAzcUKGQoS4paB4Kyq4Ky/4Kyk4Ky+4KyuFQAIzcUKGQoS4paB4Kyu4Ky+4Kyh4Ky84Ky/FQAQzcUKGQoS4paB4Ky44Ky+4Kyw4K2N4KylFQAYzcUKHAoV4paB4KyF4Kys4KyV4Ky+4Kyw4K2AFQAgzcUKHAoV4paB4Kyc4K2f4Kyo4K2N4Kyk4K2AFQAozcUKIgob4paB4Ky44K2B4Kys4Kyw4K2N4Kyj4K2N4KyjFQAwzcUKKAoh4paB4KyH4Kyj4K2N4Kyh4Ky34K2N4Kyf4K2N4Kyw4Ky/FQA4zcUKKAoh4paB4Kys4Ky/4Kyu4Ky+4Kyo4Kys4Kyo4K2N4Kym4KywFQBAzcUKDQoG4KyP4KyaFQBIzcUKEAoJ4Kyn4Ky+4KyuFQBQzcUKEAoJ4paB4KyF4KygFQBYzcUKEAoJ4paB4Kyh4KyVFQBgzcUKEAoJ4paB4Kym4KyuFQBozcUKEAoJ4paB4Kyy4K2AFQBwzcUKEwoM4KyV4K2N4Kyw4K2LFQB4zcUKEwoM4Ky44K2N4Kys4KywFQCAzcUKEwoM4paB4KyF4Kys4KywFQCIzcUKEwoM4paB4KyF4Ky24K2BFQCQzcUKEwoM4paB4KyH4Kyo4Ky/FQCYzcUKEwoM4paB4Kyc4KyX4Ky/FQCgzcUKEwoM4paB4Kyw4Ky/4KycFQCozcUKFgoP4KyV4Ky/4Ky24K2L4KywFQCwzcUKFgoP4paB4KyG4KyX4K2B4KyGFQC4zcUKFgoP4paB4KyX4K2D4Ky54Ky/FQDAzcUKFgoP4paB4Kyo4Ky/4KyX4KyuFQDIzcUKFgoP4paB4Kyw4Kya4Ky/4KykFQDQzcUKFgoP4paB4Kyw4K2L4KyX4KywFQDYzcUKFgoP4paB4Ky54K2L4KyH4KyoFQDgzcUKGQoS4Kyy4KyV4K2N4Ky34Ky/4KykFQDozcUKGQoS4paB4KyP4KyV4Ky+4Kyh4K2HFQDwzcUKGQoS4paB4KyV4Ky54Ky/4Kys4K2HFQD4zcUKGQoS4paB4KyV4Ky+4Kyz4Ky/4KyGFQAAzsUKGQoS4paB4Kyc4Ky+4KyX4K2N4KywFQAIzsUKGQoS4paB4Kyh4Ky+4KyV4K2N4KykFQAQzsUKGQoS4paB4Kyh4Ky/4Kyc4K2H4KyyFQAYzsUKHAoV4Kym4K2N4Kyn4Ky+4Kyw4Ky/4KykFQAgzsUKHAoV4paB4Kyq4Kyh4Ky/4Kyl4Ky+4KyPFQAozsUKHAoV4paB4Kys4K2N4K2f4Ky+4Kyf4K2NFQAwzsUKHAoV4paB4Kyv4K2H4Ky54K2H4Kyk4K2BFQA4zsUKHwoY4paB4Kyw4K2B4Ky54Kyo4K2N4Kyk4K2BFQBAzsUKIgob4paB4Kyk4Ky+4Ky54Ky+4Ky54K2H4Kyy4K2HFQBIzsUKIgob4paB4Kys4Kyi4Ky84Ky/4Kys4Ky+4Kyw4K2HFQBQzsUKIgob4paB4Kyt4KyX4Kys4Ky+4Kyo4KyZ4K2N4KyVFQBYzsUKIgob4paB4Kyu4K2N4K2f4Ky+4Kya4K2N4Kyw4K2HFQBgzsUKIgob4paB4Kyw4Ky54K2B4Kyb4Kyo4K2N4Kyk4Ky/FQBozsUKDQoG4KyV4KyuFQBwzsUKEAoJ4Kyc4Ky/4KyCFQB4zsUKEAoJ4paB4Kys4KyCFQCAzsUKEwoM4Ky54K2H4Kys4Ky+FQCIzsUKEwoM4paB4KyG4Ky44K2LFQCQzsUKEwoM4paB4KyP4Kyy4Ky/FQCYzsUKEwoM4paB4KyV4Kyu4Ky+FQCgzsUKEwoM4paB4Kyb4Kys4Ky/FQCozsUKEwoM4paB4Kyc4K2B4KyoFQCwzsUKEwoM4paB4Kyr4K2N4KyyFQC4zsUKEwoM4paB4Ky24Kyw4KyjFQDAzsUKFgoP4Kyo4KyZ4K2N4KyV4K2BFQDIzsUKFgoP4Kyo4Ky+4KyZ4K2N4KyVFQDQzsUKFgoP4Ky/4Kys4Ky+4KyV4K2BFQDYzsUKFgoP4paB4KyF4Ky44K2B4KywFQDgzsUKFgoP4paB4Kyq4K2N4Kyw4Ky5FQDozsUKFgoP4paB4Kyu4Kyn4K2N4KyvFQDwzsUKFgoP4paB4Ky44K2N4Kyl4KyXFQD4zsUKGQoS4paB4Kyu4Ky44K2N4Kyk4Ky/FQAAz8UKHAoV4paB4Kyq4K2B4Kyw4Ky+4Kyk4KyoFQAIz8UKHAoV4paB4Kys4Ky/4KyV4K2N4Kyw4KyuFQAQz8UKHAoV4paB4Ky24Ky/4Kyz4K2N4Kyq4K2AFQAYz8UKHwoY4paB4KyP4Kyc4K2H4Kyo4K2N4Ky44Ky/FQAgz8UKHwoY4paB4KyV4K2N4Kyw4Kyu4Ky+4KyX4KykFQAoz8UKHwoY4paB4KyX4Kyw4K2N4Kyt4Kys4Kyk4K2AFQAwz8UKHwoY4paB4Kyc4Kyo4K2N4Kyu4Kym4Ky/4KyoFQA4z8UKHwoY4paB4Ky44Ky+4Kyo4Ky/4Kyf4Ky+4KyHFQBAz8UKCQoCLCgVAEjPxQoQCgngrafgrLDgrYcVAFDPxQoTCgzgrJfgrYHgrKHgrLwVAFjPxQoTCgziloHgrKjgrYDgrKQVAGDPxQoTCgziloHgrKzgrYfgrKoVAGjPxQoTCgziloHgrangrabgraYVAHDPxQoWCg/iloHgrIXgrLbgrY3grbEVAHjPxQoWCg/iloHgrJzgrYfgrLLgrY0VAIDPxQoWCg/iloHgrKrgrL7grJfgrLMVAIjPxQoWCg/iloHgrKzgrYvgrLngrL8VAJDPxQoWCg/iloHgrLjgrY3grbHgrLAVAJjPxQoWCg/iloHgrLngrKDgrL7grKQVAKDPxQoZChLgrKPgrY3grKHgrL7grLDgrYcVAKjPxQoZChLgrKbgrYfgrKzgrL7grJXgrYEVALDPxQoZChLiloHgrIbgrKrgrKPgrL7grLAVALjPxQoZChLiloHgrJXgrLDgrL/grJvgrYEVAMDPxQoZChLiloHgrKrgrY3grLLgrL7grKgVAMjPxQoZChLiloHgrK3grKzgrL7grKjgrYAVANDPxQocChXiloHgrIbgrLjgrKjgrY3grKTgrL8VANjPxQocChXiloHgrJXgrL7grLngrL7grLDgrL8VAODPxQocChXiloHgrJfgrYHgrJzgrLDgrL7grJ8VAOjPxQocChXiloHgrKjgrL/grKjgrY3grKbgrL4VAPDPxQocChXiloHgrLjgrILgrKfgrY3grZ/grL4VAPjPxQofChjiloHgrJXgrL7grLDgrJbgrL7grKjgrL4VAADQxQofChjiloHgrKrgrL7grLDgrY3grJ/grL/grLAVAAjQxQofChjiloHgrKzgrYjgrKbgrYfgrLbgrL/grJUVABDQxQoiChviloHgrK7grY3grZ/grL7grKjgrYfgrJzgrLAVABjQxQoiChviloHgrLbgrYHgrK3grYfgrJrgrY3grJvgrL4VACDQxQoiChviloHgrLjgrK7grLjgrY3grZ/grL7grJXgrYEVACjQxQolCh7iloHgrKzgrL/grKrgrLDgrY3grK/grY3grZ/grZ8VADDQxQolCh7iloHgrbHgrYfgrKzgrLjgrL7grIfgrJ/grLDgrYcVADjQxQoJCgJzZRUAQNDFCgsKBOKWgTkVAEjQxQoQCgngrLLgrYvgrJcVAFDQxQoQCgniloHgrKHgrYEVAFjQxQoQCgniloHgrKrgrYgVAGDQxQoTCgzgrL7grK7grKPgrL8VAGjQxQoTCgziloHgrJrgrLDgrK4VAHDQxQoTCgziloHgrJzgrKzgrL4VAHjQxQoTCgziloHgrKrgrYLgrLAVAIDQxQoTCgziloHgrK/grL7grJUVAIjQxQoWCg/iloHgrJXgrL7grLPgrL8VAJDQxQoWCg/iloHgrJvgrL7grKHgrLwVAJjQxQoWCg/iloHgrKbgrL7grLDgrL8VAKDQxQoWCg/iloHgrLLgrYHgrJrgrL8VAKjQxQoWCg/iloHgrLbgrJngrY3grJYVALDQxQoZChLiloHgrIXgrJngrY3grJfgrKgVALjQxQoZChLiloHgrIXgrKbgrL7grLLgrKQVAMDQxQoZChLiloHgrJfgrKzgrYfgrLfgrKMVAMjQxQoZChLiloHgrK3grKTgrY3grKTgrL4VANDQxQoZChLiloHgrLjgrYHgrJ/grL/grIIVANjQxQocChXiloHgrLjgrILgrJXgrLPgrY3grKoVAODQxQofChjgrK/grL7grKTgrY3grLDgrL7grLDgrYcVAOjQxQofChjiloHgrLbgrL/grJXgrY3grLfgrL7grLAVAPDQxQoiChviloHgrIXgrLjgrYHgrKzgrL/grKfgrL7grLAVAPjQxQoiChviloHgrKzgrY3grZ/grJXgrY3grKTgrL/grLAVAADRxQoiChviloHgrLbgrKTgrL7grKzgrY3grKbgrYDgrLAVAAjRxQolCh7iloHgrIXgrK3grL/grKzgrYPgrKbgrY3grKfgrL8VABDRxQolCh7iloHgrKbgrYfgrJbgrL7grKbgrYfgrIfgrJvgrL8VABjRxQoxCiriloHgrLbgrL/grJXgrY3grLfgrL7grKjgrYHgrLfgrY3grKDgrL7grKgVACDRxQoQCgngrK7grL7grIcVACjRxQoQCgniloHgrKXgrL8VADDRxQoTCgzgrJ7grY3grJzgrZ8VADjRxQoTCgzgrKbgrYfgrLLgrL4VAEDRxQoTCgzgrLjgrY3grKTgrJUVAEjRxQoTCgziloHgrJfgrYHgrIcVAFDRxQoTCgziloHgrJrgrLDgrY0VAFjRxQoWCg/iloHgrK7grYfgrKHgrLwVAGDRxQoWCg/iloHgrK7grYfgrLPgrL4VAGjRxQoWCg/iloHgrLjgrKDgrL/grJUVAHDRxQoWCg/iloHgrZ/grYHgrKrgrL8VAHjRxQoZChLiloHgrKTgrL7grLLgrL/grK4VAIDRxQoZChLiloHgrKbgrL/grKjgrL/grIYVAIjRxQoZChLiloHgrKzgrYHgrJ3grL7grK4VAJDRxQoZChLiloHgrLjgrILgrJfgrY3grLAVAJjRxQoZChLiloHgrLjgrYzgrK7grY3grZ8VAKDRxQocChXiloHgrKrgrYPgrLfgrY3grKDgrL4VAKjRxQocChXiloHgrLbgrLDgrYDgrLDgrJXgrYEVALDRxQocChXiloHgrLjgrK7grY3grKrgrL7grKYVALjRxQofChjiloHgrKjgrL/grLDgrY3grK/grL7grKQVAMDRxQoiChvgrKjgrL7grLngrL7grIHgrKjgrY3grKTgrL8VAMjRxQoiChviloHgrKTgrKTgrY3grJXgrL7grLPgrYDgrKgVANDRxQoiChviloHgrKbgrYfgrKzgrL7grKrgrL7grIfgrIEVANjRxQorCiTiloHgrIngrLLgrY3grLLgrYfgrJbgrK/grYvgrJfgrY3grZ8VAODRxQorCiTiloHgrJXgrYfgrKjgrY3grKbgrY3grLDgrL7grKrgrKHgrL4VAOjRxQorCiTiloHgrKjgrL/grLDgrY3grKbgrY3grKfgrL7grLDgrL/grKQVAPDRxQoNCgbgrajgra8VAPjRxQoQCgngrJ/grLDgrY0VAADSxQoQCgngrKrgrYvgrLcVAAjSxQoQCgngrK7grKjgrL4VABDSxQoQCgngrYvgrKfgrKgVABjSxQoQCgniloHgra/gra8VACDSxQoTCgzgrJXgrYfgrJ/grY0VACjSxQoTCgzgrKXgrYHgrLLgrL4VADDSxQoTCgzgrLLgrL/grJ/grL8VADjSxQoTCgziloHgrI/grKHgrL8VAEDSxQoTCgziloHgrKbgrYfgrJMVAEjSxQoTCgziloHgrKjgrL7grZ8VAFDSxQoTCgziloHgrKrgrYfgrKgVAFjSxQoTCgziloHgrKzgrYfgrLMVAGDSxQoTCgziloHgrLjgrKjgrL4VAGjSxQoTCgziloHgrLjgrLjgrY0VAHDSxQoWCg/grKfgrL7grKjgrY3grZ8VAHjSxQoWCg/iloHgrJXgrK7grLPgrL4VAIDSxQoWCg/iloHgrKrgrKbgrLDgrYEVAIjSxQoWCg/iloHgrKvgrY3grZ/grL4VAJDSxQoWCg/iloHgrKzgrL7grLLgrL4VAJjSxQoWCg/iloHgrKzgrL/grKTgrL8VAKDSxQoWCg/iloHgrK7grYvgrJ/grL4VAKjSxQoWCg/iloHgrLjgrYHgrKjgrYAVALDSxQoZChLgrK/grL/grKzgrL7grLDgrYEVALjSxQoZChLgrLfgrY3grJ/grY3grLDgrYcVAMDSxQoZChLiloHgrJXgrLLgrYfgrJzgrLAVAMjSxQoZChLiloHgrJrgrYfgrZ/grL7grLAVANDSxQoZChLiloHgrKzgrLjgrL/grLLgrYcVANjSxQoZChLiloHgrKzgrL7grKTgrL/grLIVAODSxQoZChLiloHgrLLgrKLgrLzgrYfgrIcVAOjSxQoZChLiloHgrLjgrJngrY3grJXgrJ8VAPDSxQocChXiloHgrKbgrL/grIbgrK/grL7grI8VAPjSxQocChXiloHgrLjgrK3grY3grZ/grKTgrL4VAADTxQofChjiloHgrLjgrY3grKXgrL7grKjgrLDgrYEVAAjTxQoiChviloHgrKjgrYHgrLngrIHgrKjgrY3grKTgrL8VABDTxQoiChviloHgrKzgrKjgrY3grKfgrYHgrJngrY3grJUVABjTxQolCh7iloHgrKrgrL7grLbgrY3grJrgrL7grKTgrY3grZ8VACDTxQooCiHiloHgrLngrKTgrY3grZ/grL7grJXgrL7grKPgrY3grKEVACjTxQoJCgJjaBUAMNPFCgkKAnViFQA408UKEAoJ4KyX4K2A4KykFQBA08UKEAoJ4Kyl4Ky/4KykFQBI08UKEAoJ4Kyy4Ky+4Ky4FQBQ08UKEAoJ4Ky54Kyw4Ky/FQBY08UKEAoJ4K2A4KyV4K2BFQBg08UKEAoJ4paB4KyX4KyJFQBo08UKEwoM4paB4Kyb4Ky+4KykFQBw08UKEwoM4paB4Kyy4Ky54KywFQB408UKFgoP4paB4Kyf4K2N4Kyw4KyVFQCA08UKFgoP4paB4Kyh4Ky+4Kyf4Ky+FQCI08UKFgoP4paB4Kym4K2H4Kyi4Ky8FQCQ08UKFgoP4paB4Kym4K2H4Ky24K2AFQCY08UKFgoP4paB4Kys4K2L4Kyy4K2NFQCg08UKFgoP4paB4Kyu4K2H4KyX4Ky+FQCo08UKFgoP4paB4Kyy4Ky+4KyH4KyoFQCw08UKFgoP4paB4K2n4K2m4Kyf4Ky/FQC408UKFgoP4paB4K2o4K2m4K2n4K2qFQDA08UKGQoS4Kys4Kyo4Ky/4Kyu4K2N4KyoFQDI08UKGQoS4Kys4Kyy4Ky+4KyZ4K2N4KyXFQDQ08UKGQoS4Ky/4Kyk4Ky+4KyZ4K2N4KyVFQDY08UKGQoS4paB4Kyc4Ky+4Kyk4Ky/4KywFQDg08UKGQoS4paB4Kym4Ky+4Kyw4K2N4Ky2FQDo08UKGQoS4paB4Kyr4K2L4Kyw4K2N4Ky4FQDw08UKGQoS4paB4Kyt4Ky+4KyH4Kyw4Ky4FQD408UKGQoS4paB4Kyu4Kye4K2N4Kyc4Ky/FQAA1MUKGQoS4paB4Ky44Kyg4Ky/4KyV4K2NFQAI1MUKHAoV4paB4KyG4KyH4Kyq4Ky/4KyP4KyyFQAQ1MUKHAoV4paB4KyG4Ky54K2N4Kys4Ky+4KyoFQAY1MUKHAoV4paB4Kya4Ky+4KyV4Ky/4Kyw4K2AFQAg1MUKHAoV4paB4Kyk4Kyk4K2N4Kyk4K2N4K2xFQAo1MUKHAoV4paB4Kyo4Ky/4Ky54Ky+4Kyk4Ky/FQAw1MUKHAoV4paB4Ky44K2N4Kys4Kya4K2N4KybFQA41MUKHAoV4paB4Ky54Kyo4K2B4Kyu4Ky+4KyoFQBA1MUKHwoY4paB4KyV4Kyw4K2N4Kyq4K2L4Kyw4K2HFQBI1MUKHwoY4paB4Kyn4K2I4Kyw4K2N4Kyv4K2N4K2fFQBQ1MUKHwoY4paB4Kyw4Ky+4K2f4KyX4Kyh4Ky84Ky+FQBY1MUKIgob4paB4Kys4Ky44Ky/4Kyb4Kyo4K2N4Kyk4Ky/FQBg1MUKJQoe4paB4Kyo4Ky/4Kyu4Kyo4K2N4Kyk4K2N4Kyw4KyjFQBo1MUKJQoe4paB4Kyu4K2B4Kyu4K2N4Kys4Ky+4KyH4Kyw4K2HFQBw1MUKKAoh4paB4Kyu4K2B4Ky54Ky+4KyB4Kyu4K2B4Ky54Ky/4KyBFQB41MUKKwok4paB4KyV4K2N4Ky34Kyk4Ky/4KyX4K2N4Kyw4Ky44K2N4KykFQCA1MUKKwok4paB4Kys4Ky+4Kyn4K2N4K2f4Kyk4Ky+4Kyu4K2C4Kyz4KyVFQCI1MUKMQoq4paB4Kyq4Kyw4Ky/4Kyq4K2N4Kyw4K2H4KyV4K2N4Ky34K2A4Kyw4K2HFQCQ1MUKEwoM4KyP4Kyu4Ky44Ky/FQCY1MUKEwoM4Kyq4K2N4Kyw4Ky4FQCg1MUKEwoM4Kyq4K2N4Kyw4Ky/FQCo1MUKEwoM4Ky24Ky+4Kyz4Ky+FQCw1MUKEwoM4paB4Kym4Ky+4KyXFQC41MUKEwoM4paB4Kym4K2A4KyoFQDA1MUKEwoM4paB4Kys4K2D4Ky3FQDI1MUKFgoP4KyX4Kyj4Ky+4Kyw4K2HFQDQ1MUKFgoP4Kyu4K2N4Kys4Ky/4KykFQDY1MUKFgoP4Ky/4KyV4Ky+4Kyw4K2AFQDg1MUKFgoP4paB4KyF4Kys4Kyn4Ky/FQDo1MUKFgoP4paB4KyW4Ky+4Kyk4Ky+FQDw1MUKFgoP4paB4KyX4K2B4Kyc4Ky+FQD41MUKFgoP4paB4Kys4Ky44Ky+4KyHFQAA1cUKGQoS4paB4Kyc4KyX4Kyk4Kyw4K2HFQAI1cUKGQoS4paB4Kyc4Kyh4Ky84Ky/4KykFQAQ1cUKGQoS4paB4Kyq4Kyw4Ky+4Kyc4K2fFQAY1cUKGQoS4paB4Ky44Ky+4Kyi4Ky84K2HFQAg1cUKHAoV4paB4Kyf4K2N4K2x4Ky/4Kyf4K2NFQAo1cUKHAoV4paB4Kyq4K2C4Kyw4K2N4Kys4KyVFQAw1cUKHwoY4paB4Kyq4K2N4Kyw4Ky+4K2f4Kyk4KyDFQA41cUKHwoY4paB4Kyq4K2N4Kyw4K2f4K2L4Kyc4KyoFQBA1cUKHwoY4paB4Kys4Kyw4K2N4Kyk4Kyu4Ky+4KyoFQBI1cUKHwoY4paB4Ky54K2H4Kys4Ky+4Kyq4Kyw4K2HFQBQ1cUKHwoY4paB4Ky54K2L4KyH4Kyq4Ky+4Kyw4K2BFQBY1cUKIgob4paB4KyG4Kyt4Ky/4Kyu4K2B4KyW4K2N4K2fFQBg1cUKIgob4paB4KyJ4Kyq4Ky44Kyt4Ky+4Kyq4Kyk4Ky/FQBo1cUKIgob4paB4Kys4Ky/4Ky24K2N4Kyz4K2H4Ky34KyjFQBw1cUKIgob4paB4Kyu4Ky/4Kyz4Ky/4Kyq4Ky+4Kyw4K2HFQB41cUKKAoh4paB4Kyu4Kyo4K2N4Kyk4K2N4Kyw4K2A4KyZ4K2N4KyVFQCA1cUKDQoG4Kyn4K2NFQCI1cUKEAoJ4Kya4K2H4KywFQCQ1cUKEAoJ4Kys4Kyw4K2NFQCY1cUKEAoJ4Kyz4K2L4KyVFQCg1cUKEAoJ4paB4KyV4KyqFQCo1cUKEwoM4Ky44Ky+4Kyw4Ky+FQCw1cUKDQoG4paBMjAxFQC41cUKEwoM4paB4KyV4Kym4Ky+FQDA1cUKEwoM4paB4KyW4Ky+4KyBFQDI1cUKEwoM4paB4KyX4Kyc4Ky+FQDQ1cUKEwoM4paB4KyY4Ky+4KykFQDY1cUKEwoM4paB4Kyu4Kyw4KyjFQDg1cUKFgoP4paB4KyP4Kyf4Ky/4KyPFQDo1cUKFgoP4paB4Kyo4Ky/4Kyw4KysFQDw1cUKFgoP4paB4Kys4Ky54K2B4KyzFQD41cUKFgoP4paB4Kyt4Kyw4Ky44Ky+FQAA1sUKFgoP4paB4Ky24K2B4Kyj4K2BFQAI1sUKGQoS4KyX4K2B4Kyh4Ky/4KyV4K2BFQAQ1sUKGQoS4Kya4K2N4Kyb4Kyo4K2N4KyoFQAY1sUKGQoS4Kyf4K2N4Kyw4Ky/4KyV4K2NFQAg1sUKGQoS4paB4KyF4Kyo4K2N4Kyk4KyDFQAo1sUKGQoS4paB4KyJ4Kyg4Ky/4Kyb4Ky/FQAw1sUKGQoS4paB4Kys4K2B4Kyh4Ky84Ky/FQA41sUKGQoS4paB4Ky44Ky/4Ky34K2N4KyfFQBA1sUKHAoV4Kyv4Ky+4KyG4Kyo4K2N4Kyk4Ky/FQBI1sUKHAoV4paB4KyV4Kyu4K2H4Kyj4K2N4KyfFQBQ1sUKHAoV4paB4Kym4K2B4Kyo4Ky/4KyG4KywFQBY1sUKHAoV4paB4Kyu4Kyo4K2H4Ky54K2B4KyPFQBg1sUKHAoV4paB4Ky44KyC4KyX4Kyg4Kyo4KywFQBo1sUKHwoY4paB4KyH4Kyj4K2N4Kyh4Ky/4KyG4KywFQBw1sUKHwoY4paB4Kyu4Kyo4K2B4Ky34K2N4K2f4KywFQB41sUKIgob4paB4Ky44Kyu4Ky+4Kyc4Ky44K2H4Kys4K2AFQCA1sUKJQoe4paB4KyG4Kyo4K2N4Kyk4Kyw4K2N4Kyc4Ky+4KykFQCI1sUKKAoh4paB4Kys4K2N4K2f4Kys4Ky44K2N4Kyl4Ky+4KyV4K2BFQCQ1sUKCgoDcm9tFQCY1sUKEAoJ4Kyh4Ky+4KywFQCg1sUKEwoM4KyV4Ky+4KyC4Ky2FQCo1sUKEwoM4paB4KyF4Kyu4KyzFQCw1sUKEwoM4paB4KyJ4Kyw4K2NFQC41sUKEwoM4paB4KyP4Kyj4K2HFQDA1sUKEwoM4paB4KyV4K2D4Ky3FQDI1sUKEwoM4paB4Kyf4Kyu4Ky+FQDQ1sUKFgoP4Kyf4KyZ4K2N4KyV4Ky+FQDY1sUKFgoP4paB4Kyc4K2N4Kys4KywFQDg1sUKFgoP4paB4Kys4K2A4Kyu4Ky+FQDo1sUKFgoP4paB4Kyu4Ky/4Kyg4Ky+FQDw1sUKGQoS4Kys4K2H4KyV4K2N4Ky34KyjFQD41sUKGQoS4Kyu4Ky+4Kyo4Kyo4K2N4KymFQAA18UKGQoS4paB4Kyo4Kyu4K2B4Kyo4Ky+FQAI18UKGQoS4paB4Kyq4Kyw4Kyu4Ky+4KyjFQAQ18UKGQoS4paB4Kyq4Ky+4KyH4Kyy4Ky+FQAY18UKGQoS4paB4Ky44Kyw4K2N4Kyt4Ky/FQAg18UKHAoV4paB4KyF4Kyt4Ky/4Kyt4Ky+4KysFQAo18UKHAoV4paB4KyV4Kyw4Ky/4Kym4Ky/4KyGFQAw18UKHAoV4paB4Kym4Ky/4Kys4K2N4K2f4Ky+FQA418UKHAoV4paB4Kys4Ky/4Ky24K2N4Kys4KywFQBA18UKHwoY4paB4KyG4Kyj4Ky/4Kyl4Ky/4Kyy4K2HFQBI18UKHwoY4paB4Kyf4K2N4Kyw4Ky+4Kyo4K2N4Ky4FQBQ18UKHwoY4paB4Ky44Kyw4K2N4Kys4Ky24K2H4Ky3FQBY18UKHwoY4paB4Ky44K2N4K2x4K2A4KyV4Ky+4KywFQBg18UKIgob4paB4Kyc4Ky+4Kyo4K2B4K2f4Ky+4Kyw4K2AFQBo18UKIgob4paB4Kyo4K2H4KyJ4Kyb4Kyo4K2N4Kyk4Ky/FQBw18UKIgob4paB4Kyq4K2C4Kyw4K2N4Kyj4K2N4Kyj4Ky/FQB418UKIgob4paB4Kyq4K2N4Kyw4Ky24Ky/4KyV4K2N4Ky3FQCA18UKIgob4paB4Kys4Ky/4Kyc4K2H4Kyq4Ky/4KyV4K2BFQCI18UKIgob4paB4Ky44Ky54Ky+4K2f4Kyk4Ky+4Kyw4K2HFQCQ18UKJQoe4paB4KyV4Ky+4Kyw4K2N4Kyv4K2N4K2f4Kyw4KykFQCY18UKJQoe4paB4Kyq4Ky+4KyH4Kyq4Ky+4Kyw4Ky/4Kys4K2HFQCg18UKJQoe4paB4Kyt4Kys4Ky/4Ky34K2N4K2f4Kyk4Kyw4K2HFQCo18UKJQoe4paB4Ky44Kyw4K2N4Kys4Kyo4Ky/4Kyu4K2N4KyoFQCw18UKKwok4paB4Kyv4K2H4KyJ4KyB4Kyu4Ky+4Kyo4KyZ4K2N4KyV4KywFQC418UKDQoG4Kyc4K2LFQDA18UKDQoG4Kyg4K2HFQDI18UKCwoE4paBKxUA0NfFChAKCeKWgeCsnOCssBUA2NfFChAKCeKWgeCtrOCtqxUA4NfFChMKDOCsuOCsv+CsqOCtjRUA6NfFChMKDOKWgeCsj+CsleCtgRUA8NfFChMKDOKWgeCsquCsvuCsthUA+NfFChMKDOKWgeCsq+CssOCtjRUAANjFChMKDOKWgeCtreCsn+CsvxUACNjFChYKD+CsmeCtjeCsl+CsvuCsshUAENjFChYKD+Csp+CsvuCsqOCsvuCspRUAGNjFChYKD+KWgeCsleCsvuCsruCtgRUAINjFChYKD+KWgeCsmOCsn+Csv+CsrBUAKNjFChYKD+KWgeCsmuCsv+CspOCtjRUAMNjFChYKD+KWgeCsruCsvuCsqOCtjRUAONjFChYKD+KWgeCsuOCsueCsv+CsphUAQNjFChkKEuKWgeCsheCsleCtjeCsuOCsvxUASNjFChkKEuKWgeCsoeCsv+CsnOCsvuCshxUAUNjFChkKEuKWgeCsr+Csv+CsrOCsvuCssBUAWNjFChwKFeCsrOCsvuCsqOCtgeCsruCsvuCsqBUAYNjFChwKFeKWgeCsheCssuCsv+CsruCtjeCsqhUAaNjFChwKFeKWgeCsnOCso+CsvuCsh+CsrOCsvhUAcNjFChwKFeKWgeCspuCsvuCsrOCsv+CssOCthxUAeNjFChwKFeKWgeCsrOCsmeCtjeCsl+Cti+CsqhUAgNjFChwKFeKWgeCsrOCtgeCsneCsv+CsrOCsvhUAiNjFChwKFeKWgeCssuCsleCsoeCsvuCsieCsqBUAkNjFCh8KGOKWgeCsheCsqOCtjeCsp+CsleCsvuCssBUAmNjFCh8KGOKWgeCsnOCsmeCtjeCsl+CssuCssOCthxUAoNjFCh8KGOKWgeCsqOCsv+CsruCsv+CspOCtjeCspBUAqNjFCh8KGOKWgeCsquCtjeCssOCsmuCss+Csv+CspBUAsNjFCh8KGOKWgeCsrOCsv+CsuOCtjeCspOCtg+CspBUAuNjFCh8KGOKWgeCsuOCsvuCsueCtgeCsmeCtjeCslRUAwNjFCh8KGOKWgeCsuOCth+CsleCth+Cso+CtjeCsoRUAyNjFCiIKG+KWgeCssuCsvuCsh+CsuOCth+CsqOCtjeCsuBUA0NjFCiUKHuKWgeCsuOCsruCtjeCsquCssOCtjeCsleCsv+CspBUA2NjFCg0KBuCtgOCsthUA4NjFChAKCeCsn+Csv+CsqxUA6NjFChMKDOCsleCso+CtjeCsoBUA8NjFChMKDOCsquCtjeCssOCtgBUA+NjFChMKDOKWgeCsruCtgOCsqBUAANnFChMKDOKWgeCssOCth+CsshUACNnFChYKD+CsnOCtjeCsnOCsv+CspBUAENnFChYKD+CsrOCstuCtjeCtn+CslRUAGNnFChYKD+KWgeCsleCtgeCsueCtgRUAINnFChYKD+KWgeCsqOCspuCtgOCssBUAKNnFChYKD+KWgeCsr+Cti+Csl+CtgBUAMNnFChkKEuCsrOCtjeCtn+CsleCtjeCspBUAONnFChkKEuKWgeCsleCsn+CsleCssOCthxUAQNnFChkKEuKWgeCspOCtgOCssOCtjeCspRUASNnFChkKEuKWgeCspOCtgeCss+CsuOCtgBUAUNnFChkKEuKWgeCsquCtguCsnOCsvuCssBUAWNnFChkKEuKWgeCsreCsvuCsl+CsrOCspBUAYNnFChkKEuKWgeCsruCsvuCsqOCtjeCtnxUAaNnFChkKEuKWgeCsruCsv+CspeCtgeCsqBUAcNnFChwKFeKWgeCsheCsruCsv+CspOCsvuCsrRUAeNnFChwKFeKWgeCsr+Cti+Csl+CspuCsvuCsqBUAgNnFChwKFeKWgeCssuCsv+CsruCsv+Csn+CthxUAiNnFChwKFeKWgeCsuOCsv+CsuOCsv+Csn+CsvxUAkNnFCh8KGOKWgeCsleCsueCtgeCspeCsv+CssuCthxUAmNnFCh8KGOKWgeCsleCti+CssuCsleCsvuCspOCsvhUAoNnFCh8KGOKWgeCsruCsvuCsk+CsrOCsvuCspuCtgBUAqNnFCiIKG+KWgeCsheCsp+CtjeCtn+CsleCtjeCst+CsvhUAsNnFCiUKHuKWgeCsuOCsv+CspuCtjeCsp+CsvuCsqOCtjeCspBUAuNnFCigKIeKWgeCsleCssOCsv+CspuCth+Csh+CspeCsv+CssuCthxUAwNnFCisKJOKWgeCsqOCsv+CssOCtjeCsreCssOCsr+Cti+Csl+CtjeCtnxUAyNnFCg0KBuCsmuCspBUA0NnFChAKCeCsruCsvuCsphUA2NnFChAKCeKWgeCsoeCssBUA4NnFChAKCeKWgeCsquCsshUA6NnFChAKCeKWgeCtq+CtrhUA8NnFChAKCeKWgeCtreCtrhUA+NnFChMKDOCspuCth+CsrOCthxUAANrFChMKDOCsrOCsvuCsquCsvhUACNrFChMKDOCsuOCsmeCtjeCslRUAENrFChMKDOKWgeCshuCsoeCsvBUAGNrFChMKDOKWgeCsoOCsvuCsrBUAINrFChMKDOKWgeCsquCsvuCsuBUAKNrFChMKDOKWgeCsrOCti+CsiBUAMNrFChMKDOKWgeCsuOCtjeCsqxUAONrFChYKD+CsmeCtjeCsl+CsvuCssBUAQNrFChYKD+KWgeCshuCsrOCsvuCsuBUASNrFChYKD+KWgeCsl+Cti+CsquCsqBUAUNrFChYKD+KWgeCsoeCtjeCssOCthxUAWNrFChYKD+KWgeCsrOCsv+Csl+CspBUAYNrFChYKD+KWgeCsuOCspuCtg+CsthUAaNrFChYKD+KWgeCsuOCsruCsv+CspBUAcNrFChYKD+KWgeCsuOCtguCsmuCsqBUAeNrFChYKD+KWgeCsuOCth+CspeCtjRUAgNrFChkKEuKWgeCsieCsqOCtjeCsruCtixUAiNrFChkKEuKWgeCsquCsvuCsieCsm+CsvxUAkNrFChkKEuKWgeCstuCsv+CstuCtgeCssBUAmNrFChkKEuKWgeCsuOCth+CsreCss+CsvxUAoNrFChwKFeKWgeCsleCth+CsnOCtjeCssOCsvxUAqNrFChwKFeKWgeCsmuCsvuCsueCsv+CspuCsvhUAsNrFChwKFeKWgeCsruCsvuCssOCsv+CsrOCsvhUAuNrFChwKFeKWgeCtseCsv+CsleCth+Csn+CtjRUAwNrFCh8KGOKWgeCshuCsq+CtjeCssOCsv+CsleCsvhUAyNrFCh8KGOKWgeCshuCssOCsruCtjeCsreCssOCtgRUA0NrFCiIKG+KWgeCsquCtjeCssOCstuCsvuCsuOCsqOCssBUA2NrFCiUKHuKWgeCspuCth+Csh+CspeCsvuCsqOCtjeCspOCsvxUA4NrFCiUKHuKWgeCsuOCtjeCsrOCsvuCsreCsvuCsrOCsv+CslRUA6NrFCigKIeKWgeCshuCsp+CtjeCtn+CsvuCspOCtjeCsruCsv+CslRUA8NrFCigKIeKWgeCsl+Cso+CsruCsvuCsp+CtjeCtn+CsruCssOCthxUA+NrFCigKIeKWgeCsquCtjeCssOCspOCsv+CsrOCsqOCtjeCsp+CslRUAANvFCg0KBuCsoeCslRUACNvFChAKCeCsnOCsqOCtjRUAENvFChAKCeCtn+Cti+CslxUAGNvFCgwKBeKWgXdoFQAg28UKEwoM4Kyg4Ky/4Kyw4K2HFQAo28UKEwoM4Kyq4K2N4Kyw4KylFQAw28UKEwoM4Ky44Ky+4Ky54Ky/FQA428UKEwoM4Ky54K2H4Kyy4Ky+FQBA28UKEwoM4paB4KyF4Kya4KyzFQBI28UKEwoM4paB4KyV4Kyq4K2NFQBQ28UKEwoM4paB4Kya4Kyu4KyVFQBY28UKFgoP4Kyq4K2B4Kyk4K2N4KywFQBg28UKFgoP4Ky24K2C4Kyo4K2N4K2fFQBo28UKFgoP4paB4KyV4K2N4Ky34KyjFQBw28UKFgoP4paB4KyW4K2N4K2f4Ky+FQB428UKFgoP4paB4Kym4Ky/4Ky24K2BFQCA28UKFgoP4paB4Kys4Ky+4Kyw4K2BFQCI28UKFgoP4paB4Kyv4K2A4Ky24K2BFQCQ28UKFgoP4paB4Ky44Kya4K2N4KyaFQCY28UKGQoS4Kye4K2N4Kya4Kyz4Kyw4K2HFQCg28UKHAoV4paB4Kyq4K2B4Kye4K2N4Kyc4Ky/FQCo28UKHAoV4paB4Kyu4Ky+4Kyf4Ky/4Kyw4K2HFQCw28UKHwoY4paB4KyG4Kyo4Kyo4K2N4Kym4Kyw4K2HFQC428UKHwoY4paB4Kyq4Ky+4KyG4Kyo4K2N4Kyk4Ky/FQDA28UKHwoY4paB4Ky44Kyu4K2D4Kym4K2N4Kyn4Ky/FQDI28UKIgob4paB4Kya4Kyz4Ky/4Kyk4Kys4Kyw4K2N4Ky3FQDQ28UKIgob4paB4Kya4Ky+4Ky34K2A4KyZ4K2N4KyV4K2BFQDY28UKIgob4paB4Kyq4Kyw4Ky/4Kym4Kyw4K2N4Ky24KyoFQDg28UKIgob4paB4Ky34K2N4Kyf4K2H4Ky44Kyo4Kyw4K2HFQDo28UKJQoe4paB4Ky54Kyw4Ky+4KyH4Kyb4Kyo4K2N4Kyk4Ky/FQDw28UKKAoh4paB4Kyc4KyX4Kyo4K2N4Kyo4Ky+4Kyl4KyZ4K2N4KyVFQD428UKKwok4paB4Kyq4Ky/4Kyy4Ky+4Kyu4Ky+4Kyo4KyZ4K2N4KyV4K2BFQAA3MUKCQoCYWsVAAjcxQoQCgngrILgrJjgrKgVABDcxQoQCgngrJ/grJXgrL4VABjcxQoTCgzgrJ7grY3grJzgrL4VACDcxQoTCgzgrK/grL7grI/grIEVACjcxQoTCgziloHgrJXgrYvgrLcVADDcxQoTCgziloHgrJbgrYvgrLIVADjcxQoTCgziloHgrKbgrLngrL8VAEDcxQoTCgziloHgrK3grYvgrJUVAEjcxQoTCgziloHgrK3grZ/grK0VAFDcxQoTCgziloHgrLDgrL7grJcVAFjcxQoWCg/grK7grL/grJXgrY3grLAVAGDcxQoWCg/grLPgrJXgrKPgrY3grKAVAGjcxQoWCg/iloHgrIbgrLbgrY3grbEVAHDcxQoWCg/iloHgrJXgrY3grLfgrK4VAHjcxQoWCg/iloHgrJjgrYfgrKjgrL8VAIDcxQoWCg/iloHgrJrgrKLgrLzgrL8VAIjcxQoWCg/iloHgrJrgrK7grY3grKoVAJDcxQoWCg/iloHgrK3grYLgrLfgrKMVAJjcxQoZChLiloHgrIbgrJngrY3grJfgrYEVAKDcxQoZChLiloHgrI/grJXgrKTgrY3grLAVAKjcxQocChXiloHgrIXgrKjgrYHgrJXgrYLgrLMVALDcxQocChXiloHgrJXgrLPgrL/grJngrY3grJcVALjcxQocChXiloHgrJrgrY3grZ/grL7grKjgrYcVAMDcxQocChXiloHgrKvgrL/grJngrY3grJfgrL8VAMjcxQocChXiloHgrK3grL7grLjgrY3grJXgrLAVANDcxQofChjiloHgrIbgrJzgrL/grJXgrL7grLLgrL8VANjcxQofChjiloHgrIngrKbgrY3grJjgrL7grJ/grKgVAODcxQofChjiloHgrLjgrY3grKXgrL7grKrgrL/grKQVAOjcxQoiChvgrLfgrY3grJ/grL7grJfgrY3grLDgrL7grK4VAPDcxQoiChviloHgrIXgrK3grL/grKjgrKjgrY3grKbgrKgVAPjcxQoiChviloHgrJXgrY3grZ/grL7grK7grYfgrLDgrL4VAADdxQoiChviloHgrLjgrL/grLjgrL/grJ/grL/grK3grL8VAAjdxQooCiHiloHgrIngrKrgrLjgrY3grKXgrL/grKTgrL/grLDgrYcVABDdxQooCiHiloHgrJXgrLDgrL7grK/grL7grIfgrKrgrL7grLDgrYcVABjdxQooCiHiloHgrKjgrL/grIbgrK/grL7grIfgrKXgrL/grLLgrL4VACDdxQooCiHiloHgrKrgrLDgrL/grKzgrLDgrY3grKTgrY3grKTgrYcVACjdxQoQCgngrKzgrL7grZ8VADDdxQoQCgngrK3grY3grLAVADjdxQoQCgngrLDgrY3grJwVAEDdxQoQCgngrLPgrYDgrLAVAEjdxQoQCgniloHgrKvgrJ8VAFDdxQoQCgniloHgra/grasVAFjdxQoTCgzgrLLgrYfgrJbgrL4VAGDdxQoTCgziloHgrJ/grL/grI8VAGjdxQoTCgziloHgrLngrL7grJ8VAHDdxQoTCgziloHgrafgravgraYVAHjdxQoWCg/grJ/grKvgrLDgrY3grK4VAIDdxQoWCg/grL7grLjgrYfgrKzgrYAVAIjdxQoWCg/iloHgrJrgrLDgrY3grK4VAJDdxQoWCg/iloHgrKHgrYvgrJzgrY0VAJjdxQoWCg/iloHgrKTgrYfgrLLgrYcVAKDdxQoWCg/iloHgrKjgrL7grK7grL4VAKjdxQoWCg/iloHgrKzgrL7grIfgrJUVALDdxQoWCg/iloHgrK7grYLgrLPgrJUVALjdxQoWCg/iloHgrK/grYzgrKzgrKgVAMDdxQoWCg/iloHgrLbgrLjgrY3grZ8VAMjdxQoWCg/iloHgrLjgrL/grLLgrY0VANDdxQoZChLgrJfgrYHgrKHgrL/grJXgrLAVANjdxQoZChLiloHgrIXgrKvgrL/grLjgrY0VAODdxQoZChLiloHgrJXgrLDgrL/grLLgrYcVAOjdxQoZChLiloHgrJzgrL7grK7grL/grKgVAPDdxQoZChLiloHgrJzgrYfgrLLgrLDgrYcVAPjdxQoZChLiloHgrKjgrKbgrYDgrLDgrYcVAADexQoZChLiloHgrKzgrL7grKTgrL7grKwVAAjexQocChXiloHgrJXgrLbgrY3grK7grYDgrLAVABDexQocChXiloHgrKzgrJ7grY3grJrgrL/grKQVABjexQocChXiloHgrKzgrL/grJXgrLbgrL/grKQVACDexQocChXiloHgrK7grKPgrL/grLfgrJXgrYEVACjexQocChXiloHgrLjgrYvgrKjgrKrgrYHgrLAVADDexQofChjiloHgrIXgrLDgrKzgrL/grKjgrY3grKYVADjexQofChjiloHgrKjgrL/grLDgrY3grKbgrY3grKcVAEDexQofChjiloHgrK/grYvgrJfgrY3grZ/grKTgrL4VAEjexQoiChviloHgrKrgrY3grLDgrKbgrYfgrLbgrLDgrYcVAFDexQolCh7iloHgrIbgrK7grYfgrLDgrL/grJXgrL7grLDgrYcVAFjexQolCh7iloHgrKrgrY3grLDgrL7grKfgrL7grKjgrY3grZ8VAGDexQolCh7iloHgrLjgrY3grbHgrL7grKfgrYDgrKjgrKTgrL4VAGjexQorCiTiloHgrKrgrYLgrLDgrY3grKzgrL7grKjgrYHgrK7grL7grKgVAHDexQorCiTiloHgrKrgrY3grLDgrL7grLDgrY3grKXgrYDgrJngrY3grJUVAHjexQouCifiloHgrIbgrKjgrY3grKfgrY3grLDgrKrgrY3grLDgrKbgrYfgrLYVAIDexQouCifiloHgrLbgrY3grLDgrKbgrY3grKfgrL7grJ7grY3grJzgrLPgrL8VAIjexQo0Ci3iloHgrJfgrYHgrLDgrYHgrKTgrY3grKzgrKrgrYLgrLDgrY3grKPgrY3grKMVAJDexQoKCgN0aWMVAJjexQoQCgngrJXgrYDgrZ8VAKDexQoQCgngrLLgrL/grIIVAKjexQoQCgngrLPgrY3grJUVALDexQoQCgniloHgrKrgrJ8VALjexQoQCgniloHgrK3grYwVAMDexQoTCgzgrKjgrL7grLDgrYEVAMjexQoTCgzgrKrgrLbgrY3grJoVANDexQoTCgziloHgrI/grLLgrY0VANjexQoTCgziloHgrJXgrYvgrIkVAODexQoTCgziloHgrJfgrYHgrLIVAOjexQoTCgziloHgrJ/grKjgrY0VAPDexQoTCgziloHgrKfgrYDgrLAVAPjexQoTCgziloHgrKrgrL7grLMVAADfxQoTCgziloHgrLjgrL7grKUVAAjfxQoTCgziloHgrafgra/grakVABDfxQoWCg/grK/grYHgrKbgrY3grKcVABjfxQoWCg/grYvgrJfgrY3grLDgrL4VACDfxQoWCg/iloHgrJXgrYvgrKDgrL4VACjfxQoWCg/iloHgrKvgrY3grLDgrL8VADDfxQoWCg/iloHgrKzgrLLgrLDgrYcVADjfxQoZChLiloHgrKjgrY3grZ/grYHgrJwVAEDfxQoZChLiloHgrKrgrLbgrY3grJrgrL8VAEjfxQoZChLiloHgrKrgrYDgrKHgrL/grKQVAFDfxQocChXiloHgrLjgrJngrY3grJfgrKDgrKgVAFjfxQocChXiloHgrLjgrLngrJXgrL7grLDgrYAVAGDfxQocChXiloHgrLjgrYHgrKrgrL7grLDgrL8VAGjfxQofChjiloHgrI/grLngrL7grJvgrKHgrLzgrL4VAHDfxQofChjiloHgrKHgrL7grJXgrY3grKTgrLDgrYAVAHjfxQofChjiloHgrKbgrL/grIbgrK/grL/grKzgrL4VAIDfxQofChjiloHgrKzgrYHgrJ3grL7grK7grKPgrL4VAIjfxQofChjiloHgrLjgrKzgrYHgrJXgrL/grJvgrL8VAJDfxQoiChviloHgrI/grK7grL7grKjgrJngrY3grJXgrLAVAJjfxQoiChviloHgrLDgrL7grIngrLDgrJXgrYfgrLLgrL4VAKDfxQolCh7iloHgrJXgrY3grLfgrKTgrL/grKrgrYLgrLDgrKMVAKjfxQolCh7iloHgrJrgrY3grZ/grL7grLLgrYfgrJ7grY3grJwVALDfxQooCiHiloHgrJ3grL7grLDgrLjgrYHgrJfgrYHgrKHgrLzgrL4VALjfxQoJCgJhYhUAwN/FCgsKBOKWgTcVAMjfxQoKCgNlcnMVANDfxQoQCgngrKzgrLLgrY0VANjfxQoQCgniloHgrJfgrKoVAODfxQoTCgzgrLjgrY3grLDgrL4VAOjfxQoPCgjiloHgpaQiJxUA8N/FChMKDOKWgeCsieCst+CsvhUA+N/FChMKDOKWgeCsrOCssOCsoxUAAODFChYKD+KWgeCsieCsoeCsvOCsvxUACODFChYKD+KWgeCsleCtjeCssOCtnxUAEODFChYKD+KWgeCsl+CsvuCsh+CsoRUAGODFChYKD+KWgeCsquCsouCsvOCsvhUAIODFChYKD+KWgeCsquCsvuCsluCthxUAKODFChYKD+KWgeCsreCsv+CspOCssBUAMODFChYKD+KWgeCsuOCspOCssOCthxUAOODFChYKD+KWgeCsuOCsruCtgOCssBUAQODFChYKD+KWgeCsuOCsvuCspeCtgBUASODFChkKEuKWgeCsj+CsueCsvuCssOCsvxUAUODFChkKEuKWgeCsl+CsvuCsp+Cti+CshxUAWODFChkKEuKWgeCsreCsvuCsl+CssOCthxUAYODFChkKEuKWgeCsreCsvuCst+CsvuCssBUAaODFChwKFeKWgeCshuCsp+CsvuCssOCsv+CspBUAcODFChwKFeKWgeCsleCtjeCssOCtgOCsoeCsvBUAeODFChwKFeKWgeCsnOCsruCsv+CspuCsvuCssBUAgODFChwKFeKWgeCsquCtjeCssOCth+CsruCssBUAiODFChwKFeKWgeCsreCsvuCsh+CssOCsuOCtjRUAkODFChwKFeKWgeCsruCtgeCssOCtjeCsruCtgRUAmODFCh8KGOKWgeCshuCsuOCtgeCspeCsv+CssuCthxUAoODFCh8KGOKWgeCsquCsmuCsvuCssOCsv+CssuCthxUAqODFCiIKG+KWgeCsruCsqOCti+CssOCsnuCtjeCsnOCsqBUAsODFCiUKHuKWgeCsieCssuCtjeCssuCth+CsluCsqOCtgOCtnxUAuODFCi4KJ+KWgeCsheCsreCsv+Csr+CtgeCsleCtjeCspOCsmeCtjeCsleCtgRUAwODFCi4KJ+KWgeCsnOCsv+CssuCtjeCssuCsvuCsquCsvuCss+CsmeCtjeCslRUAyODFCg0KBuCshuCsshUA0ODFCgwKBeKWgSEiFQDY4MUKEwoM4KyX4Ky+4Kyu4K2AFQDg4MUKEwoM4Kym4Ky+4Kyw4K2HFQDo4MUKEwoM4Kyn4Ky+4Kyw4KyjFQDw4MUKEwoM4Kyq4Ky/4Kyf4Ky+FQD44MUKEwoM4K2A4KyV4K2D4KykFQAA4cUKEwoM4paB4KyW4Ky+4KyyFQAI4cUKEwoM4paB4Ky44Kyo4K2NFQAQ4cUKFgoP4paB4KyX4Ky+4KyG4KyBFQAY4cUKFgoP4paB4KyX4K2B4Kyu4K2NFQAg4cUKFgoP4paB4Kya4K2L4Kyw4Ky+FQAo4cUKFgoP4paB4Kym4Ky+4Kyw4K2BFQAw4cUKFgoP4paB4Kym4Ky/4Kys4Ky+FQA44cUKFgoP4paB4Kym4K2N4Kys4Ky+FQBA4cUKFgoP4paB4Kyo4Ky/4K2f4K2BFQBI4cUKFgoP4paB4Kys4KyV4K2N4Ky4FQBQ4cUKFgoP4paB4Ky54Kyw4K2N4Ky3FQBY4cUKFgoP4paB4Ky54K2H4KyJ4KybFQBg4cUKGQoS4Ky/4Kyo4Kyl4Ky/4Kyy4Ky+FQBo4cUKGQoS4paB4KyH4Kyy4K2H4KyV4K2NFQBw4cUKGQoS4paB4Kyq4K2H4Kyc4KyV4K2BFQB44cUKGQoS4paB4Kys4Ky44K2N4Kyk4Ky/FQCA4cUKGQoS4paB4Kys4K2B4Kyi4Ky84K2AFQCI4cUKGQoS4paB4Kyw4Ky/4Kyy4Ky/4KycFQCQ4cUKGQoS4paB4Kyy4Ky+4KyH4Kyt4K2NFQCY4cUKHAoV4paB4KyF4KyZ4K2N4KyX4Ky+4KywFQCg4cUKHAoV4paB4KyF4Ky44K2N4Kyl4Ky/4KywFQCo4cUKHAoV4paB4KyV4Kyw4K2N4Kyj4K2N4KyjFQCw4cUKHAoV4paB4Kya4Ky/4Ky54K2N4Kyo4Ky/FQC44cUKHAoV4paB4Kyc4Kyw4K2N4Kyu4Ky+4KyoFQDA4cUKHAoV4paB4Kyu4Kyj4K2N4Kyh4Kyz4K2AFQDI4cUKHAoV4paB4Kyu4K2B4Ky54KyB4KyV4K2BFQDQ4cUKHAoV4paB4Ky44K2N4Kyl4KyX4Ky/4KykFQDY4cUKHwoY4paB4KyV4K2N4Ky34K2f4KyV4K2N4Ky3FQDg4cUKHwoY4paB4Kyb4Kyk4K2N4Kyw4Kyq4K2B4KywFQDo4cUKHwoY4paB4Kyo4Ky/4Kyw4K2N4Kys4Ky+4KyaFQDw4cUKHwoY4paB4Kys4Ky+4Kyk4Ky+4Kys4Kyw4KyjFQD44cUKHwoY4paB4Kyu4Ky54K2L4Kyk4K2N4Ky44KysFQAA4sUKHwoY4paB4Kyw4K2B4Ky54Kyo4K2N4Kyk4Ky/FQAI4sUKIgob4paB4Ky44Ky+4Kyn4Ky+4Kyw4Kyj4Kyw4K2HFQAQ4sUKDQoG4Kyt4KyXFQAY4sUKCwoE4paBOBUAIOLFChAKCeCsmuCth+CspBUAKOLFChAKCeCsquCtjeCsnxUAMOLFChAKCeCssOCtgeCsqhUAOOLFChAKCeKWgeCsp+CtgRUAQOLFChAKCeKWgeCsruCtgBUASOLFChMKDOCsvuCssOCti+CsuRUAUOLFChMKDOKWgeCsh+Csn+CsvhUAWOLFChMKDOKWgeCsl+Cti+CsoRUAYOLFChMKDOKWgeCsrOCsv+CsmxUAaOLFChMKDOKWgeCsruCstuCsvhUAcOLFChYKD+CsnOCso+CsmeCtjeCslRUAeOLFChYKD+CsquCsv+Csn+CsvuCsshUAgOLFChYKD+KWgeCsh+CsqOCsv+CsghUAiOLFChYKD+KWgeCsleCspuCss+CtgBUAkOLFChYKD+KWgeCsnOCsn+Csv+CssxUAmOLFChYKD+KWgeCsquCsvuCss+CsvxUAoOLFChYKD+KWgeCsuOCtjeCssOCtixUAqOLFChkKEuCsleCsv+CsmeCtjeCsl+CtjRUAsOLFChkKEuCspuCsvuCsqOCsqOCtjeCsphUAuOLFChkKEuCssuCth+CstuCtjeCtseCssBUAwOLFChkKEuCsuOCtgeCsqOCtjeCspuCssBUAyOLFChkKEuCsuOCtjeCspOCssOCssOCthxUA0OLFChkKEuKWgeCsleCssOCsv+CsrOCtgRUA2OLFChkKEuKWgeCsleCsvuCsouCsvOCsvxUA4OLFChkKEuKWgeCspuCtgeCsqOCsv+CshhUA6OLFChkKEuKWgeCssOCtgeCst+Csv+CshhUA8OLFChkKEuKWgeCsuOCtjeCtseCtn+CsghUA+OLFChkKEuKWgeCsueCti+Csh+CssuCthxUAAOPFChwKFeKWgeCsnOCsvuCsl+CtjeCssOCspBUACOPFChwKFeKWgeCsrOCsmeCtjeCsl+Css+CsvhUAEOPFChwKFeKWgeCssOCsvuCsnOCsuOCtjeCtsRUAGOPFCh8KGOKWgeCsmuCsv+CspOCtjeCsleCsvuCssBUAIOPFCiIKG+KWgeCsrOCtjeCtn+CsvuCsn+CtjeCsuOCsrhUAKOPFCiUKHuKWgeCsheCsquCssOCsvuCsueCtjeCsqOCssOCthxUAMOPFCiUKHuKWgeCsq+Cth+CssOCsv+Csm+CsqOCtjeCspOCsvxUAOOPFCiUKHuKWgeCsuOCtjeCtseCsvuCsreCsvuCsrOCsv+CslRUAQOPFCg0KBuCsheCsmxUASOPFCg0KBuCsieCsnBUAUOPFChAKCeCsmeCtjeCsmBUAWOPFChAKCeCsruCtn+CtgBUAYOPFChMKDOKWgeCsheCsquCsoxUAaOPFChMKDOKWgeCsheCsquCtjRUAcOPFChMKDOKWgeCsieCsoOCthxUAeOPFChMKDOKWgeCsleCtgOCssBUAgOPFChMKDOKWgeCsruCssOCtjRUAiOPFChMKDOKWgeCssOCstuCtjRUAkOPFChYKD+CspuCth+CstuCssOCthxUAmOPFChYKD+CsruCsvuCsmeCtjeCslRUAoOPFChYKD+KWgeCshuCssuCtgeCshRUAqOPFChYKD+KWgeCsrOCsvuCssOCsoxUAsOPFChYKD+KWgeCsrOCsv+CsqOCtixUAuOPFChYKD+KWgeCssuCsruCtjeCsrBUAwOPFChYKD+KWgeCsuOCtjeCtseCthxUAyOPFChkKEuKWgeCspuCtgeCsg+CsluCssBUA0OPFChkKEuKWgeCsuOCth+CsquCssOCsvxUA2OPFChwKFeKWgeCshuCssOCsvuCsp+CsqOCsvhUA4OPFChwKFeKWgeCsruCsueCsvuCsqOCsl+CssBUA6OPFChwKFeKWgeCsuOCsruCsvuCsrOCth+CsthUA8OPFCh8KGOKWgeCssOCsvuCsuOCtjeCspOCsvuCssBUA+OPFCh8KGOKWgeCstuCsv+CsleCtjeCst+Csv+CspBUAAOTFCh8KGOKWgeCsuOCsruCtjeCsrOCth+CspuCsqBUACOTFCiIKG+KWgeCsleCsvuCssOCtjeCsr+CtjeCtn+CssBUAEOTFCiIKG+KWgeCsnOCsv+CssuCtjeCssuCsvuCsleCtgRUAGOTFCiUKHuCsreCtgeCsrOCsqOCth+CstuCtjeCsrOCssOCsgxUAIOTFCisKJOKWgeCsleCtgeCsueCsvuCsr+CsvuCsh+CspeCsv+CssuCsvhUAKOTFCg0KBuCsheCsgRUAMOTFCg0KBuCtquCtqxUAOOTFChAKCeCsrOCssOCsvhUAQOTFChAKCeCstuCspOCsgxUASOTFChAKCeCti+CspuCssBUAUOTFChAKCeKWgeCsruCtiBUAWOTFChMKDOCsoeCtjeCssOCthxUAYOTFChMKDOCsruCsmeCtjeCslRUAaOTFChMKDOCsuOCsreCsvuCssBUAcOTFChMKDOKWgeCsheCsmOCsnxUAeOTFChMKDOKWgeCsk+Cst+CsvhUAgOTFChMKDOKWgeCsp+CtjeCtsRUAiOTFChMKDOKWgeCsqOCsrOCsvhUAkOTFChMKDOKWgeCsqOCtjeCtnxUAmOTFChMKDOKWgeCsquCsueCsvxUAoOTFChMKDOKWgeCsueCtjeCssBUAqOTFChYKD+CshuCsqOCtjeCspOCsvhUAsOTFChYKD+Csv+CsleCsvuCssOCthxUAuOTFChYKD+KWgeCssuCsouCsvOCsvxUAwOTFChkKEuCsqOCsvuCssOCsvuCtn+CsoxUAyOTFChkKEuKWgeCsleCsnuCtjeCsmuCsvhUA0OTFChkKEuKWgeCsreCtn+CsreCtgOCspBUA2OTFChkKEuKWgeCsruCsueCsvuCstuCtnxUA4OTFChkKEuKWgeCstuCtjeCss+Cti+CslRUA6OTFChwKFeCsrOCtgOCsqOCtjeCspuCtjeCssBUA8OTFChwKFeKWgeCsheCsqOCtjeCspOCspOCsgxUA+OTFChwKFeKWgeCsieCsquCsvuCtn+CssOCthxUAAOXFChwKFeKWgeCsleCsvuCsueCsv+CsgeCslRUACOXFChwKFeKWgeCsnOCss+CsuOCth+CsmuCsqBUAEOXFChwKFeKWgeCsquCtjeCssOCspOCsv+CsthUAGOXFChwKFeKWgeCsruCsqOCti+CsqOCtgOCspBUAIOXFCh8KGOCst+CtjeCsn+Csv+CsmuCtjeCtn+CtgRUAKOXFCh8KGOKWgeCsheCsp+Csv+CsrOCth+CstuCsqBUAMOXFCh8KGOKWgeCsheCsqOCtgeCsreCtguCspOCsvxUAOOXFCh8KGOKWgeCsquCsvuCsieCspeCsv+CsrOCsvhUAQOXFCiIKG+KWgeCsrOCsv+CstuCth+Cst+CsleCssOCsvxUASOXFCiUKHuKWgeCsquCtjeCssuCsvuCsn+Csq+CssOCtjeCsrhUAUOXFCigKIeKWgeCsleCsvuCssOCtjeCsr+CtjeCtn+CsleCss+CsvhUAWOXFCigKIeKWgeCst+CtjeCsn+CsvuCsoeCsv+Ctn+CsruCssOCthxUAYOXFCg0KBuCsleCsoxUAaOXFChAKCeCsoeCth+CsqBUAcOXFChAKCeCsquCsleCtgRUAeOXFChAKCeCsquCsoeCsvxUAgOXFChAKCeCsuOCtgeCssBUAiOXFChAKCeKWgeCsruCsshUAkOXFChMKDOCsl+CsnuCtjeCsnBUAmOXFChMKDOCsrOCsvuCssuCsvxUAoOXFChMKDOCsruCsv+Css+CsvxUAqOXFChMKDOKWgeCsheCssuCtjRUAsOXFChMKDOKWgeCsmuCss+CsvxUAuOXFChMKDOKWgeCsqOCsl+CsphUAwOXFChMKDOKWgeCsruCtg+CslxUAyOXFChMKDOKWgeCsueCsv+CspBUA0OXFChYKD+CspuCtgOCsmeCtjeCslRUA2OXFChYKD+CsrOCsvuCsueCtjeCsqBUA4OXFChYKD+KWgeCsj+CsoeCsvOCthxUA6OXFChYKD+KWgeCsmOCtg+Cso+CsvhUA8OXFChYKD+KWgeCsq+CsvuCsn+CsvxUA+OXFChYKD+KWgeCsrOCtjeCss+CslRUAAObFChYKD+KWgeCsreCspOCssOCthxUACObFChYKD+KWgeCtqOCtpuCtp+CtqxUAEObFChkKEuCsuOCssOCsleCsvuCssOCtgBUAGObFChkKEuKWgeCsheCsguCsmuCss+CssBUAIObFChkKEuKWgeCsieCsquCtgeCsnOCsvxUAKObFChkKEuKWgeCsleCsvuCsueCsv+CslRUAMObFChkKEuKWgeCsmuCsvuCsh+CsqOCsvhUAOObFChkKEuKWgeCsoeCtjeCssOCsl+CtjRUAQObFChkKEuKWgeCspuCtg+Cst+CtjeCsnxUASObFChkKEuKWgeCsrOCssOCtjeCsl+CssBUAUObFChkKEuKWgeCsruCso+CtjeCsoeCsvxUAWObFChkKEuKWgeCsuOCsvuCsh+CsrOCssBUAYObFChwKFeKWgeCsleCth+CsnOCsvuCso+CsvxUAaObFChwKFeKWgeCsquCtgeCssOCtgOCssOCthxUAcObFChwKFeKWgeCsruCsv+CstuCtjeCssOCsoxUAeObFChwKFeKWgeCsuOCtjeCspOCssOCtgOCtnxUAgObFChwKFeKWgeCsuOCtjeCsquCth+CstuCsvhUAiObFCh8KGOKWgeCsleCsrOCsv+CspOCsvuCssOCthxUAkObFCh8KGOKWgeCsleCsruCtjeCsquCsvuCsqOCsvxUAmObFCh8KGOKWgeCsr+Cti+CsnOCsqOCsvuCsleCtgRUAoObFCh8KGOKWgeCsuOCsvuCssOCtjeCsn+Csv+CsqxUAqObFCiIKG+KWgeCsheCsqOCtjeCtn+CsruCsvuCsqOCthxUAsObFCiIKG+KWgeCsquCtjeCssOCth+CsruCsv+CsleCsvhUAuObFCiIKG+KWgeCsrOCsvuCsueCsvuCssOCsv+CsrOCsvhUAwObFCiUKHuKWgeCsruCtg+CspOCtjeCtn+CtgeCsrOCssOCsoxUAyObFCgkKAm9jFQDQ5sUKCgoDYWxsFQDY5sUKEAoJ4Ky+4Kyo4KyoFQDg5sUKEAoJ4K2C4KyG4KyBFQDo5sUKEAoJ4K2p4Kyw4K2HFQDw5sUKEAoJ4paB4K2r4K2oFQD45sUKEwoM4Kyj4K2N4Kyh4K2AFQAA58UKEwoM4Kyj4K2N4Kyh4K2LFQAI58UKEwoM4Kyj4K2N4Kyh4K2NFQAQ58UKDQoG4paBVGhlFQAY58UKFgoP4Kyj4K2N4Kyf4Ky/4KyGFQAg58UKFgoP4Kyw4Kye4K2N4Kyc4KyoFQAo58UKFgoP4paB4KyF4Kys4K2N4K2fFQAw58UKFgoP4paB4KyV4Ky+4Kyz4K2HFQA458UKFgoP4paB4Kyq4K2B4KyF4KywFQBA58UKGQoS4Kyq4Ky+4Kyw4Ky/4Kyy4K2HFQBI58UKGQoS4Kyt4K2L4KyV4K2N4Kyk4Ky+FQBQ58UKGQoS4paB4Kyq4Kyo4K2N4Kym4KywFQBY58UKGQoS4paB4Ky44Ky+4Kyw4K2N4KysFQBg58UKGQoS4paB4Ky44K2N4Kyu4Ky+4KywFQBo58UKHAoV4paB4KyV4Ky/4Kyj4Ky/4Kys4Ky+FQBw58UKHAoV4paB4Kys4Kyw4K2N4Kym4K2N4KynFQB458UKHAoV4paB4Kys4Ky+4Kyj4K2N4Kyf4Ky/FQCA58UKHAoV4paB4Ky44K2N4K2x4Kya4K2N4KybFQCI58UKHwoY4paB4Kyk4Ky+4Ky54Ky+4KyZ4K2N4KyVFQCQ58UKHwoY4paB4Kyq4K2N4Kyw4Ky44Ky+4Kyw4KyjFQCY58UKJQoe4Kyz4Ky+4KyW4K2H4Kyu4K2B4Kyj4K2N4Kyh4Ky/FQCg58UKJQoe4paB4Ky44Kyu4K2N4Kyq4K2N4Kyw4Kym4Ky+4K2fFQCo58UKJQoe4paB4Ky54K2H4KyJ4Kyl4Ky/4Kys4Ky+4Kyw4K2BFQCw58UKKwok4paB4Kyc4Kyo4Ky44Ky+4Kyn4Ky+4Kyw4Kyj4KyZ4K2N4KyVFQC458UKMQoq4paB4Kyq4K2N4Kyw4Kyk4Ky/4Kyv4K2L4KyX4Ky/4Kyk4Ky+4Kyw4K2HFQDA58UKCwoE4paBPRUAyOfFChAKCeCsk+CsoeCsvxUA0OfFChAKCeCsk+CtjeCsrBUA2OfFChAKCeCsm+CsoeCsvhUA4OfFChAKCeCtg+CspOCsvhUA6OfFChAKCeCtn+CsvuCsqBUA8OfFChAKCeKWgeCshuCsoxUA+OfFChAKCeKWgeCtrOCtrBUAAOjFChMKDOCsnOCsmeCtjeCslxUACOjFChMKDOCsreCsleCtjeCspBUAEOjFChMKDOKWgeCsheCsuOCtgBUAGOjFChMKDOKWgeCsnOCtgeCshhUAIOjFChMKDOKWgeCsquCspOCsvhUAKOjFChMKDOKWgeCsrOCti+CspBUAMOjFChMKDOKWgeCssOCsruCsvhUAOOjFChYKD+CsmuCsqOCtjeCspuCsqBUAQOjFChYKD+KWgeCshuCsl+CsruCsqBUASOjFChYKD+KWgeCsleCsv+CssOCsoxUAUOjFChYKD+KWgeCsnOCth+CsnOCthxUAWOjFChYKD+KWgeCssOCsleCtjeCstxUAYOjFChYKD+KWgeCsueCsvuCsoeCsvBUAaOjFChYKD+KWgeCtqOCtpuCtpuCtphUAcOjFChkKEuKWgeCsleCsueCsv+CsrOCtgRUAeOjFChkKEuKWgeCsoOCsv+CsleCso+CsvhUAgOjFChkKEuKWgeCsrOCsvuCsh+CsleCtjRUAiOjFChkKEuKWgeCsruCsvuCsuOCsv+CslRUAkOjFChwKFeCsvuCsqOCtjeCspOCssOCsv+CspBUAmOjFChwKFeKWgeCsieCsmuCtjeCsmuCspOCsvhUAoOjFCh8KGOCssOCtjeCsr+CtjeCtn+CsvuCspuCsvhUAqOjFCh8KGOKWgeCsnOCsrOCssOCspuCsuOCtjeCspBUAsOjFCh8KGOKWgeCsqOCth+CsieCspeCsv+CsrOCsvhUAuOjFCisKJOKWgeCshuCsqOCtjeCspOCssOCtjeCsnOCsvuCspOCsv+CslRUAwOjFCg0KBuCsoeCtgBUAyOjFChAKCeCshuCssOCsvxUA0OjFChAKCeKWgeClpOClpBUA2OjFChAKCeKWgeCtreCtqRUA4OjFChMKDOCsn+Cth+CssOCsvhUA6OjFChMKDOCsqOCsvuCssuCtjRUA8OjFChMKDOCssOCtjeCsquCsoxUA+OjFChMKDOCsv+CsleCssOCsvxUAAOnFChMKDOKWgeCsl+Csm+CssBUACOnFChYKD+CspuCsvuCsrOCsvuCsphUAEOnFChYKD+CsrOCsvuCsoeCsvOCsvxUAGOnFChYKD+KWgeCshuCsh+Csn+CsvxUAIOnFChYKD+KWgeCshuCsreCtjeCtnxUAKOnFChYKD+KWgeCsleCth+CsnOCsvxUAMOnFChYKD+KWgeCsl+Cso+CsqOCsvhUAOOnFChYKD+KWgeCsq+CtjeCssOCthxUAQOnFChYKD+KWgeCssOCtgOCspOCsvxUASOnFChYKD+KWgeCsuOCsueCssOCsvhUAUOnFChYKD+KWgeCsueCsn+CsvuCshxUAWOnFChkKEuCspeCsv+CssuCsvuCsl+CsvxUAYOnFChkKEuKWgeCsieCsoOCsv+CssuCthxUAaOnFChkKEuKWgeCsreCsvuCsmeCtjeCslxUAcOnFChkKEuKWgeCsruCsueCssuCssOCthxUAeOnFChkKEuKWgeCsruCth+Cso+CtjeCsnxUAgOnFChkKEuKWgeCsuOCsruCtjeCsquCsphUAiOnFChwKFeCsrOCtjeCtn+CsrOCsueCsvuCssBUAkOnFChwKFeKWgeCsheCsquCtguCssOCtjeCsrBUAmOnFChwKFeKWgeCsnOCsv+CssuCsvuCssOCthxUAoOnFChwKFeKWgeCspuCsruCtjeCsquCspOCsvxUAqOnFChwKFeKWgeCsquCspuCtjeCsp+CspOCsvxUAsOnFCh8KGOKWgeCsieCspOCtjeCspOCti+Css+CsqBUAuOnFCh8KGOKWgeCsnOCth+CsqOCth+CssOCsvuCsshUAwOnFCh8KGOKWgeCsquCsvuCsh+CsrOCsvuCssOCthxUAyOnFCh8KGOKWgeCsquCtjeCssOCstuCsguCsuOCslRUA0OnFCh8KGOKWgeCsuOCtguCsleCtjeCst+CtjeCsrhUA2OnFCiIKG+KWgeCsh+CspOCsv+CsueCsvuCsuOCssOCthxUA4OnFCiIKG+KWgeCstuCtjeCssOCth+Cso+CtgOCssOCthxUA6OnFCiIKG+KWgeCsuOCtjeCspeCsv+CspOCsv+CsleCtgRUA8OnFCiUKHuKWgeCsqOCsv+CssOCtjeCsr+CsvuCspOCsqOCsvhUA+OnFCigKIeKWgeCsrOCsmeCtjeCsl+Cti+CsquCsuOCsvuCsl+CssBUAAOrFCg0KBuCsquCsshUACOrFCgoKA2FyZRUAEOrFChAKCeCsrOCsvuCsshUAGOrFChAKCeCstuCsvuCssBUAIOrFChAKCeCsueCssOCthxUAKOrFChAKCeCtqOCssOCthxUAMOrFChAKCeKWgeCsmuCsshUAOOrFChMKDOKWgeCsquCti+CslRUAQOrFChMKDOKWgeCtruCsn+CsvxUASOrFChYKD+CsmOCso+CtjeCsn+CsvhUAUOrFChYKD+CsquCssuCtjeCssuCtgBUAWOrFChYKD+KWgeCsmeCtjeCsleCssBUAYOrFChYKD+KWgeCsmuCtgeCsquCtjRUAaOrFChYKD+KWgeCsm+Cth+Css+CsvxUAcOrFChYKD+KWgeCsnOCti+CssOCtjRUAeOrFChYKD+KWgeCsueCti+Csh+CspRUAgOrFChkKEuKWgeCsieCsquCsreCti+CslxUAiOrFChkKEuKWgeCstuCtjeCssOCth+CtnxUAkOrFChkKEuKWgeCsuOCsvuCsqOCtjeCspBUAmOrFChkKEuKWgeCsuOCth+Cso+CtjeCsnxUAoOrFChwKFeKWgeCshuCsleCtg+Cst+CtjeCsnxUAqOrFCgoKA+KWgRUAsOrFCgoKA+CsvhUAuOrFCgoKA+CtjRUAwOrFCgoKA+CssBUAyOrFCgoKA+CsvxUA0OrFCgoKA+CslRUA2OrFCgoKA+CthxUA4OrFCgoKA+CsrBUA6OrFCgoKA+CsqBUA8OrFCgoKA+CspBUA+OrFCgoKA+CtgRUAAOvFCgoKA+CsuBUACOvFCgoKA+CsrhUAEOvFCgoKA+CsqhUAGOvFCgoKA+CsuRUAIOvFCgoKA+CsshUAKOvFCgoKA+CsphUAMOvFCgoKA+CtnxUAOOvFCgoKA+ClpBUAQOvFCgoKA+CshxUASOvFCgoKA+CtixUAUOvFCgoKA+CslxUAWOvFCgoKA+CspRUAYOvFCgoKA+CsoxUAaOvFCgoKA+CsnBUAcOvFCgoKA+CsnxUAeOvFCgoKA+CsjxUAgOvFCgoKA+CsthUAiOvFCgoKA+CtgBUAkOvFCgoKA+CshhUAmOvFCgoKA+CsrRUAoOvFCgoKA+CsrxUAqOvFCgoKA+CsmRUAsOvFCgoKA+CssxUAuOvFCgoKA+CshRUAwOvFCgoKA+CstxUAyOvFCgoKA+CsmxUA0OvFCgoKA+CspxUA2OvFCgoKA+CsoRUA4OvFCgoKA+CsmhUA6OvFCggKASwVAPDrxQoKCgPgrIkVAPjrxQoKCgPgrJYVAADsxQoKCgPgrIEVAAjsxQoKCgPgrIIVABDsxQoKCgPgrLwVABjsxQoKCgPgrJMVACDsxQoKCgPgrYIVACjsxQoKCgPgrKAVADDsxQoKCgPgrYMVADjsxQoKCgPgrKsVAEDsxQoKCgPgrJgVAEjsxQoKCgPgrbEVAFDsxQoKCgPgracVAFjsxQoKCgPgrJ4VAGDsxQoICgEnFQBo7MUKCgoD4K2oFQBw7MUKCAoBLRUAeOzFCgoKA+CtphUAgOzFCgoKA+CsohUAiOzFCggKAS4VAJDsxQoKCgPgrYwVAJjsxQoKCgPgrakVAKDsxQoKCgPgrasVAKjsxQoKCgPgraoVALDsxQoICgFlFQC47MUKCgoD4KyDFQDA7MUKCAoBaRUAyOzFCggKASkVANDsxQoICgEoFQDY7MUKCAoBYRUA4OzFCgoKA+CsnRUA6OzFCgoKA+CtrxUA8OzFCggKASIVAPjsxQoICgFuFQAA7cUKCAoBdBUACO3FCgoKA+CtrBUAEO3FCgoKA+CtiBUAGO3FCgoKA+CtrhUAIO3FCgoKA+CtrRUAKO3FCggKAW8VADDtxQoICgE/FQA47cUKCAoBchUAQO3FCggKAXMVAEjtxQoICgE6FQBQ7cUKCAoBbBUAWO3FCgoKA+CsiBUAYO3FCggKAWQVAGjtxQoICgEhFQBw7cUKCAoBaBUAeO3FCggKAW0VAIDtxQoICgFjFQCI7cUKCAoBdRUAkO3FCggKAWcVAJjtxQoICgE7FQCg7cUKCAoBeRUAqO3FCggKAXwVALDtxQoICgEvFQC47cUKCAoBYhUAwO3FCggKAXAVAMjtxQoICgExFQDQ7cUKCAoBZhUA2O3FCggKATIVAODtxQoKCgPgrIsVAOjtxQoICgEwFQDw7cUKCAoBXBUA+O3FCggKAXcVAADuxQoICgFBFQAI7sUKCAoBSRUAEO7FCggKAUQVABjuxQoICgFTFQAg7sUKCAoBUBUAKO7FCggKAUMVADDuxQoICgFrFQA47sUKCAoBdhUAQO7FCggKAVIVAEjuxQoICgFPFQBQ7sUKCgoD4KyUFQBY7sUKCAoBVBUAYO7FCggKAU4VAGjuxQoICgF6FQBw7sUKCAoBMxUAeO7FCggKAU0VAIDuxQoICgFqFQCI7sUKCAoBfhUAkO7FCggKAUIVAJjuxQoICgE1FQCg7sUKCAoBORUAqO7FCggKATQVALDuxQoKCgPgrIoVALjuxQoICgFRFQDA7sUKCgoD4KyQFQDI7sUKCAoBQBUA0O7FCggKATgVANjuxQoICgFIFQDg7sUKCAoBJRUA6O7FCggKAUwVAPDuxQoICgFFFQD47sUKCAoBRxUAAO/FCggKATYVAAjvxQoICgFGFQAQ78UKCAoBNxUAGO/FCgoKA+ClpRUAIO/FCgoKA+KAohUAKO/FCggKAXgVADDvxQoJCgLCsBUAOO/FCggKASoVAEDvxQoJCgLCtxUASO/FCggKAVcVAFDvxQoICgErFQBY78UKCAoBWxUAYO/FCggKAV0VAGjvxQoICgFLFQBw78UKCAoBcRUAeO/FCggKAUoVAIDvxQoICgFWFQCI78UKCgoD4Ky9FQCQ78UKCAoBWBUAmO/FCggKAVUVAKDvxQoICgE9FQCo78UKCgoD4K2YFQCw78UKCAoBPhUAuO/FCgkKAsO0FQDA78UKCgoD4KS+FQDI78UKCAoBWRUA0O/FCggKASYVANjvxQoICgEjFQDg78UKCgoD4KWHFQDo78UKCgoD4KSVFQDw78UKCgoD4KSwFQD478UKCgoD4KWNFQAA8MUKCgoD4KS4FQAI8MUKCAoBYBUAEPDFCgkKAsKpFQAY8MUKCAoBWhUAIPDFCggKAV8VACjwxQoKCgPgpIIVADDwxQoICgF7FQA48MUKCgoD4KSoFQBA8MUKCAoBfRUASPDFCgoKA+CkvxUAUPDFCgoKA+CkshUAWPDFCgoKA+CkuRUAYPDFCgkKAsOgFQBo8MUKCgoD4LGBFQBw8MUKCgoD4oaRFQB48MUKCgoD4KSqFQCA8MUKCgoD4KSvFQCI8MUKCgoD4KWAFQCQ8MUKCAoBPBUAmPDFCgoKA+CkpBUAoPDFCgoKA+CkrhUAqPDFCgoKA+ClixUAsPDFCgkKAsOoFQC48MUKCQoCw5cVAMDwxQoJCgLEgRUAyPDFCgoKA++4jxUA0PDFCgoKA+CkrBUA2PDFCgkKAsKuFQDg8MUKCQoCwrsVAOjwxQoKCgPgpKYVAPDwxQoKCgPgpJcVAPjwxQoKCgPgsKQVAADxxQoJCgLDghUACPHFCgoKA+CtoBUAEPHFCgkKAsuaFQAY8cUKCgoD4KS1FQAg8cUKCgoD4KS2FQAo8cUKCgoD4KS8FQAw8cUKCgoD4KWIFQA48cUKCgoD4p6hFQBA8cUKCQoCw6IVAEjxxQoKCgPgpJwVAFDxxQoKCgPgpKEVAFjxxQoKCgPgpYEVAGDxxQoKCgPinKEVAGjxxQoKCgPgsLAVAHDxxQoJCgLDiRUAePHFCgoKA+ClghUAgPHFCgoKA+KXjxUAiPHFCgoKA+KZphUAkPHFCgoKA+CkrRUAmPHFCgoKA+CtlhUAoPHFCgsKBPCfkYkVAKjxxQoICgEkFQCw8cUKCQoCxaEVALjxxQoKCgPgpJ8VAMDxxQoKCgPihakVAMjxxQoJCgLCuhUA0PHFCgkKAsOhFQDY8cUKCQoCw6wVAODxxQoKCgPgpJYVAOjxxQoKCgPgpJoVAPDxxQoJCgLDgRUA+PHFCgoKA+CkhhUAAPLFCgoKA+CkiRUACPLFCgoKA+ClsBUAEPLFCgoKA+CtohUAGPLFCgkKAsKrFQAg8sUKCQoCw5MVACjyxQoJCgLMihUAMPLFCgkKAsOAFQA48sUKCQoCw4gVAEDyxQoJCgLDtxUASPLFCgkKAsSrFQBQ8sUKCgoD4KSPFQBY8sUKCgoD4KWvFQBg8sUKCgoD4oGjFQBo8sUKCgoD4oaSFQBw8sUKCQoCw68VAHjyxQoJCgLKvBUAgPLFCgoKA+CkgxUAiPLFCgoKA+CkhRUAkPLFCgoKA+CkqxUAmPLFCgoKA+CwshUAoPLFCgoKA+G4uxUAqPLFCgoKA+KCuRUAsPLFCgkKAsOnFQC48sUKCQoCw7gVAMDyxQoKCgPgpKcVAMjyxQoKCgPgpLcVANDyxQoKCgPigYQVANjyxQoKCgPimpwVAODyxQoJCgLDkRUA6PLFCgkKAsOkFQDw8sUKCQoCw78VAPjyxQoKCgPgpIEVAADzxQoKCgPgpKAVAAjzxQoKCgPgpaYVABDzxQoKCgPgppUVABjzxQoKCgPgp40VACDzxQoKCgPigJUVACjzxQoKCgPihpAVADDzxQoLCgTwn6aLFQA488UKCAoBXhUAQPPFCgkKAsKzFQBI88UKCQoCw6sVAFDzxQoJCgLDvhUAWPPFCgkKAsWgFQBg88UKCgoD4KSHFQBo88UKCgoD4KSIFQBw88UKCgoD4KSlFQB488UKCgoD4KWnFQCA88UKCgoD4K2EFQCI88UKCgoD4K2XFQCQ88UKCgoD4bmHFQCY88UKCgoD4oCgFQCg88UKCgoD4oChFQCo88UKCgoD4payFQCw88UKCQoCwqYVALjzxQoJCgLCpxUAwPPFCgkKAsKqFQDI88UKCQoCwqwVANDzxQoJCgLCshUA2PPFCgkKAsOSFQDg88UKCQoCw5sVAOjzxQoJCgLDnBUA8PPFCgkKAsOmFQD488UKCQoCw6kVAAD0xQoJCgLDqhUACPTFCgkKAsSxFQAQ9MUKCQoCyZQVABj0xQoKCgPgpIoVACD0xQoKCgPgpJQVACj0xQoKCgPgpJ4VADD0xQoKCgPgpKMVADj0xQoKCgPgpYwVAED0xQoKCgPgpa4VAEj0xQoKCgPgppkVAFD0xQoKCgPguIgVAFj0xQoKCgPguJQVAGD0xQoKCgPguK4VAGj0xQoKCgPhuI0VAHD0xQoKCgPhuZsVAHj0xQoKCgPhuaAVAID0xQoKCgPigLkVAIj0xQoKCgPihaYVAJD0xQoKCgPilqoVAJj0xQoKCgPinKoVAKD0xQoKCgPinaQVAKj0xQoKCgPvuI4VALD0xQoLCgTwn5mPFQC49MUKCQoCwqEVAMD0xQoJCgLCsRUAyPTFCgkKAsK4FQDQ9MUKCQoCw4UVANj0xQoJCgLDixUA4PTFCgkKAsOQFQDo9MUKCQoCw5QVAPD0xQoJCgLDmRUA+PTFCgkKAsOaFQAA9cUKCQoCw50VAAj1xQoJCgLDnxUAEPXFCgkKAsOjFQAY9cUKCQoCw7AVACD1xQoJCgLDsRUAKPXFCgkKAsO2FQAw9cUKCQoCw7wVADj1xQoJCgLJmRUAQPXFCgkKAsygFQBI9cUKCQoCzrEVAFD1xQoJCgLOvBUAWPXFCgoKA+CkkBUAYPXFCgoKA+CkkxUAaPXFCgoKA+CkmBUAcPXFCgoKA+CkmxUAePXFCgoKA+ClgxUAgPXFCgoKA+CliRUAiPXFCgoKA+ClqBUAkPXFCgoKA+ClqhUAmPXFCgoKA+ClqxUAoPXFCgoKA+ClrBUAqPXFCgoKA+CmhRUAsPXFCgoKA+CmmhUAuPXFCgoKA+CmnhUAwPXFCgoKA+CmpxUAyPXFCgoKA+CmsBUA0PXFCgoKA+CmtxUA2PXFCgoKA+CmvxUA4PXFCgoKA+CngRUA6PXFCgoKA+CqnxUA8PXFCgoKA+CqpRUA+PXFCgoKA+CqsBUAAPbFCgoKA+CrjRUACPbFCgoKA+CsjBUAEPbFCgoKA+CwqBUAGPbFCgoKA+C4pxUAIPbFCgoKA+C4qhUAKPbFCgoKA+C4tBUAMPbFCgoKA+C4uBUAOPbFCgoKA+C4uRUAQPbFCgoKA+GAkhUASPbFCgoKA+GAkxUAUPbFCgoKA+GAnRUAWPbFCgoKA+GAuhUAYPbFCgoKA+GBihUAaPbFCgoKA+GemhUAcPbFCgoKA+G4jBUAePbFCgoKA+G5hRUAgPbFCgoKA+G5nxUAiPbFCgoKA+G5oRUAkPbFCgoKA+G7lRUAmPbFCgoKA+KAsBUAoPbFCgoKA+KAshUAqPbFCgoKA+KAvBUAsPbFCgoKA+KBuRUAuPbFCgoKA+KCghUAwPbFCgoKA+KDoxUAyPbFCgoKA+KFoxUA0PbFCgoKA+KFpBUA2PbFCgoKA+KFpxUA4PbFCgoKA+KJpBUA6PbFCgoKA+KRoxUA8PbFCgoKA+KSuBUA+PbFCgoKA+KZgBUAAPfFCgoKA+KZoRUACPfFCgoKA+KZpRUAEPfFCgoKA+KalhUAGPfFCgoKA+KdlxUAIPfFCgoKA+KdoxUAKPfFCgoKA+OAgRUAMPfFCgoKA+OAghUAOPfFCgsKBPCfjbMVAED3xQoLCgTwn4+5FQBI98UKCwoE8J+PuhUAUPfFCgsKBPC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-213\n▁ଭା\t-214\nନ୍ଧ\t-215\n▁କରୁ\t-216\n▁ଚା\t-217\n▁ବୋଲି\t-218\n▁ଲୋକ\t-219\nକ୍ର\t-220\n୍ତ୍ତ\t-221\nନି\t-222\n▁-\t-223\nଭା\t-224\nମି\t-225\nଶା\t-226\n▁ନାହିଁ\t-227\n▁ନିଜ\t-228\n▁ସର\t-229\n▁ସି\t-230\nଯା\t-231\nଟ୍\t-232\n▁କାର୍\t-233\n▁କୋ\t-234\n▁(\t-235\nଥାଏ\t-236\nର୍ମ\t-237\n▁ପ୍ରତି\t-238\n▁ଜି\t-239\nଯାଇଛି\t-240\nଟା\t-241\n▁ଲା\t-242\nନ୍ଦ୍ର\t-243\nତ୍ତ\t-244\nପଣ\t-245\nହିତ\t-246\n▁ଆସି\t-247\nକି\t-248\nୀର\t-249\nୟରେ\t-250\nଉଁ\t-251\nସା\t-252\nଗୁ\t-253\nିକା\t-254\n▁ପର\t-255\nଗି\t-256\nଥାନ\t-257\nନ୍ନ\t-258\n▁ହେଉ\t-259\n▁ନିର୍\t-260\nମ୍ଭ\t-261\n▁'\t-262\nଯୋଗ\t-263\nଷ୍ଠ\t-264\nପରି\t-265\n▁ରେ\t-266\n▁ଦିନ\t-267\nଞ୍\t-268\n▁କରିଥିଲେ\t-269\nାରଣ\t-270\n▁ବର୍ଷ\t-271\n▁ବିଶ\t-272\nଜ୍\t-273\nଚନା\t-274\n▁ମାନ\t-275\n▁ମନ\t-276\nପ୍ର\t-277\n▁କରିଛନ୍ତି\t-278\nକର\t-279\nଢ଼\t-280\n▁ତାଙ୍କ\t-281\n୍ୟୁ\t-282\n▁କାର୍ଯ୍ୟ\t-283\n▁ସରକାର\t-284\nାସ\t-285\nଳରେ\t-286\n▁ଜା\t-287\n▁ହେବ\t-288\n▁ସମସ୍ତ\t-289\nତ୍ର\t-290\nାରୀ\t-291\n▁୩\t-292\nିବ\t-293\nଧାନ\t-294\n▁ପି\t-295\n▁ରାଜ୍ୟ\t-296\n▁ସବୁ\t-297\nନ୍ତ୍ରୀ\t-298\n▁ଅଧିକ\t-299\nକ୍ତି\t-300\nପୁର\t-301\n▁ଅବ\t-302\n▁କିଛି\t-303\nଟର\t-304\n▁ଭାର\t-305\n▁ଶୁ\t-306\n▁ଆପଣ\t-307\nମାନଙ୍କ\t-308\n▁ଗ୍ର\t-309\n▁ଠ\t-310\n▁ନେ\t-311\n▁ତେବେ\t-312\n▁ଉପରେ\t-313\n▁ଆଉ\t-314\nଞ୍ଜ\t-315\nୃଷ୍\t-316\nଖା\t-317\n▁ଯୋଗ\t-318\n▁ସେହି\t-319\nଦେଶ\t-320\nଜେ\t-321\n▁ଲେ\t-322\n▁ପୂର୍\t-323\n▁ବୁ\t-324\nମୁ\t-325\n▁ଜିଲ୍\t-326\nମିତ\t-327\nଟେ\t-328\n▁କଥା\t-329\nସର\t-330\n▁ଓଡ଼ି\t-331\nର୍ଯ୍ୟ\t-332\nାତ୍ର\t-333\nିର\t-334\nଟିଏ\t-335\n▁ଦେଖି\t-336\nାନ୍\t-337\nେକ\t-338\n▁କିନ୍ତୁ\t-339\n▁ବେଳେ\t-340\n▁ଭାରତ\t-341\nାନ୍ତି\t-342\nୋଧ\t-343\nେଷ\t-344\n▁ଅନ୍ୟ\t-345\n▁ମୁଁ\t-346\n୍ନ\t-347\n▁ଯାଇ\t-348\nକ୍ଷା\t-349\n▁କ୍ଷ\t-350\nୀନ\t-351\n▁?\t-352\nବେଳେ\t-353\nସ୍ଥ\t-354\n▁ଦେଶ\t-355\nଦେ\t-356\n▁ଆର\t-357\n▁ସୂ\t-358\n▁ଅଭି\t-359\n▁ଆଜି\t-360\nୋଜ\t-361\n▁ସ୍ୱ\t-362\nର୍ଶ\t-363\nସୁ\t-364\nବାଦ\t-365\n▁ଥିବା\t-366\nଣୀ\t-367\n▁ଦୁଇ\t-368\n▁୫\t-369\nଠି\t-370\n▁ବ୍ୟବ\t-371\n▁ଯେଉଁ\t-372\n▁ଥ\t-373\nଣୁ\t-374\n▁୪\t-375\n▁କୌ\t-376\nଖ୍ୟ\t-377\n▁ରହିଛି\t-378\nଙ୍କା\t-379\n▁ଜୀ\t-380\n▁କରିବାକୁ\t-381\nଳ୍\t-382\n▁ଯୁ\t-383\n▁କହିଛନ୍ତି\t-384\n▁ଗା\t-385\n▁ମିଳି\t-386\nସନ\t-387\nନୀ\t-388\n▁ହେଲେ\t-389\nଜା\t-390\n୍ବ\t-391\n▁ଝ\t-392\n▁ମଧ୍ୟରେ\t-393\nାରା\t-394\n▁ଏବେ\t-395\nଚ୍ଚ\t-396\n▁ଜଣେ\t-397\nଣସି\t-398\nୋର୍\t-399\nକାଶ\t-400\nରଣ\t-401\nଗ୍ର\t-402\nସ୍ଥା\t-403\n▁ବହୁ\t-404\n▁ଘର\t-405\nଳ୍ପ\t-406\n▁ହୋଇଛି\t-407\nଠାରେ\t-408\n▁ସହିତ\t-409\nିବା\t-410\n▁ବର\t-411\n▁ପୂର୍ବ\t-412\n▁ଲାଗି\t-413\n▁ରୁ\t-414\n▁ଅର୍\t-415\n▁ସ୍ଥାନ\t-416\n▁ଏଥି\t-417\nୋଷ\t-418\n▁କୌଣସି\t-419\nଡ଼ା\t-420\n▁ଯାହା\t-421\nବଳ\t-422\nମନ୍ତ୍ରୀ\t-423\nଣ୍ଟ\t-424\n▁୨୦\t-425\n▁ଭୁ\t-426\n▁ଶ୍ରୀ\t-427\n▁ତାହା\t-428\nଣ୍ଣ\t-429\n▁ମହା\t-430\nର୍କ\t-431\n▁ଜିଲ୍ଲା\t-432\n▁କଲେ\t-433\n▁ଗୋଟିଏ\t-434\n▁ରଖି\t-435\nୃଷ୍ଟି\t-436\nଦା\t-437\n୍ଲ\t-438\n▁ରି\t-439\nପର\t-440\n▁ମେ\t-441\n▁\"\t-442\nବାରୁ\t-443\n▁ହୁ\t-444\n▁ଆମ\t-445\nିକ୍ଷ\t-446\nଭାବ\t-447\nପାରି\t-448\nାଇଁ\t-449\n▁ସମ୍ପ\t-450\n▁ସମୟରେ\t-451\nଗତ\t-452\nୂଆ\t-453\n▁ସୂଚନା\t-454\nନ୍ତ୍ର\t-455\n▁ମୃତ\t-456\nଛୁ\t-457\nକେ\t-458\n▁ମୁଖ୍ୟ\t-459\n▁ଚି\t-460\nଜାର\t-461\nୂର\t-462\n▁ପ୍ରଥ\t-463\n▁ତୁ\t-464\nଲ୍ଲ\t-465\n▁ହି\t-466\n▁ପ୍ରା\t-467\n୍ୱର\t-468\nମାନଙ୍କୁ\t-469\nୟୁ\t-470\n▁ଆରମ୍ଭ\t-471\nୂପ\t-472\nୟା\t-473\nତ୍ୟ\t-474\n▁ଜୀବନ\t-475\nଚ୍\t-476\n▁ଘଟ\t-477\nଠାରୁ\t-478\nବାସ\t-479\n▁ଭଲ\t-480\nପ୍ତ\t-481\n▁ଭାବେ\t-482\n▁ନୁ\t-483\nୀତ\t-484\nଟରେ\t-485\nସେ\t-486\n▁ଅନେକ\t-487\n▁କରାଯାଇ\t-488\nୋର\t-489\nଶି\t-490\n▁ସାମ\t-491\n▁ଅପ\t-492\nଯାଉ\t-493\nଯିବ\t-494\n▁ମହ\t-495\n▁ପକ୍ଷ\t-496\n▁ଆମେ\t-497\nହେଁ\t-498\n▁କରାଯାଇଛି\t-499\nକ୍ରମ\t-500\nୀଙ୍କ\t-501\nଗର\t-502\n▁ଖବର\t-503\nପତି\t-504\nଭୁ\t-505\n▁କାମ\t-506\nବ୍\t-507\nତନ\t-508\n▁ଦଳ\t-509\n▁ଶି\t-510\nଡା\t-511\nିକ୍\t-512\n▁ଭାବ\t-513\n▁ବୃ\t-514\n▁ହେବା\t-515\nଦାନ\t-516\n୍ରି\t-517\n▁ଡା\t-518\n▁ତାଙ୍କର\t-519\nଚନ\t-520\nଖରେ\t-521\n▁କେତେ\t-522\nଜ୍ଞ\t-523\nଜନ\t-524\n▁ତାଙ୍କୁ\t-525\n▁ଲକ୍ଷ\t-526\nମ୍ପ\t-527\n▁ଟଙ୍କା\t-528\n▁କର୍ମ\t-529\n▁କାରଣ\t-530\n▁ଭି\t-531\nଲୋ\t-532\nିନି\t-533\nଯାଏ\t-534\n▁ସର୍\t-535\n▁ବ୍ୟା\t-536\n▁ଆଗ\t-537\n▁ଦିଆ\t-538\n▁ଭିତ\t-539\nଶ୍ଚ\t-540\n▁ଥିଲେ\t-541\n▁!\t-542\nଣ୍ଡି\t-543\nତ୍ୟା\t-544\n▁ବିଷ\t-545\n▁ବିଜେ\t-546\n▁ଉତ୍\t-547\nଦ୍ୟ\t-548\n▁:\t-549\n▁ଫେ\t-550\nୋନା\t-551\nଲୁ\t-552\n▁ହୋଇଥିବା\t-553\nଯୁ\t-554\nଳନ\t-555\nଦ୍ର\t-556\nଭିନ୍ନ\t-557\nମାର\t-558\nାର୍ଥ\t-559\nପାଇଁ\t-560\n▁ଗତ\t-561\n▁ଗୁରୁ\t-562\nଶ୍ୟ\t-563\nପ୍\t-564\n▁ଶିକ୍ଷ\t-565\n▁୬\t-566\n▁କରିଛି\t-567\n▁ସ୍ବ\t-568\nଦନ\t-569\n▁ଟି\t-570\nରୀ\t-571\n▁ବିଭିନ୍ନ\t-572\nଦ୍ଧି\t-573\n▁ପଦ\t-574\nଶୁ\t-575\nହଁ\t-576\n▁ନାମ\t-577\n▁ସେମାନେ\t-578\n▁ପର୍ଯ୍ୟ\t-579\nସ୍ଥି\t-580\n▁ହେଉଛି\t-581\n▁ପ୍ରକାଶ\t-582\nଟକ\t-583\n▁ବଡ଼\t-584\nମ୍ବା\t-585\n▁ଖୁ\t-586\n▁ପ୍ରଶ\t-587\n▁ଏହାର\t-588\nନ୍ତା\t-589\n▁ବୈ\t-590\n▁ଉତ୍ତ\t-591\n▁ଆଇ\t-592\nଭଳି\t-593\n▁ତଥା\t-594\n▁ଯଦି\t-595\n▁ପୋଲି\t-596\nପରେ\t-597\nତିକ\t-598\n▁ପ୍ରଧାନ\t-599\nସ୍ତୁ\t-600\n▁ଦ୍ୱ\t-601\n▁ସମା\t-602\nଶ୍ର\t-603\nଳୟ\t-604\n▁ଡି\t-605\n▁ହୋଇଥିଲା\t-606\nବନେଶ\t-607\n▁ଜନ\t-608\n▁୭\t-609\nଦୀ\t-610\n▁ନୁହେଁ\t-611\nଗା\t-612\n▁ହୁଏ\t-613\n▁କରୋନା\t-614\nୟକ\t-615\nବୀ\t-616\nପୁ\t-617\n▁ପଡ଼ି\t-618\n▁ମହି\t-619\n▁ଜଣା\t-620\nଲରେ\t-621\n▁ପ୍ରଥମ\t-622\n▁ଗ୍ରାମ\t-623\n▁ଅଭ\t-624\nାଇବା\t-625\nିଆ\t-626\nମାଣ\t-627\n▁ପହ\t-628\n▁ପକ୍ଷରୁ\t-629\n▁ଖା\t-630\n▁ନୂଆ\t-631\n▁କେନ୍ଦ୍ର\t-632\n▁ଥିଲା\t-633\nିତା\t-634\n୍ମ\t-635\n▁ବ୍ୟକ୍ତି\t-636\n▁ସାଧ\t-637\n▁ଚାଲି\t-638\n▁ପୁଣି\t-639\nଲ୍ଲୀ\t-640\nୁଲ\t-641\n▁ସମୟ\t-642\nଚିତ\t-643\n▁ସ୍ତ\t-644\nପାରେ\t-645\n▁ପୂ\t-646\n▁ନିର୍ବା\t-647\n▁୧୦\t-648\nଜର\t-649\n▁ଖେ\t-650\n▁ସୋ\t-651\n▁ହାତ\t-652\nହଣ\t-653\nଚ୍ଛ\t-654\n▁ଜାଣି\t-655\nମାନଙ୍କର\t-656\n▁ଚେ\t-657\n▁ଶେଷ\t-658\n▁ମୃତ୍ୟୁ\t-659\n▁ବିର\t-660\n▁ପୂର୍ବରୁ\t-661\nଦିନ\t-662\n▁ପରିବାର\t-663\n▁ପାରି\t-664\n▁ହେଲା\t-665\n▁ବୋ\t-666\n▁ବିଭା\t-667\nବତ\t-668\nଧା\t-669\n▁କେବଳ\t-670\nମରେ\t-671\n▁ମହିଳା\t-672\nବ୍ୟ\t-673\n▁ପର୍ଯ୍ୟନ୍ତ\t-674\nଳୀ\t-675\nସରେ\t-676\nମିତି\t-677\n▁ଘଟଣା\t-678\n▁ହଜାର\t-679\nତୁ\t-680\nଥିବାରୁ\t-681\n▁ଅଭିଯୋଗ\t-682\nଡ୍\t-683\nମର\t-684\n▁ନିକ\t-685\n▁ତେଣୁ\t-686\n▁ଦୁର୍\t-687\nଦ୍ଦେଶ\t-688\n▁ପୋଲିସ\t-689\nବେଶ\t-690\n▁ଟେ\t-691\nଖ୍ୟା\t-692\nଦ୍ଧା\t-693\nନ୍ଦର\t-694\n▁୮\t-695\nଂଗ\t-696\n▁ପାଣି\t-697\nଆଇ\t-698\n▁ଏହାକୁ\t-699\nଞ୍ଚି\t-700\nନେ\t-701\n▁ଦେଇଛନ୍ତି\t-702\nବର୍ତ୍ତ\t-703\nିକେ\t-704\n▁ଅଞ୍ଚ\t-705\n▁କରିବେ\t-706\n▁ସୃଷ୍ଟି\t-707\n▁ଯାଉ\t-708\n▁ସ୍ବା\t-709\n▁ମୂ\t-710\n▁ତାରି\t-711\n▁ଆସ\t-712\n▁ପ୍ରକ\t-713\n▁ଷ୍ଟ\t-714\n▁,\t-715\nପୋର୍\t-716\nଫଳ\t-717\n▁ଦେଖ\t-718\n▁ଧରି\t-719\n▁ବର୍ତ୍ତ\t-720\n▁କୁହା\t-721\n▁ଅଛି\t-722\nମୀ\t-723\nରରେ\t-724\n▁୧୯\t-725\n▁ଦେଖା\t-726\n▁ପୁର\t-727\n▁ଫି\t-728\n▁ମାମ\t-729\n▁ମୋର\t-730\n▁ଢ\t-731\nଣ୍ଡା\t-732\n▁ଭୂ\t-733\nଜୁ\t-734\nମିକ\t-735\nାୟୀ\t-736\n▁କରନ୍ତୁ\t-737\n▁ନିଜର\t-738\nଗ୍\t-739\nନର\t-740\n▁କ୍ର\t-741\n▁ଜେ\t-742\n▁ରୂପ\t-743\nସ୍ୟ\t-744\nତିରେ\t-745\nଚା\t-746\nଂଶ\t-747\n▁ସମ୍ପର୍କ\t-748\n▁ସଙ୍ଗ\t-749\nବାହ\t-750\nସ୍କ\t-751\n▁ଓଡ଼ିଶା\t-752\n▁ବର୍ତ୍ତମାନ\t-753\n▁ଏପରି\t-754\nପାଳ\t-755\n▁ପୋ\t-756\n▁୯\t-757\n▁ଗାଁ\t-758\n▁ଅନ୍ତ\t-759\nବନେଶ୍ୱର\t-760\n▁କୁମାର\t-761\nମ୍ବର\t-762\n▁ଭିତରେ\t-763\nତେବେଳେ\t-764\nାନା\t-765\n▁ପ୍ରେ\t-766\n▁ସ୍ଥି\t-767\nନାଥ\t-768\nଦ୍ୟା\t-769\n▁ବିରୋଧ\t-770\n୍ବର\t-771\nଳିତ\t-772\nଡ଼ିକ\t-773\n▁ନିର୍ବାଚନ\t-774\nଯାଉଛି\t-775\n▁ଦେବ\t-776\n▁ବଡ\t-777\n▁ସ୍ୱା\t-778\n▁ରିପୋର୍\t-779\n▁ଶା\t-780\nଥର\t-781\nଥାନ୍ତି\t-782\nେତ୍ର\t-783\n▁ବନ୍ଧ\t-784\n▁ବହୁତ\t-785\nଶୀ\t-786\n▁ବିଶ୍ୱ\t-787\nଜଣ\t-788\nାବି\t-789\n▁ଅଫ\t-790\nଳୁ\t-791\n▁ପ୍ରାୟ\t-792\n▁ଅଧିକାରୀ\t-793\n▁ଆବ\t-794\n▁ଅସ\t-795\nହ୍ନ\t-796\nଦର୍ଶ\t-797\n▁ଦ୍\t-798\n▁କମି\t-799\nମୁଖ\t-800\nବାନ\t-801\nକରେ\t-802\nତର\t-803\n▁ଅତି\t-804\n▁ଲୁ\t-805\n▁ହୋଇଛନ୍ତି\t-806\nକ୍ତର\t-807\nଂଗ୍ରେ\t-808\n▁ବୃଦ୍ଧି\t-809\nବନ୍ଧ\t-810\n▁ଗ୍ରହଣ\t-811\n▁ସିଂ\t-812\n▁ଏଥିରେ\t-813\n▁ସ୍କୁ\t-814\n▁ରୋଗ\t-815\nସ୍ତ୍ର\t-816\nଳର\t-817\nୂର୍ଣ୍ଣ\t-818\n▁ମୁଖ୍ୟମନ୍ତ୍ରୀ\t-819\n▁ସେମାନଙ୍କ\t-820\n▁ନିୟ\t-821\n▁ଅର୍ଥ\t-822\nଦେଇ\t-823\nୋଟ\t-824\nଷ୍ଟ୍ର\t-825\n▁କଂଗ୍ରେ\t-826\n▁ପେ\t-827\nଯୁକ୍ତ\t-828\nସାରେ\t-829\nନ୍ଦିର\t-830\nିଅ\t-831\nନାହିଁ\t-832\nଭେ\t-833\n▁ଘଟି\t-834\n▁ଥାନ\t-835\n..\t-836\nଯ୍ୟା\t-837\nସ୍ଥ୍ୟ\t-838\n▁ଛା\t-839\n▁ବସି\t-840\n▁ଦେଇଥିଲେ\t-841\n▁ହିଁ\t-842\nର୍ଚ୍ଚ\t-843\n▁ମାତ୍ର\t-844\nଚାର\t-845\n▁ପଡି\t-846\nନରେ\t-847\n▁ପ୍ରସ୍ତୁ\t-848\n▁ଜଳ\t-849\n▁ଯୋଜ\t-850\n▁କୋଟି\t-851\nଆର\t-852\nର୍ଥ\t-853\nେତ\t-854\nଆରେ\t-855\nିତ୍\t-856\n▁ଲି\t-857\n▁ୱ\t-858\n▁ଏଠାରେ\t-859\n▁ଭାବରେ\t-860\n▁କରିଥିବା\t-861\n▁ସୁର\t-862\nଶ୍ୟକ\t-863\n▁କହିଲେ\t-864\n▁ବିଭାଗ\t-865\n▁ବିଷୟରେ\t-866\nan\t-867\n▁ବନ୍ଦ\t-868\nଲାର\t-869\n▁ନବ\t-870\n▁ହୋଇଥାଏ\t-871\n▁ବିଜେପି\t-872\n▁ଦା\t-873\nକାରୀ\t-874\n▁କରୁଛନ୍ତି\t-875\n▁ଦେବା\t-876\nଠା\t-877\n▁ଦର\t-878\nାତ୍ରୀ\t-879\n▁ଆଣି\t-880\n▁ବୁଝ\t-881\nଜୟ\t-882\nଦର\t-883\n▁ଭାଇ\t-884\nଗଲା\t-885\nଭବ\t-886\n▁ହା\t-887\n▁ବିଶେଷ\t-888\nମ୍ବ\t-889\n▁|\t-890\nୃତି\t-891\n୦୦\t-892\n▁ପରୀ\t-893\nମୋ\t-894\n▁ଦାସ\t-895\nଭୂ\t-896\nଢ଼ି\t-897\nହିତ୍ୟ\t-898\n▁ପ୍ରଦାନ\t-899\n▁ଦେଖିବାକୁ\t-900\n▁ଭଳି\t-901\n▁ଯୁବ\t-902\nକ୍ରମଣ\t-903\nିୟ\t-904\nାନୁ\t-905\nଭର\t-906\n▁କରେ\t-907\n▁ମାମଲା\t-908\n▁ଘରେ\t-909\nଲୋଚନା\t-910\nଟୋ\t-911\n▁ପୁଲି\t-912\n▁ଶ୍ର\t-913\n▁କେଉଁ\t-914\nଧିକ\t-915\n▁ମଣି\t-916\n▁ଦୂର\t-917\n▁ଆୟ\t-918\n▁ବ୍ର\t-919\n▁ସେହିପରି\t-920\n▁ସାଧାରଣ\t-921\nଗରେ\t-922\nନ୍ୟ\t-923\n▁ଦୀ\t-924\n▁ୟୁ\t-925\n▁ବେଶ\t-926\n▁ସଂଖ୍ୟା\t-927\nୀରେ\t-928\n▁ଜାରି\t-929\n▁କରନ୍ତି\t-930\nଧି\t-931\nଚି\t-932\n▁ଯୋଜନା\t-933\n▁ଧର୍ମ\t-934\nଲର\t-935\nହର\t-936\n▁ଆବଶ୍ୟକ\t-937\nବର୍ଷ\t-938\n▁ମନ୍ତ୍ରୀ\t-939\nର୍ଣ୍ଣ\t-940\n▁କରୁଥିବା\t-941\n▁ପହଞ୍ଚି\t-942\n▁ଏଭଳି\t-943\n▁ଗୃ\t-944\n▁ଜଣାପ\t-945\nଙ୍ଗା\t-946\n▁ସ୍ପ\t-947\n▁ଝିଅ\t-948\n▁ଯୋଗୁ\t-949\n▁ଉତ୍ତର\t-950\n▁ଉଚ୍ଚ\t-951\n▁କ୍ଷେତ୍ର\t-952\nବରେ\t-953\n▁t\t-954\nଗୁଡ଼ିକ\t-955\n▁ଭାରତୀୟ\t-956\n▁ପିଲା\t-957\n▁ଗଣ\t-958\nଳକୁ\t-959\n▁ଆଦି\t-960\nପକ୍ଷ\t-961\nଜୀ\t-962\n▁ଶର\t-963\n▁ପଞ୍ଚ\t-964\n▁ହୋଇଥିଲେ\t-965\nଂସ\t-966\n▁ଏଇ\t-967\n▁ନେତା\t-968\n▁ପୃ\t-969\nଡେ\t-970\n▁କରିବ\t-971\n▁ଓଡ଼ିଆ\t-972\n▁ବ୍ୟବହାର\t-973\n▁ଶୁଣି\t-974\nବାସୀ\t-975\nାତ୍ରା\t-976\n▁କୃ\t-977\nଷ୍ଟା\t-978\n▁ଅଭିନ\t-979\n▁ଘୋଷ\t-980\nଳକ\t-981\n▁ସାହିତ୍ୟ\t-982\nଗଡ଼\t-983\n▁କଣ\t-984\n▁ବାର\t-985\n▁ଭର\t-986\nତ୍ତି\t-987\n▁ବ୍ଲ\t-988\n▁ମନେ\t-989\n▁ମ୍ୟା\t-990\n▁ନିର୍ଦ୍ଦେଶ\t-991\nହନ\t-992\n▁ମାସ\t-993\nନ୍ୟା\t-994\n▁ଦ୍ୱାରା\t-995\n▁ସେଥି\t-996\n▁ତାକୁ\t-997\n▁କରାଯାଇଥିଲା\t-998\n▁ପ୍ରଶ୍ନ\t-999\n▁ଠାରୁ\t-1000\n▁ଡାକ୍ତର\t-1001\n▁ରାଜ୍ୟରେ\t-1002\nାତା\t-1003\nti\t-1004\nପାର\t-1005\n▁କଂଗ୍ରେସ\t-1006\n▁ଆସୁ\t-1007\nଠୁ\t-1008\n▁ଦ୍ବାର\t-1009\nଦାର\t-1010\nୃଷ୍ଣ\t-1011\n▁ଏନ\t-1012\nଷା\t-1013\n▁ସ୍ଥାନରେ\t-1014\n▁ପ୍ରଧାନମନ୍ତ୍ରୀ\t-1015\nୟନ\t-1016\nନାୟକ\t-1017\n▁କଲ\t-1018\n▁ପର୍\t-1019\n▁କାର୍ଯ୍ୟକ୍ରମ\t-1020\n▁ପ୍ରସ\t-1021\n▁ପାଇବା\t-1022\n▁ବାହାର\t-1023\n▁ଦର୍ଶ\t-1024\n▁ସର୍ବ\t-1025\nଧୁ\t-1026\nଷ୍ପ\t-1027\nାନୀ\t-1028\n▁ମାନେ\t-1029\n▁ବୟ\t-1030\n▁ପୁଅ\t-1031\n▁ସାମ୍\t-1032\n▁ଅନୁସାରେ\t-1033\n▁ଉଭ\t-1034\n▁ଦୃଷ୍ଟି\t-1035\n▁କେହି\t-1036\nଣ୍ଟା\t-1037\n▁ପାଖରେ\t-1038\n▁ଫଳରେ\t-1039\n▁ଅଛନ୍ତି\t-1040\n▁ଅବସ୍ଥା\t-1041\n▁ପ୍ରକାର\t-1042\n▁କଟକ\t-1043\n▁ସଭା\t-1044\nରାମ\t-1045\nନ୍ମ\t-1046\nହ୍ମ\t-1047\n▁ତାର\t-1048\n▁ମିଳିଛି\t-1049\n▁୨୦୧\t-1050\n▁ଚାଷ\t-1051\n▁ପାର\t-1052\n▁ପୁରୁ\t-1053\nଲ୍ୟ\t-1054\nଯାୟୀ\t-1055\nିତ୍ର\t-1056\nସ୍ୟା\t-1057\n▁କିମ୍ବା\t-1058\n▁ଓଡ଼ିଶ\t-1059\nକରଣ\t-1060\n▁କାର\t-1061\n▁ବ୍ୟବସ୍ଥା\t-1062\nକାଳ\t-1063\n▁ପାଠ\t-1064\n▁ନିକଟରେ\t-1065\nସ୍ଥିତ\t-1066\n▁ଯି\t-1067\ned\t-1068\nିଲ\t-1069\n▁ମତ\t-1070\n▁ଋ\t-1071\nପନ\t-1072\nକ୍ସ\t-1073\n▁ସେଇ\t-1074\n▁ଏସ\t-1075\nିମ\t-1076\n▁ଏକା\t-1077\nଧାର\t-1078\n▁ଆପଣଙ୍କ\t-1079\n▁ଲଗ\t-1080\n▁ଆଲୋଚନା\t-1081\n▁ପଢ\t-1082\n▁ମନ୍ଦିର\t-1083\n▁କରାଯିବ\t-1084\n▁୧୨\t-1085\n▁ନିଆ\t-1086\nୋଟି\t-1087\nକଙ୍କ\t-1088\n▁ଚନ୍ଦ୍ର\t-1089\nଦେବ\t-1090\nମନ୍ତ\t-1091\n▁ଅନୁଷ୍ଠ\t-1092\nଷ୍ୟ\t-1093\n▁କଳା\t-1094\n▁ରହିବ\t-1095\n▁ଦେଶର\t-1096\n▁ସାହା\t-1097\n▁ବିକ\t-1098\n▁ପାର୍\t-1099\nସ୍ତା\t-1100\nଟ୍ଟ\t-1101\n▁ସଫଳ\t-1102\n▁କହିଥିଲେ\t-1103\n▁ଭେ\t-1104\nନିକ\t-1105\n▁ଫିଲ୍\t-1106\n▁ପ୍ରତ୍ୟ\t-1107\n▁ସଂପ\t-1108\n▁ପ୍ରସ୍ତୁତ\t-1109\n▁ଚିକ\t-1110\nଷଣ\t-1111\n▁ଲୋକଙ୍କ\t-1112\n▁ପ୍ରଭାବ\t-1113\n▁ଦେଶରେ\t-1114\nନୁ\t-1115\n▁ଗିର\t-1116\n▁କମ୍ପ\t-1117\nପାରିବେ\t-1118\ner\t-1119\nଯାଇଥିଲା\t-1120\n▁ଦୃ\t-1121\n▁ସରକାରଙ୍କ\t-1122\nଡ଼େ\t-1123\n▁ବିଧ\t-1124\n▁ମଣିଷ\t-1125\nାମୀ\t-1126\nୟାର\t-1127\n▁ଆତ୍\t-1128\n▁ଘୋଷଣା\t-1129\n▁ହେବାକୁ\t-1130\nଦନ୍ତ\t-1131\n▁ବର୍ଷା\t-1132\nin\t-1133\n▁ସେଠାରେ\t-1134\nଥୁ\t-1135\nକ୍ରାନ୍ତ\t-1136\n▁ଦେଉ\t-1137\nଷି\t-1138\n୍ତ୍ତି\t-1139\nହାନ୍ତି\t-1140\nଶତ\t-1141\nଚେ\t-1142\n▁୧୮\t-1143\n▁୧୫\t-1144\n▁କବ\t-1145\nଖୁ\t-1146\nଚାରୀ\t-1147\n▁ଛାତ୍ର\t-1148\n▁କରିଥିଲା\t-1149\n▁ଅନୁଯାୟୀ\t-1150\n▁ଚିକିତ୍\t-1151\n▁ଉଭୟ\t-1152\n▁ଦାବି\t-1153\n▁ବିବାହ\t-1154\nକ୍ଷେ\t-1155\n▁ଗିରଫ\t-1156\n▁ପକ\t-1157\n▁କ୍ୟା\t-1158\n▁।\"\t-1159\n▁କାହା\t-1160\nଠନ\t-1161\n▁ଦୁଃ\t-1162\nରକୁ\t-1163\n▁ଖୋ\t-1164\n▁ଆର୍\t-1165\n▁ସଭ\t-1166\n▁ବନ୍ଧୁ\t-1167\nତାର\t-1168\nଶାସନ\t-1169\n▁ମୋତେ\t-1170\n▁ଶିକ୍ଷା\t-1171\nକ୍ଷ୍\t-1172\nଡିଓ\t-1173\nାୟକ\t-1174\n▁ହେବେ\t-1175\n▁ସମସ୍ୟା\t-1176\n▁ବଢ\t-1177\nଗ୍ରହ\t-1178\n▁ଅଧ୍ୟ\t-1179\n▁ତାରିଖ\t-1180\nଯିବା\t-1181\n▁ଥାନା\t-1182\nଜ୍ୟ\t-1183\n▁ଭଗ\t-1184\n▁ହର\t-1185\nଗ୍ରା\t-1186\n▁ଲେଖି\t-1187\n▁ଚିହ୍ନ\t-1188\n▁ସ୍କୁଲ\t-1189\nଳନା\t-1190\n▁ଜାତ\t-1191\nଦିଲ୍ଲୀ\t-1192\n▁ମସି\t-1193\n▁ପଛ\t-1194\n▁ଖାଇ\t-1195\nଂଚ\t-1196\n):\t-1197\n▁ସାହୁ\t-1198\n▁ସୁନ୍ଦର\t-1199\n▁କେବେ\t-1200\n▁କହ\t-1201\n▁କ୍ଷେତ୍ରରେ\t-1202\n▁କର୍ମଚାରୀ\t-1203\n▁ଉଠି\t-1204\n▁ଏତେ\t-1205\n▁ଶବ୍\t-1206\nଦୁ\t-1207\n▁ଶକ୍ତି\t-1208\nଗଣ\t-1209\nହାରି\t-1210\n▁ହୃ\t-1211\n▁ହାଇ\t-1212\n▁ସମସ୍ତେ\t-1213\n▁ପରୀକ୍ଷା\t-1214\non\t-1215\nଦ୍ୟାଳୟ\t-1216\nଟ୍ର\t-1217\nସିକ\t-1218\n▁ଲୋକେ\t-1219\n▁ତଦନ୍ତ\t-1220\n▁ଜନ୍ମ\t-1221\nବଲ\t-1222\n▁ପ୍ରାର୍ଥ\t-1223\n▁ଫେରି\t-1224\n▁ଛୁ\t-1225\n▁ସହା\t-1226\nସିଆ\t-1227\n▁କରୁଛି\t-1228\nତଃ\t-1229\n▁ପୁଲିସ\t-1230\n▁ସେମାନଙ୍କୁ\t-1231\n▁ସ୍ୱାସ୍ଥ୍ୟ\t-1232\nିକାଶ\t-1233\nଠାଇ\t-1234\n▁ସମ୍ପର୍କରେ\t-1235\nତାବ\t-1236\nଗଲେ\t-1237\n▁୧୧\t-1238\nହାରେ\t-1239\n▁କାହିଁ\t-1240\nପେ\t-1241\n▁b\t-1242\nିତୀୟ\t-1243\nନ୍ଦୁ\t-1244\nhe\t-1245\nଗୋ\t-1246\n▁ସଦ\t-1247\n▁ସାହାଯ୍ୟ\t-1248\n▁ବ୍ୟବସ\t-1249\n▁ବିଜୟ\t-1250\nରିକ\t-1251\nନୂଆ\t-1252\n▁ବଦ\t-1253\n▁ସତ\t-1254\n▁ମିଶ୍ର\t-1255\n▁କହୁ\t-1256\nସିଂ\t-1257\n▁ଲାଭ\t-1258\nପଟେ\t-1259\n▁ଜଗ\t-1260\n▁ସରକାରୀ\t-1261\nଲକ\t-1262\n୍ଲା\t-1263\n▁ନୂ\t-1264\nଦ୍ଧାର\t-1265\n▁ଜମି\t-1266\n▁ପଟ୍ଟ\t-1267\n▁ବିକାଶ\t-1268\nଣ୍ଠ\t-1269\nଘର\t-1270\nଡୁ\t-1271\n▁ଉଚିତ\t-1272\n▁ନୀ\t-1273\nପକ\t-1274\n▁ଦୁଇଟି\t-1275\n▁ପ୍ରଶାସନ\t-1276\n▁s\t-1277\n▁ଡେ\t-1278\n୨୦\t-1279\n▁ବାପା\t-1280\n▁ଆକ୍ରାନ୍ତ\t-1281\nସ୍ତାନ\t-1282\n▁ସେବା\t-1283\n▁ସୁରକ୍ଷା\t-1284\n▁କେତେକ\t-1285\n▁ଅନ୍ତର୍\t-1286\n▁ସହର\t-1287\n▁ସୁଦ୍ଧା\t-1288\nସଂ\t-1289\n▁ପାଖ\t-1290\n▁ମୁହଁ\t-1291\n▁ଅଫି\t-1292\n▁ଦ୍ବାରା\t-1293\nଜରେ\t-1294\nତନ୍ତ୍ର\t-1295\n▁ପ୍ରଥମେ\t-1296\n▁ରୋ\t-1297\nଫ୍\t-1298\nଜ୍ଞାନ\t-1299\nାନି\t-1300\nଚ୍ଛା\t-1301\n▁ସଙ୍ଗେ\t-1302\n▁ଯାଏ\t-1303\nସ୍ପି\t-1304\n▁ଦିଅ\t-1305\nସ୍କାର\t-1306\n▁ସିଂହ\t-1307\n▁ଏମିତି\t-1308\n▁ଶ୍ରେ\t-1309\n▁ଚଳ\t-1310\n▁ଅଶ\t-1311\n▁ବୈଠ\t-1312\n▁କ୍ଲ\t-1313\n▁କରିବାର\t-1314\n▁ଅବସର\t-1315\n▁ଉନ୍ନ\t-1316\n▁ଆସନ୍ତା\t-1317\nଦର୍ଶନ\t-1318\n▁ତିନି\t-1319\n▁ମାଧ୍ୟ\t-1320\n▁ଶରୀର\t-1321\n▁ଠିକ୍\t-1322\n▁ମଧ୍ୟରୁ\t-1323\n▁ଟା\t-1324\n▁ଆହ\t-1325\n▁ଖାଦ୍ୟ\t-1326\n▁ଗୃହ\t-1327\nକରି\t-1328\n▁ଲେଖା\t-1329\nାଇଛନ୍ତି\t-1330\n▁ଆକ\t-1331\n▁ସୀ\t-1332\nୁରୋ\t-1333\nତୀ\t-1334\n▁ନୂତନ\t-1335\n▁ଇଂ\t-1336\n▁ଓଡି\t-1337\n▁ଆଦ\t-1338\n▁ଫୁ\t-1339\n▁ଲାଇ\t-1340\n▁କୋର୍\t-1341\n▁ଟ୍ର\t-1342\nଇଁ\t-1343\nବିଧା\t-1344\n▁ଥର\t-1345\nାନ୍ଧ\t-1346\n▁ସ୍ତ୍ରୀ\t-1347\n▁ଆପଣଙ୍କୁ\t-1348\nଙ୍କାର\t-1349\n▁ଲୋକଙ୍କୁ\t-1350\nବର୍ତ୍ତନ\t-1351\nଭୁବନେଶ୍ୱର\t-1352\n▁ଚିହ୍ନଟ\t-1353\nସୀ\t-1354\n▁କରୁଥିଲେ\t-1355\n▁ଶାସ\t-1356\nପାଦ\t-1357\n▁ନିର୍ମାଣ\t-1358\nଣ୍ଟି\t-1359\n▁କମ\t-1360\n▁ଜ୍\t-1361\n▁ନଜର\t-1362\nନୂଆଦିଲ୍ଲୀ\t-1363\nଏସ\t-1364\n▁ଯେତେବେଳେ\t-1365\nପାରିବ\t-1366\n▁ଭିଡିଓ\t-1367\n▁ଭୁବନେଶ୍ୱର\t-1368\nର୍ଗ\t-1369\n▁ସାଙ୍ଗ\t-1370\nଡିକ\t-1371\n▁କବି\t-1372\n▁ଏଥିପାଇଁ\t-1373\n▁ଦଶ\t-1374\n▁ହତ୍ୟା\t-1375\n▁ଚିକିତ୍ସା\t-1376\nନୀତି\t-1377\n▁ଦେବାକୁ\t-1378\n▁ଅଞ୍ଚଳରେ\t-1379\n▁ସୌ\t-1380\nକ୍ଟର\t-1381\n▁ଦୁଃଖ\t-1382\n▁ବନ\t-1383\nତ୍ୱ\t-1384\n▁ରାମ\t-1385\nବସ\t-1386\nସଭା\t-1387\n▁ଶୁଭ\t-1388\n▁ଶବ୍ଦ\t-1389\n▁ରହୁ\t-1390\n▁ଘରକୁ\t-1391\n▁ନିଷ୍ପ\t-1392\n▁ଦିଲ୍ଲୀ\t-1393\n▁ସହଯୋଗ\t-1394\nଷ୍ଟି\t-1395\n▁ଦାୟ\t-1396\n▁ଉକ୍ତ\t-1397\nis\t-1398\nାୟତ\t-1399\n▁ଦକ୍ଷ\t-1400\n▁ଦିଆଯାଇଛି\t-1401\n▁ହେଇ\t-1402\n▁ମୁଣ୍ଡ\t-1403\n▁ସମ୍ମାନ\t-1404\n▁ପ୍ରମୁଖ\t-1405\n▁ଅନୁଷ୍ଠିତ\t-1406\nଫା\t-1407\nନାର\t-1408\n▁ଶିଶୁ\t-1409\n▁କରାଯାଉଛି\t-1410\n▁ଭୋ\t-1411\nମନ୍ତେ\t-1412\n▁ଯୋଗୁଁ\t-1413\nଲିକ\t-1414\n▁ଆଶ\t-1415\n▁ପଟ୍ଟନାୟକ\t-1416\n▁ସେତେବେଳେ\t-1417\n▁ଖଣ୍ଡ\t-1418\n▁ସଭାପତି\t-1419\nୟର\t-1420\n▁ବିଜେଡି\t-1421\n▁ଉଦ୍ୟ\t-1422\nଙ୍ଗି\t-1423\nକାଲି\t-1424\nପୂର୍ଣ୍ଣ\t-1425\n▁ଯୋ\t-1426\n▁ଥାଏ\t-1427\n୍ରୁ\t-1428\n▁ବଢ଼\t-1429\n▁ପ୍ରୟ\t-1430\n▁ନିଷ୍ପତ୍ତି\t-1431\n▁ରକ୍ତ\t-1432\nଲାଇ\t-1433\nରାଜ\t-1434\nିରେ\t-1435\nକୋ\t-1436\nଗେ\t-1437\n▁ସ୍ଥାନୀୟ\t-1438\n▁କମ୍\t-1439\n▁ଦେହ\t-1440\nମନ\t-1441\n▁ଭାଗ\t-1442\n▁ରାଷ୍ଟ୍ର\t-1443\n▁ରିପୋର୍ଟର\t-1444\nଗ୍ରୀ\t-1445\n▁ନ୍ୟୁ\t-1446\n▁ଜୁ\t-1447\nଞ୍ଜନ\t-1448\nମକୁ\t-1449\n▁ଗଛ\t-1450\n▁ଆଖି\t-1451\nକ୍ଷେପ\t-1452\nର୍ଘ\t-1453\n▁ପ୍ରତ୍ୟେକ\t-1454\nig\t-1455\n▁ବିଧାୟକ\t-1456\n▁ହୋ\t-1457\nୱା\t-1458\n▁ଏନେଇ\t-1459\n▁ତାରିଖରେ\t-1460\nବେଦନ\t-1461\n▁ପାକି\t-1462\nନକ\t-1463\n▁ସାଇ\t-1464\n▁ଖୁବ୍\t-1465\n▁ଦେଲେ\t-1466\nଢା\t-1467\n▁ଘଟଣ\t-1468\n▁ଆତ୍ମ\t-1469\n▁ଖୁସି\t-1470\n▁ପ୍ରଭ\t-1471\nor\t-1472\n▁ପ୍ରାଣ\t-1473\n▁ପ୍ରତିଶତ\t-1474\n▁ବସ\t-1475\nକଳ\t-1476\n▁କୋଭି\t-1477\nନନ୍ଦ\t-1478\nଘଟ\t-1479\n▁ଅଲ\t-1480\n▁ପଡ଼\t-1481\n▁ମଙ୍ଗ\t-1482\n▁ଚର୍ଚ୍ଚ\t-1483\nାନ୍ସ\t-1484\nୀଙ୍କୁ\t-1485\nିକି\t-1486\n▁ପଚ\t-1487\nal\t-1488\n▁ବିରୋଧରେ\t-1489\nଲେଖ\t-1490\nତମ\t-1491\n▁ଅପେ\t-1492\nାତ୍\t-1493\n▁ଜ୍ଞ\t-1494\n▁ପଶ୍ଚ\t-1495\nଯୋଗ୍ୟ\t-1496\nିତି\t-1497\n▁ତର\t-1498\n▁ବଳ\t-1499\n▁ଚେଷ୍ଟା\t-1500\nଣ୍ଡର\t-1501\n▁ଯୁବକ\t-1502\n▁ନାଁ\t-1503\n▁D\t-1504\n▁ହାସ\t-1505\nୋଳନ\t-1506\n▁ପକାଇ\t-1507\nଦରେ\t-1508\n▁ପ୍ରସ୍ତ\t-1509\n▁ରାଜ୍ୟର\t-1510\n▁ରହ\t-1511\n▁୩୦\t-1512\nଏସ୍\t-1513\n▁ଅସୁ\t-1514\n▁କିଏ\t-1515\nom\t-1516\nଷ୍ଠାନ\t-1517\n▁ଅଟେ\t-1518\n▁ସନ୍ତ\t-1519\n▁ଲକ୍ଷ୍ୟ\t-1520\n▁ରିପୋର୍ଟ\t-1521\n▁ପୂଜା\t-1522\nଥରେ\t-1523\n▁୧୪\t-1524\n▁ଅମ\t-1525\n▁ପୁରୁଷ\t-1526\n▁ମୂଲ୍ୟ\t-1527\n▁ଆମର\t-1528\n▁ତଳେ\t-1529\nୋଲ\t-1530\n▁ସୁଖ\t-1531\n▁ପାଳନ\t-1532\n▁1\t-1533\n▁ହେବାର\t-1534\nୁଦ୍ର\t-1535\nଧୀନ\t-1536\n▁ଆଶା\t-1537\n▁ଯୁଦ୍ଧ\t-1538\nସହ\t-1539\nହେରା\t-1540\n▁ଜୟ\t-1541\nଦୟ\t-1542\nବାଇ\t-1543\nାବ୍ୟ\t-1544\n▁ସଂଗ\t-1545\n▁ରାସ୍ତା\t-1546\n▁ମହାର\t-1547\nାଜ\t-1548\n▁ଉପସ୍ଥିତ\t-1549\n▁କଲା\t-1550\n▁୨୪\t-1551\n▁ମିଶ\t-1552\n▁ପ୍ରେମ\t-1553\n▁ମାଧ୍ୟମରେ\t-1554\n▁ଟେଲି\t-1555\n▁ମିଳିବ\t-1556\nଣ୍ଡିଆ\t-1557\n▁ସମେତ\t-1558\n▁ସାର\t-1559\n▁ଥରେ\t-1560\nାର୍ଯ୍ୟ\t-1561\nil\t-1562\n▁ଫା\t-1563\n▁ନବୀନ\t-1564\n▁ସ୍ଥା\t-1565\n▁ସନ୍ଧ\t-1566\nଧର\t-1567\nଶ୍\t-1568\nଘ୍ର\t-1569\nହୁଁ\t-1570\n▁କାରଣରୁ\t-1571\nଣେ\t-1572\nଲୋକ\t-1573\nତଙ୍କ\t-1574\n▁ହସ୍ପି\t-1575\nଥିବ\t-1576\n▁ଜାତୀୟ\t-1577\n▁ବିମାନ\t-1578\n▁ଢା\t-1579\n୍ତ୍ତା\t-1580\n▁ଏହାପରେ\t-1581\n▁ପରିସ୍ଥି\t-1582\n▁ନିମନ୍ତେ\t-1583\nର୍ଭ\t-1584\n▁ମୋଦି\t-1585\n▁ହସ୍ପିଟା\t-1586\n▁ମାରି\t-1587\n▁ମାର୍\t-1588\n▁ଉଦ୍ଧାର\t-1589\nକୃତ\t-1590\nବ୍ର\t-1591\n▁ଦଳର\t-1592\n▁ଚଳିତ\t-1593\nଷ୍ଠା\t-1594\n▁ହୁଅ\t-1595\n▁ତୁମେ\t-1596\nଥ୍ୟ\t-1597\nକୋର୍\t-1598\n▁ସତ୍ୟ\t-1599\n▁ଶିକ୍ଷକ\t-1600\n▁ବ୍ଲକ\t-1601\n▁ସଂକ୍ର\t-1602\n▁ଘଣ୍ଟା\t-1603\n▁ଉଦ୍\t-1604\n▁ଦାୟିତ\t-1605\n▁ବ୍ୟାଙ୍କ\t-1606\n▁ଶୀ\t-1607\n▁ଜିଲ୍ଲାରେ\t-1608\n▁ପାକିସ୍ତାନ\t-1609\n▁ଦୀର୍ଘ\t-1610\n▁ଆହୁ\t-1611\n▁ଖେଳା\t-1612\n୍ୟାନ\t-1613\n▁ବାବୁ\t-1614\nୂର୍ଯ୍ୟ\t-1615\n▁ଆକ୍ରମଣ\t-1616\n▁ସଂକ୍ରମଣ\t-1617\nପତ୍ର\t-1618\n▁ହୋଇପାରେ\t-1619\n▁ଭକ୍ତ\t-1620\nଭ୍\t-1621\nବାଦୀ\t-1622\nଂସା\t-1623\nଢ଼ା\t-1624\nପ୍ରା\t-1625\n▁ସାରା\t-1626\n▁ମାନଙ୍କ\t-1627\n▁ରହିଥିବା\t-1628\n▁ଫୋ\t-1629\n▁ବିଧାନ\t-1630\nୃତ୍ତ\t-1631\nନ୍ସ\t-1632\nତ୍ବ\t-1633\n▁ଭୟ\t-1634\n▁ଟ୍ରେ\t-1635\n▁ହେଉଛନ୍ତି\t-1636\n▁ଈ\t-1637\n▁ନିଶ୍ଚ\t-1638\nମ୍ବାଇ\t-1639\n▁ଲକ୍ଷ୍\t-1640\n▁ବାହାରି\t-1641\n▁ବିଶ୍ବ\t-1642\n▁ସେମାନଙ୍କର\t-1643\nମତା\t-1644\n▁ଥା\t-1645\nନ୍ନାଥ\t-1646\n▁ପତ୍ର\t-1647\nଥିକ\t-1648\n▁ତିଆ\t-1649\n▁ଅର୍ଥା\t-1650\nଯି\t-1651\nହେଲେ\t-1652\nପଦ\t-1653\n▁ଉତ୍ସ\t-1654\n▁ଆହୁରି\t-1655\n▁ଓଡ଼ିଶାର\t-1656\nପ୍ତା\t-1657\n▁ଉଲ୍\t-1658\n▁ଅପେକ୍ଷା\t-1659\n▁କାହିଁକି\t-1660\nଲେଶ\t-1661\n▁ହେଉଥିବା\t-1662\nଟୁ\t-1663\n▁ସମ୍ଭାବ\t-1664\n▁ସ୍ଥ\t-1665\n▁ରହିବା\t-1666\n▁ବ୍ୟବସାୟ\t-1667\n▁ଅନୁଭବ\t-1668\nୂଳ\t-1669\n▁ନର\t-1670\nକ୍ଷଣ\t-1671\n▁ସ୍ଥିତି\t-1672\n▁ଔ\t-1673\nar\t-1674\n▁ପ୍ରବେଶ\t-1675\n▁2\t-1676\nଆନ୍ତି\t-1677\n▁ନିଜେ\t-1678\n▁the\t-1679\n▁ବ୍ରା\t-1680\n▁ମ୍ୟାଚ\t-1681\n▁ଚିନ୍ତା\t-1682\n▁ସମାଜ\t-1683\n▁ସଦସ୍ୟ\t-1684\nାଇଥିଲେ\t-1685\nିକାର\t-1686\n▁୧୩\t-1687\n▁ପୁରୀ\t-1688\n▁ମନରେ\t-1689\n▁ସାମିଲ\t-1690\n▁ସଭ୍ୟ\t-1691\n▁ବିଷୟ\t-1692\n▁ରଙ୍ଗ\t-1693\nସ୍ଥାନ\t-1694\n▁ମିଡି\t-1695\n▁ଫିଲ୍ମ\t-1696\n▁ଅଧ୍ୟକ୍ଷ\t-1697\n▁ପୂର୍ବତନ\t-1698\n▁୧୬\t-1699\n▁ଯିବା\t-1700\nର୍ମା\t-1701\n▁ସ୍ମ\t-1702\n▁ଆଇନ\t-1703\n▁ଆର୍ଥିକ\t-1704\nର୍ସ\t-1705\n▁କରିବାରେ\t-1706\n▁ଜିଲ୍ଲାର\t-1707\n▁ଶୋ\t-1708\n▁ଆନ୍ଦ\t-1709\nରେନ୍ଦ୍ର\t-1710\nନାକୁ\t-1711\n▁ବରି\t-1712\n▁ଫଳ\t-1713\n▁କ୍ଲିକ୍\t-1714\n▁S\t-1715\n▁ଭାରତର\t-1716\n▁ପଦକ୍ଷେପ\t-1717\n▁ଗୌ\t-1718\nes\t-1719\n▁ନାଗ\t-1720\n▁ସକା\t-1721\nନ୍ଦା\t-1722\n▁୨୦୨\t-1723\n▁o\t-1724\n▁ପାଉ\t-1725\nିକେଟ\t-1726\n▁ଫଟୋ\t-1727\n▁ତଥ୍ୟ\t-1728\n▁ପଢନ୍ତୁ\t-1729\nଞ୍ଜି\t-1730\n▁ଆୟୋଜ\t-1731\n▁ଚାରି\t-1732\nen\t-1733\n▁ଆମେରି\t-1734\n▁ଜଣାପଡି\t-1735\n▁ଅଭିଯୁକ୍ତ\t-1736\n▁ଲାଗିଛି\t-1737\n▁ସଂକ୍ରମିତ\t-1738\n▁ତିଆରି\t-1739\nମୟ\t-1740\n▁କମିଟି\t-1741\n▁ଅଗ\t-1742\n▁ପରିବର୍ତ୍ତନ\t-1743\n▁ନିକଟ\t-1744\n▁ସହାୟ\t-1745\n▁ନିଜକୁ\t-1746\n▁୧୭\t-1747\nଗ୍ରାମ୍\t-1748\n▁ହୋଇଯାଇଛି\t-1749\n▁ଲେଖ\t-1750\n▁ବିଦ\t-1751\n▁କରିଥାଏ\t-1752\nତିକି\t-1753\nର୍ଦ୍ଧ\t-1754\n▁ବ୍ରହ୍ମ\t-1755\n▁ରାଉ\t-1756\nସାଦ\t-1757\n▁ୟ\t-1758\n▁ଧନ\t-1759\n▁ହରି\t-1760\n▁ରାୟ\t-1761\nପାତ୍ର\t-1762\n▁ଅତ୍ୟ\t-1763\n▁ପ୍ରମାଣ\t-1764\n▁କୋର୍ଟ\t-1765\nବର୍ତ୍ତୀ\t-1766\nଦ୍ୱ\t-1767\nଭାର\t-1768\n▁ଗୁଣ\t-1769\n▁ଛୋଟ\t-1770\nଥିବାର\t-1771\n▁ପ୍ରକୃତ\t-1772\n▁by\t-1773\n▁ସଚ\t-1774\nଟିକୁ\t-1775\n▁ରବି\t-1776\n▁ସମର୍ଥ\t-1777\n▁ପର୍ଯ୍ୟା\t-1778\nକର୍ମ\t-1779\n▁ଧାନ\t-1780\nଣ୍ଟର\t-1781\n▁ମନ୍ତ୍ର\t-1782\n▁ଗୀତ\t-1783\n▁ବିଶ୍\t-1784\n▁ସୂର୍ଯ୍ୟ\t-1785\n▁ଟେଲିଗ୍ରାମ୍\t-1786\nସାର\t-1787\n▁ବୁଝି\t-1788\nାଇବାକୁ\t-1789\nବୃ\t-1790\n▁ଷ\t-1791\n▁ଫେର\t-1792\n▁ଖୋଲି\t-1793\n▁ଲାଗୁ\t-1794\nପ୍ରେ\t-1795\n▁ସ୍ପଷ୍ଟ\t-1796\n▁ଲୋକମାନେ\t-1797\nଅଛି\t-1798\nଯାଇଥିବା\t-1799\n▁ଯାତ୍ରା\t-1800\nବାରେ\t-1801\n▁ଘଟିଛି\t-1802\n▁ଟିମ୍\t-1803\n▁ସଂସ୍କ\t-1804\nତରେ\t-1805\nରାପ\t-1806\nମେଣ୍ଟ\t-1807\nଛା\t-1808\n▁ନେତ\t-1809\n▁ଆଗକୁ\t-1810\nଆଁ\t-1811\n▁ଅବସରରେ\t-1812\n▁ନାୟକ\t-1813\n▁ଆନ୍ଦୋଳନ\t-1814\n▁ପୁନ\t-1815\nପାରନ୍ତି\t-1816\n▁ହାସଲ\t-1817\n▁ପ୍ରାର୍ଥୀ\t-1818\nପଥ\t-1819\nମକ\t-1820\nତୀତ\t-1821\n▁ଚୁ\t-1822\n▁ଅପରା\t-1823\n▁ଅଳ୍ପ\t-1824\n▁ସନ୍ଦ\t-1825\nାଜିକ\t-1826\n▁ଭଗବାନ\t-1827\n▁ବିଚାର\t-1828\n▁ବେହେରା\t-1829\n▁ଆନନ୍ଦ\t-1830\nପୀ\t-1831\n▁ଯେତେ\t-1832\n▁ଙ୍କ\t-1833\n▁ସବୁଠାରୁ\t-1834\niti\t-1835\n▁କଷ୍ଟ\t-1836\n▁ପରିଚା\t-1837\n▁ଅବସ୍ଥାରେ\t-1838\nଘୁ\t-1839\nପାରୁ\t-1840\n▁ଦେଇଛି\t-1841\n▁କମ୍ପାନୀ\t-1842\n▁ଅଂଶ\t-1843\n▁ସୁଯୋଗ\t-1844\nଗଡ\t-1845\nୈତିକ\t-1846\n▁ବ୍ୟୁରୋ\t-1847\n▁କିପରି\t-1848\nହ୍\t-1849\n▁କର୍\t-1850\n▁ଜ୍ଞାନ\t-1851\n▁କାଳ\t-1852\nପକ୍ଷରେ\t-1853\nଗିରି\t-1854\n୍ୟାସ\t-1855\n▁କୃଷ୍ଣ\t-1856\n▁ତାହାର\t-1857\nବତୀ\t-1858\n▁ଲୋ\t-1859\nନ୍ତ୍ରଣ\t-1860\n▁ଦର୍ଶନ\t-1861\nସିଂହ\t-1862\n▁ଗୁରୁତ୍ୱ\t-1863\n▁ମଣ୍ଡ\t-1864\n▁ଦ୍ୱିତୀୟ\t-1865\n▁କୁହାଯାଇଛି\t-1866\nଲକୁ\t-1867\n▁ଉଲ୍ଲେଖ\t-1868\nଫେ\t-1869\nହାଁ\t-1870\n▁ଟିକା\t-1871\n▁ଆସିଛି\t-1872\nରାମର୍ଶ\t-1873\nଟନ\t-1874\nକୁର\t-1875\n▁ମଧ\t-1876\nାର୍ଡ\t-1877\n▁ଜଙ୍ଗ\t-1878\n▁ହେଲ\t-1879\n▁ଆଗରେ\t-1880\nଜାତ\t-1881\nସନ୍\t-1882\n▁୨୫\t-1883\n▁ଦକ୍ଷି\t-1884\n▁ସୁସ୍ଥ\t-1885\n▁ଗ୍ରା\t-1886\n▁ପରାମର୍ଶ\t-1887\nma\t-1888\n▁ଦେଖୁ\t-1889\n▁ପ୍ରଦର୍ଶନ\t-1890\n▁ପୀ\t-1891\n▁ଯେପରି\t-1892\n▁ଜିଲା\t-1893\nତକ\t-1894\n▁ରଥ\t-1895\nନାରେ\t-1896\n▁ଆସନ\t-1897\n▁ଲେକ\t-1898\nଛାତ୍ରୀ\t-1899\n▁ଗତକାଲି\t-1900\nରିଆ\t-1901\n▁ଡ୍ର\t-1902\nସିଆଲ\t-1903\n▁ଭିନ୍ନ\t-1904\n▁ବରିଷ୍ଠ\t-1905\nଦ୍ରା\t-1906\nଲାଙ୍ଗ\t-1907\n▁ଅଙ୍ଗ\t-1908\n▁ଯାଇଥିଲେ\t-1909\n▁ଜଗତ\t-1910\nସବୁ\t-1911\n▁ଷ୍ଟେ\t-1912\n▁ନିର\t-1913\n▁ସୁବିଧା\t-1914\n▁ସାମାଜିକ\t-1915\n▁ଦୁର୍ଘଟ\t-1916\nଟିଂ\t-1917\n▁ଭଦ୍ର\t-1918\n୍ରମ\t-1919\n▁କାନ୍\t-1920\n▁ନାହାନ୍ତି\t-1921\nଗଲ\t-1922\n▁ଜବତ\t-1923\n▁ଫାଇ\t-1924\n▁ମାର\t-1925\n▁ମୋଟ\t-1926\nଣ୍ଡରେ\t-1927\n▁ଧୀରେ\t-1928\n▁ପ୍ରସଙ୍ଗ\t-1929\n▁ଇତି\t-1930\n▁ରକ୍ଷା\t-1931\nଗାର\t-1932\n▁ବାସ\t-1933\n▁ଶିବ\t-1934\n▁ଯାଇଛି\t-1935\n▁ବଜାର\t-1936\n▁ଜଣାପଡ଼ି\t-1937\n▁ସମ୍ବନ୍ଧ\t-1938\nଙ୍ଖ\t-1939\nମ୍ଭେ\t-1940\n▁ସଂଘ\t-1941\n▁ମଙ୍ଗଳ\t-1942\n▁ହାତରେ\t-1943\n▁ଠା\t-1944\n▁ସମ୍ଭାବନା\t-1945\nପାତ\t-1946\nତ୍ସ\t-1947\n▁ଚିତ୍ର\t-1948\nାନୁଷ୍ଠାନ\t-1949\n▁.\t-1950\nକ୍ଷିତ\t-1951\n▁କୃଷି\t-1952\n▁ଛାତ୍ରଛାତ୍ରୀ\t-1953\n▁ଜୀବ\t-1954\n▁ରାଶି\t-1955\n▁ସାରି\t-1956\n▁ମସିହାରେ\t-1957\n▁ଯୋଗା\t-1958\nମତି\t-1959\n▁୫୦\t-1960\n▁ଖେଳି\t-1961\n▁ପାଇଛି\t-1962\nat\t-1963\nଏମ\t-1964\n▁ଖର୍ଚ୍ଚ\t-1965\nସାଧ\t-1966\n▁ନିୟମ\t-1967\n▁ମିଳୁ\t-1968\n▁)\t-1969\n▁ରଖା\t-1970\nପର୍ଯ୍ୟ\t-1971\nଦ୍ମ\t-1972\n▁ଆତଙ୍କ\t-1973\n▁ସ୍ବର\t-1974\n▁ଛାଡ଼ି\t-1975\n▁ସୁପ୍ର\t-1976\n▁କହିବା\t-1977\n▁ଦରକାର\t-1978\n▁ଯଥା\t-1979\n▁ଆସିବା\t-1980\nଥ୍\t-1981\nଧ୍ୟାନ\t-1982\n▁ଅନ୍ଧ\t-1983\n▁ସଫଳତା\t-1984\nଦ୍ଦେଶ୍ୟ\t-1985\nବନେଶ୍ବର\t-1986\n▁ପ୍ରକଳ୍ପ\t-1987\nିନେ\t-1988\n▁ଚାଷୀ\t-1989\n▁ଚାଲିଛି\t-1990\n୧୯\t-1991\n▁କଳ\t-1992\nଘା\t-1993\nଓଡ଼ି\t-1994\n▁ମନୋ\t-1995\n▁ରୂପେ\t-1996\n▁ଯାଉଛି\t-1997\n▁ମହାନ୍ତି\t-1998\n୍ରାସ\t-1999\n▁ନମ୍ବର\t-2000\n▁ପୋଷ୍ଟ\t-2001\n▁ଇଚ୍ଛା\t-2002\nଉନ\t-2003\n▁ଗଲେ\t-2004\nାନ୍ୟ\t-2005\n▁ପଶ୍ଚିମ\t-2006\n▁ଗଜ\t-2007\nଥିରେ\t-2008\n୍ରୀୟ\t-2009\n▁ସନ୍ଧ୍ୟା\t-2010\n▁ଉଦ୍ଦେଶ୍ୟ\t-2011\n▁କେନ୍ଦ\t-2012\n▁ଜଗନ୍ନାଥ\t-2013\n▁ଅଧି\t-2014\nୃତୀୟ\t-2015\nକର୍\t-2016\nଦ୍ଦ\t-2017\n▁ଟୁ\t-2018\n▁ଦୋକ\t-2019\n▁କରାଯାଇଥିବା\t-2020\n▁I\t-2021\n▁ପହଞ୍ଚ\t-2022\n▁ପୁସ୍ତ\t-2023\n▁ଥିବାରୁ\t-2024\n▁ବିଦ୍ୟାଳୟ\t-2025\n▁P\t-2026\n▁ପଣ୍ଡା\t-2027\nକୃଷ୍ଣ\t-2028\n▁ଗଞ୍ଜ\t-2029\n▁ସମାନ\t-2030\n▁ଗୁରୁତର\t-2031\nବୋଧ\t-2032\nକ୍ରି\t-2033\n▁ବିବାଦ\t-2034\nୋହ\t-2035\n▁of\t-2036\n▁Dig\t-2037\n▁ନାମରେ\t-2038\n▁ପାଞ୍ଚ\t-2039\n▁ହଠ\t-2040\n▁ବହି\t-2041\n▁ଶିଳ୍ପ\t-2042\nବଣ\t-2043\n▁w\t-2044\nଦୀପ\t-2045\nତାରେ\t-2046\nନୈତିକ\t-2047\n▁ପଞ୍ଜ\t-2048\nଚନ୍ଦ୍ର\t-2049\n▁ଇଣ୍ଡିଆ\t-2050\nit\t-2051\nୋପ\t-2052\nଶେଷ\t-2053\n▁ଫୁଲ\t-2054\n▁ଦକ୍ଷିଣ\t-2055\n▁ପ୍ରତିକ\t-2056\nଲିଆ\t-2057\n▁ଗେ\t-2058\n▁ଗଠନ\t-2059\n▁ରାତି\t-2060\n▁ମାନଙ୍କୁ\t-2061\n▁ସଂପର୍କ\t-2062\n▁ନେବା\t-2063\nzed\t-2064\nପିଏ\t-2065\n▁ଶାସନ\t-2066\n▁ଅଟ\t-2067\n▁ଜଣାପଡିଛି\t-2068\n▁ପ୍ରସ୍ତାବ\t-2069\n▁ପଞ୍ଚାୟତ\t-2070\nଡର\t-2071\n▁ରହିଛନ୍ତି\t-2072\n▁ବାଇ\t-2073\n▁ଧର\t-2074\nପ୍ତି\t-2075\nସ୍ୱ\t-2076\n▁ଅଭାବ\t-2077\n▁ଅନୁର\t-2078\n▁ତୃତୀୟ\t-2079\n▁ଦିନରେ\t-2080\n▁ବାଲେଶ\t-2081\n▁ସେଥିପାଇଁ\t-2082\n▁ଯନ୍ତ୍ର\t-2083\n▁ମାମଲାରେ\t-2084\n▁ମୁଖ\t-2085\n▁କାବ୍ୟ\t-2086\n▁ଜିଲ୍ଲାପାଳ\t-2087\n▁ଦ୍ର\t-2088\n▁ଅନ୍ୟାନ୍ୟ\t-2089\n▁ନାହଁ\t-2090\n▁ପୁରା\t-2091\nitized\t-2092\n▁ହଁ\t-2093\nଲାରୁ\t-2094\n▁ବଢ଼ି\t-2095\n▁ଶ୍ରୀମ\t-2096\n▁ଜୋ\t-2097\nପାଦନ\t-2098\n▁ଶୋଇ\t-2099\nଶ୍ଚର୍ଯ୍ୟ\t-2100\n▁ଆକାଶ\t-2101\nରାଟ\t-2102\n▁ଧାର\t-2103\n▁ଜୀବନରେ\t-2104\n▁ମାନସିକ\t-2105\n▁ରାଷ୍ଟ୍ରପତି\t-2106\nଲିଉ\t-2107\n▁ଆଧାର\t-2108\n▁ଚାହୁଁ\t-2109\n▁ଭାରତରେ\t-2110\n▁ପାରିବେ\t-2111\nଦେଲେ\t-2112\n▁ଗବେ\t-2113\nତ୍ନୀ\t-2114\nଯୁକ୍ତି\t-2115\n▁ସମ୍ଭବ\t-2116\nନ୍ଥ\t-2117\nକୋର୍ଟ\t-2118\n▁ଦେବେ\t-2119\n▁ପରିସ୍ଥିତି\t-2120\n▁ସମ୍ପୂର୍ଣ୍ଣ\t-2121\nସାରି\t-2122\n▁ଗ୍ରାମର\t-2123\n▁ରାଜା\t-2124\nପ୍ନ\t-2125\nଖଣ୍ଡ\t-2126\n▁ଶେଷରେ\t-2127\n▁କରିପାରିବେ\t-2128\n▁ଆହତ\t-2129\n▁ଉଦ୍ୟମ\t-2130\n▁ସଂଗ୍ରହ\t-2131\nୂର୍ତ୍ତି\t-2132\nାୟଣ\t-2133\n▁ଘେ\t-2134\n▁ଭର୍ତ୍ତି\t-2135\n),\t-2136\nକ୍ୟ\t-2137\n▁ଔଷ\t-2138\n▁ଅଞ୍ଚଳ\t-2139\n▁ଅନ୍ତର୍ଗତ\t-2140\nସଲ\t-2141\n▁ଆବେଦନ\t-2142\n▁A\t-2143\n୍ୟାୟ\t-2144\n▁ଅକ୍ଷ\t-2145\n▁ଆମକୁ\t-2146\n▁ରଖ\t-2147\n▁ରେଳ\t-2148\nଡ଼ିକୁ\t-2149\n▁ଦୋଷ\t-2150\nଟିର\t-2151\n▁ଗୋଷ୍ଠ\t-2152\n▁ବାଧ୍ୟ\t-2153\n▁ରାତିରେ\t-2154\n▁ସୋସିଆଲ\t-2155\nଡ଼କୁ\t-2156\n▁ଭୁଲ\t-2157\n▁ପିନ୍ଧ\t-2158\n▁ପରିଚାଳନା\t-2159\nର୍ବ\t-2160\nଞ୍ଚଳ\t-2161\ncom\t-2162\n▁ପରମ\t-2163\n▁ଗାନ୍ଧ\t-2164\n▁ଯୋଗାଇ\t-2165\n▁୨୧\t-2166\nଗ୍ନି\t-2167\n▁ବର୍ଣ୍ଣ\t-2168\nସ୍ତି\t-2169\n▁ଜଣକ\t-2170\n▁ନଦୀ\t-2171\n▁ତଥାପି\t-2172\nସମ\t-2173\nଟାଏ\t-2174\nରାଲ\t-2175\n▁ଜଣଙ୍କ\t-2176\n▁ଦ୍ବ\t-2177\nପାଦକ\t-2178\n▁ଜରି\t-2179\n▁କମିଶ\t-2180\nଚୟ\t-2181\n▁ସୋମ\t-2182\n▁କ୍ଷତି\t-2183\n▁ଯାହାକୁ\t-2184\n▁ଅର୍ଥାତ୍\t-2185\nକାଉ\t-2186\n▁p\t-2187\nଥିଲ\t-2188\nାନ୍ତର\t-2189\n▁ସର୍ବା\t-2190\nଟିରେ\t-2191\n▁ଜୀବନର\t-2192\n▁ସମଗ୍ର\t-2193\n▁ଟଙ୍କାର\t-2194\n▁ମିନି\t-2195\nଶୀଳ\t-2196\n▁କଣ୍ଠ\t-2197\nଚର\t-2198\n▁ତୋ\t-2199\n▁ଉଠାଇ\t-2200\n▁କଳ୍ପ\t-2201\nପ୍ରଦେଶ\t-2202\n▁ଜାଣିବା\t-2203\nru\t-2204\n▁ଯଥ\t-2205\n▁ରାହୁ\t-2206\n▁ଟେଷ୍ଟ\t-2207\nରୂପ\t-2208\n▁ଦୈ\t-2209\n▁ପରିଣ\t-2210\n▁ହ୍ରାସ\t-2211\n▁ପ୍ରଦେଶ\t-2212\n▁ସଚିବ\t-2213\nାଇଲେ\t-2214\nମ୍ବାର\t-2215\n▁ସହାୟତା\t-2216\nଡିକା\t-2217\n▁ଖାଲି\t-2218\n▁ହେତୁ\t-2219\n▁ଖାଇବା\t-2220\nଚେତନ\t-2221\nେହ\t-2222\n▁ଝା\t-2223\n▁ପଢ଼\t-2224\n▁ମନୁ\t-2225\n▁ସପ୍ତା\t-2226\n▁କାର୍ଯ୍ୟା\t-2227\n▁ବଜ\t-2228\n▁ପଠାଇ\t-2229\n▁ରଖିବା\t-2230\n▁ଅତ୍ୟନ୍ତ\t-2231\n▁Digitized\t-2232\n▁ଅଗ୍ର\t-2233\n▁ଗୋଟେ\t-2234\n▁ପ୍ରାଚ\t-2235\n▁ସମାଧାନ\t-2236\n▁ଚର୍ଚ୍ଚା\t-2237\n▁ନରେନ୍ଦ୍ର\t-2238\n▁ନୀତି\t-2239\n▁ଦୂ\t-2240\n▁ପାଲ\t-2241\n▁ଛାଡି\t-2242\n▁ମୌ\t-2243\n▁ଓଡ଼ିଶାରେ\t-2244\n▁ସେହ\t-2245\n▁୨୦୦\t-2246\n▁ଗସ୍ତ\t-2247\n▁ବିରୋଧୀ\t-2248\n▁ପରିବେଶ\t-2249\nପ୍ରତି\t-2250\n▁ଆଗାମୀ\t-2251\nରମ\t-2252\nିନୀ\t-2253\n▁ସେନା\t-2254\n▁ନିର୍ମ\t-2255\n▁ପସ\t-2256\nଶ୍ୱ\t-2257\n▁ବୋଧ\t-2258\nଗ୍ରାମ\t-2259\n▁ମୂଳ\t-2260\n▁ଭାଙ୍ଗି\t-2261\nବିଦ୍ୟାଳୟ\t-2262\n▁ଇତ୍ୟା\t-2263\n▁ପାଖକୁ\t-2264\n▁ମେଡିକା\t-2265\n▁ପ୍ରଚ\t-2266\n▁ପ୍ରିୟ\t-2267\nଘାତ\t-2268\n▁ଭବି\t-2269\n▁ଦାୟିତ୍ୱ\t-2270\nou\t-2271\nଷକ\t-2272\n▁ଏସବୁ\t-2273\n▁ହସ୍ତ\t-2274\n▁ଆଶ୍ଚର୍ଯ୍ୟ\t-2275\nମଣ୍ଡ\t-2276\n▁ଉପଲ\t-2277\n▁ମହାପାତ୍ର\t-2278\nଥିବେ\t-2279\n▁ବିଦ୍ୟା\t-2280\n▁ଐ\t-2281\nଜିତ\t-2282\n▁ପରା\t-2283\nହନ୍ତି\t-2284\n▁ଶ୍ରମିକ\t-2285\nୃକ୍ତ\t-2286\nାନ୍ଧି\t-2287\n▁କର୍ତ୍ତ\t-2288\n▁ପରିମାଣ\t-2289\nas\t-2290\n▁ପତ୍ନୀ\t-2291\n▁ମୁମ୍ବାଇ\t-2292\n▁ପଇ\t-2293\n▁ରନ୍\t-2294\nଗୁଡିକ\t-2295\nବାଦିକ\t-2296\n▁ସ୍ବାସ୍ଥ୍ୟ\t-2297\n▁ନ୍ୟୁଜ୍\t-2298\n▁୨୨\t-2299\n...\t-2300\nଭୂତ\t-2301\n▁ଠାରେ\t-2302\nସାଧାରଣ\t-2303\nବ୍ଧ\t-2304\n▁ପଦ୍ମ\t-2305\n▁ସୁନା\t-2306\n▁ବୈଠକରେ\t-2307\nପୋ\t-2308\nପଡ଼ା\t-2309\n▁ବର୍\t-2310\n▁ସାକ୍ଷ\t-2311\nଲଙ୍କ\t-2312\n▁ଜାତି\t-2313\n▁ଦଣ୍ଡ\t-2314\n▁C\t-2315\nଳିଆ\t-2316\n▁ବିଜ୍ଞାନ\t-2317\nନ୍ଦନ\t-2318\nପୂର୍\t-2319\n▁ଯିଏ\t-2320\n▁ବାସ୍ତ\t-2321\n▁ଚାକି\t-2322\nବକ\t-2323\n▁ଊ\t-2324\n▁ଔଷଧ\t-2325\n▁ବଞ୍ଚ\t-2326\n▁ବେଶ୍\t-2327\n▁ବିଭାଗର\t-2328\n▁ସୈ\t-2329\n▁ଯେଉଁମାନେ\t-2330\nଖର\t-2331\n▁ଶହ\t-2332\n▁ଏନ୍\t-2333\nୁରୀ\t-2334\n▁ରେକର୍\t-2335\n▁କଲେଜ\t-2336\n▁ନଥିଲା\t-2337\n▁ଦୋକାନ\t-2338\n▁ପାଦ\t-2339\nମାନ୍ୟ\t-2340\nଳୟରେ\t-2341\n▁ଦିନେ\t-2342\n▁ସେଥିରେ\t-2343\nଥୁଲ\t-2344\n▁ଶୀଘ୍ର\t-2345\n▁ବିଦ୍ୟୁ\t-2346\nଥିଲି\t-2347\n▁ବିକ୍ରି\t-2348\n▁କରିଥାନ୍ତି\t-2349\nମାରୀ\t-2350\n▁ପ୍ରବଳ\t-2351\n▁ଭବିଷ୍ୟ\t-2352\n▁ବାରମ୍ବାର\t-2353\nହୁଏ\t-2354\n▁ନାମକ\t-2355\n▁ସମ୍ବଲ\t-2356\nବୃତ୍ତ\t-2357\n▁ଜିନି\t-2358\n▁ଇତ୍ୟାଦି\t-2359\n▁ସାମ୍ବାଦିକ\t-2360\nଗ୍ଧ\t-2361\n▁ହସି\t-2362\nୁରନ୍ତ\t-2363\n▁ଗାଡ଼ି\t-2364\nାମା\t-2365\nmail\t-2366\nାର୍ଟ\t-2367\n▁ଚତୁ\t-2368\n▁ଅତିଥି\t-2369\n▁ବର୍ଷର\t-2370\nସ୍ତର\t-2371\n▁ଜନତା\t-2372\n▁ମ୍ୟାଚ୍\t-2373\nବ୍ୟା\t-2374\nେତ୍ରୀ\t-2375\n▁ବିଜୁ\t-2376\n▁ରଚନା\t-2377\n▁ପଚାରି\t-2378\n▁ଏଥର\t-2379\n▁ଷ୍ଟା\t-2380\n▁ପୂରଣ\t-2381\n▁ଜିନିଷ\t-2382\n▁ପ୍ରଭୁ\t-2383\n▁ଆବଶ୍ୟକତା\t-2384\n▁କାର୍ଯ୍ୟରେ\t-2385\nik\t-2386\n▁ଢେ\t-2387\n୍ଚ୍ଚ\t-2388\n▁ପ୍ରଚାର\t-2389\n▁ଯାଇଥିଲା\t-2390\n▁ଶୁଣା\t-2391\nଭାଗ\t-2392\n▁ପାରୁ\t-2393\n▁ଜୋର\t-2394\nଫି\t-2395\n▁ୱେ\t-2396\n▁ଅଗ୍ନି\t-2397\n▁ଉଦ\t-2398\n▁ଶୁକ୍ର\t-2399\n▁ବସ୍ତୁ\t-2400\n▁ଜଣାପଡ଼ିଛି\t-2401\nଭୂମି\t-2402\n▁ଦାନ\t-2403\n▁ସବୁବେଳେ\t-2404\nନାଲ\t-2405\n▁ନାର\t-2406\n▁ଶନି\t-2407\n▁ବାହା\t-2408\n▁ଉତ୍କଳ\t-2409\n▁ଖେଳାଳି\t-2410\nପାନ\t-2411\n▁ମୁକ\t-2412\n▁ନିଯୁକ୍ତି\t-2413\n▁ଦିଗ\t-2414\n▁ବିନା\t-2415\n▁ବୈଠକ\t-2416\n▁କବିତା\t-2417\n▁ବିଦେଶ\t-2418\nଟାଇ\t-2419\nବନି\t-2420\nହାସ\t-2421\n▁ପିଇ\t-2422\n▁ବାୟୁ\t-2423\n▁ରୋଗୀ\t-2424\n▁ପାତ୍ର\t-2425\nନାହାନ୍ତି\t-2426\nଦୃ\t-2427\n▁ମାସରେ\t-2428\n▁କରାଯାଏ\t-2429\nହେବ\t-2430\n▁ଆପ\t-2431\n▁ଅଷ୍ଟ\t-2432\n▁ଘରୋଇ\t-2433\n▁ମାଆ\t-2434\n▁ହଠାତ୍\t-2435\n▁ତୁରନ୍ତ\t-2436\n▁ଉପାୟ\t-2437\n▁ସନ୍ଦେହ\t-2438\nଖଲ\t-2439\n▁ପ୍ଲା\t-2440\n▁କହିଲା\t-2441\n▁ଅସ୍ତ୍ର\t-2442\n▁ସମ୍ବଲପୁର\t-2443\nଲୀ\t-2444\nand\t-2445\nୁଛି\t-2446\n▁ଠିଆ\t-2447\nପିତ\t-2448\n▁ସକାଳ\t-2449\n▁ଗୋଟାଏ\t-2450\n▁ଆମେରିକା\t-2451\n▁ବିଧାନସଭା\t-2452\nre\t-2453\n▁ରସ\t-2454\n▁ପାଇବେ\t-2455\n▁ମିଳିଥିଲା\t-2456\nଦ୍ବାର\t-2457\n▁ଦେଖାଇ\t-2458\n▁ସଚେତନ\t-2459\n▁ଅଞ୍ଚଳର\t-2460\n▁ସପ୍ତାହ\t-2461\n▁ସମସ୍ତଙ୍କ\t-2462\n▁କାର୍ଯ୍ୟକ୍ରମରେ\t-2463\n▁c\t-2464\n▁କେମିତି\t-2465\n▁ନଥିଲେ\t-2466\nସ୍ର\t-2467\n▁କିଣି\t-2468\n▁ପଡ଼ିବ\t-2469\n▁ମୁକ୍ତ\t-2470\n▁ମିଡିଆରେ\t-2471\nଝର\t-2472\n▁ଦିବସ\t-2473\nଉଛନ୍ତି\t-2474\n▁କହିଛି\t-2475\n▁ରାସ୍ତ\t-2476\n▁ସେପଟେ\t-2477\n▁ସମୁଦ୍ର\t-2478\nଦାୟ\t-2479\n▁ପଣ୍ଡ\t-2480\n▁ସାଂସ\t-2481\n▁କୁହାଯାଏ\t-2482\n▁ପୁରସ୍କାର\t-2483\nଫରୁ\t-2484\n▁ଶେ\t-2485\n▁ଯୁଗ\t-2486\nିକେଟ୍\t-2487\n▁ପିତା\t-2488\n▁କନ୍ୟା\t-2489\n▁ପସନ୍ଦ\t-2490\nଫୁ\t-2491\nରେଇ\t-2492\n▁ମଧୁ\t-2493\n▁ଆସିବ\t-2494\n▁ଘଟଣାସ୍ଥ\t-2495\n▁ଦେଇଥିବା\t-2496\nୟୀ\t-2497\n▁l\t-2498\n▁ପୋଷ\t-2499\n▁ହୋଇପ\t-2500\n▁ଦୃଶ୍ୟ\t-2501\nକନକ\t-2502\nହୋଇ\t-2503\n▁ସାହ\t-2504\n▁ସ୍ୱତନ୍ତ୍ର\t-2505\n▁କ୍\t-2506\n▁ଘୁ\t-2507\n▁ଗଳ୍ପ\t-2508\n▁ଓଡିଶା\t-2509\n▁ଥାନାରେ\t-2510\n▁ଯାତ୍ରୀ\t-2511\n▁ରାଜଧାନ\t-2512\n▁ଶ୍ରଦ୍ଧା\t-2513\n▁ପ୍ରତିଷ୍ଠା\t-2514\nୌଦ୍ଧ\t-2515\n▁ଲକ୍ଷ୍ମୀ\t-2516\n▁୨୩\t-2517\nଜ୍ଞା\t-2518\n▁ତୁମ\t-2519\n▁ସୂଚନାଯୋଗ୍ୟ\t-2520\n▁ଚୋ\t-2521\n▁ତି\t-2522\nର୍ଷଣ\t-2523\n▁ପବନ\t-2524\n▁ଗାଡି\t-2525\nଏମ୍\t-2526\n▁ହସ\t-2527\n▁ସଙ୍କ\t-2528\n▁ଦିଗରେ\t-2529\n▁୨୬\t-2530\n▁ସ୍କ\t-2531\nବିନ୍ଦ\t-2532\n▁ଅଲଗା\t-2533\n▁ନାନା\t-2534\n▁ଶାନ୍ତି\t-2535\n▁ସମସ୍ତଙ୍କୁ\t-2536\njan\t-2537\n▁ଚୌ\t-2538\n▁ନକ\t-2539\n▁ଗୁଜ\t-2540\n▁ସ୍ଥିର\t-2541\n▁ଅନୁରୋଧ\t-2542\n▁ବ୍ୟକ୍ତ\t-2543\n▁ଦିଅନ୍ତୁ\t-2544\nେଦ\t-2545\n▁ଅକ୍\t-2546\n▁ବରଂ\t-2547\n▁ଚାହିଁ\t-2548\nଚଳ\t-2549\n▁f\t-2550\n▁ବିଶି\t-2551\n▁ହରାଇ\t-2552\n▁ପରିଚୟ\t-2553\n▁କରାଯାଉ\t-2554\n▁ସଂସ୍ଥା\t-2555\n▁କାର୍ଯ୍ୟାନୁଷ୍ଠାନ\t-2556\n▁ମୟ\t-2557\n୍ୟାଣ\t-2558\n▁ବୋଲ\t-2559\n▁ଗର୍ଭ\t-2560\n▁ନଷ୍ଟ\t-2561\n▁ସୋମବାର\t-2562\n▁ଆସିଥିଲେ\t-2563\nସ୍ପ\t-2564\n▁୪୦\t-2565\nକ୍ରେ\t-2566\n▁ପାନ\t-2567\n▁ଜିତି\t-2568\n▁ମାନବ\t-2569\n▁୦\t-2570\n▁ପୃଥ\t-2571\ngmail\t-2572\n▁ନିଆଁ\t-2573\n▁ସର୍ବାଧିକ\t-2574\n▁ଜାଣ\t-2575\n▁ଜ୍ୟ\t-2576\nପ୍ରଭୁ\t-2577\n▁ସନ୍ତାନ\t-2578\n▁ଜିଲ୍ଲାରୁ\t-2579\nସୂ\t-2580\nସ୍ବ\t-2581\n▁ଭୋଟ\t-2582\n▁ଖରାପ\t-2583\n▁ଭାବି\t-2584\n▁ଅଧିକା\t-2585\nତ୍ତ୍ୱ\t-2586\n▁କରିଦେଇ\t-2587\n▁ଜେନା\t-2588\n▁ସମ୍ମି\t-2589\n▁ମନୁଷ୍ୟ\t-2590\n▁R\t-2591\n▁ଦେବୀ\t-2592\n▁ଦେଖିଲେ\t-2593\nମ୍ମ\t-2594\nଶ୍ରୀ\t-2595\n▁ଗୁଳି\t-2596\n▁ଭୋଗ\t-2597\n▁ସାଂସଦ\t-2598\nଥାନ୍ତ\t-2599\n▁ପାରେ\t-2600\n▁ଗଞ୍ଜାମ\t-2601\nମିକା\t-2602\n▁ହୃଦୟ\t-2603\n▁କର୍ମୀ\t-2604\n▁ତରଫରୁ\t-2605\n▁ଧ୍ୟାନ\t-2606\n▁ଯାହାର\t-2607\n▁ରାଜନୈତିକ\t-2608\nଗଜ\t-2609\nରୋ\t-2610\n▁sru\t-2611\n▁ଧନ୍ୟ\t-2612\n▁ସମ୍ମୁଖ\t-2613\n▁ଚୀନ\t-2614\n▁srujan\t-2615\n▁ଦିଆଯିବ\t-2616\n▁ସ୍ବାମୀ\t-2617\nପଡା\t-2618\n▁ପାରିବ\t-2619\n୍ବା\t-2620\n▁ମଝ\t-2621\nଣ୍ଡେ\t-2622\n▁ଅନୁମତି\t-2623\n▁ପରବର୍ତ୍ତୀ\t-2624\nଶ୍ମ\t-2625\n▁ହାର\t-2626\n▁ଭାଷା\t-2627\n▁ବିହାର\t-2628\n▁ସାମଗ୍ରୀ\t-2629\nଶ୍ବ\t-2630\n▁ଶୃ\t-2631\n▁ସହଜ\t-2632\n▁ଚିଠି\t-2633\nଣ୍ଣା\t-2634\n▁ବୁଧ\t-2635\nଧୁନିକ\t-2636\nମାଧ୍ୟ\t-2637\n▁ବଲିଉ\t-2638\n▁ମହାରା\t-2639\n▁ଅଭିନେତ୍ରୀ\t-2640\n▁ଅଳ\t-2641\n▁ସିରି\t-2642\n▁ବ୍ୟତୀତ\t-2643\nନୂଆଦିଲ୍ଲୀଃ\t-2644\n▁ଛି\t-2645\n▁ଭାଷ\t-2646\n▁ମହାନ\t-2647\n▁ପ୍ରଭାବିତ\t-2648\n▁ଅଧ୍ୟା\t-2649\n▁ଅଥ\t-2650\n▁ଜରୁ\t-2651\nଶୋର\t-2652\n▁ଖେଳ\t-2653\n▁ଯେଉଁଥିରେ\t-2654\nଣ୍ୟ\t-2655\n▁ରାହୁଲ\t-2656\n▁ବ୍ରାହ୍ମ\t-2657\n',\t-2658\nହୀ\t-2659\n▁।\\\t-2660\nାତ୍ମ\t-2661\n▁ପ୍ରଶଂସା\t-2662\nଷିକ\t-2663\nଙ୍ଗୁ\t-2664\n▁ଖସି\t-2665\n▁ବିଭ\t-2666\n▁ରେକର୍ଡ\t-2667\nika\t-2668\n▁ଭବ\t-2669\n▁ବୟସ\t-2670\n▁ସରି\t-2671\n▁ପଛରେ\t-2672\n▁ଏମାନେ\t-2673\nic\t-2674\n୍ରିୟା\t-2675\n▁ନନ୍ଦ\t-2676\n▁ସେତେ\t-2677\n▁ଅଭିନୟ\t-2678\n▁ମୋବାଇ\t-2679\n▁ପୁସ୍ତକ\t-2680\n▁ଦୁର୍ଘଟଣା\t-2681\nଜନକ\t-2682\n▁ବିଶ୍ବାସ\t-2683\nଣରେ\t-2684\nଦିଷ୍ଟ\t-2685\nକ୍ରିୟା\t-2686\n▁ବଲାଙ୍ଗ\t-2687\n▁ହୋଇଯାଇ\t-2688\n▁ନେଇଥିଲେ\t-2689\n▁ଶୁକ୍ରବାର\t-2690\n▁ରମ\t-2691\n▁ଏଣୁ\t-2692\nରିକ୍ତ\t-2693\n▁ଉନ୍ନତି\t-2694\nଜିଟି\t-2695\n▁ପାହା\t-2696\n▁ଗୁଡିକ\t-2697\n▁ବୌଦ୍ଧ\t-2698\n▁ସିଦ୍ଧ\t-2699\n▁ପୁରୁଣା\t-2700\nଙ୍ଗ୍\t-2701\n▁ଗାଁରେ\t-2702\n▁ଜରିଆରେ\t-2703\n▁ବାହାରେ\t-2704\n▁ଗଣମାଧ୍ୟ\t-2705\n▁ବିଶିଷ୍ଟ\t-2706\n▁in\t-2707\nଳିକା\t-2708\n▁ସୁପ୍ରିମ\t-2709\nଷ୍ଟର\t-2710\n▁ପଶି\t-2711\n▁ଆସିଲେ\t-2712\n▁ଘଟଣାର\t-2713\nସିସି\t-2714\n▁ଲଗାଇ\t-2715\n▁ତ୍ରି\t-2716\n▁ଉତ୍ପାଦନ\t-2717\n▁କରାଯିବା\t-2718\n▁ପ୍ରୋ\t-2719\n▁ବଙ୍ଗ\t-2720\n▁ପ୍ରୟୋଗ\t-2721\n▁ପୂର୍ଣ୍ଣ\t-2722\nହରଣ\t-2723\n▁କାହାକୁ\t-2724\nଧ୍ୟା\t-2725\nକାଳୀନ\t-2726\nିବାରେ\t-2727\n▁ତାହାକୁ\t-2728\n▁ରବିବାର\t-2729\n▁ରନ\t-2730\n▁ଶବ\t-2731\nତାକୁ\t-2732\n▁ଏମ୍\t-2733\n▁ପଥର\t-2734\n▁ସମୀ\t-2735\n▁ସାନ\t-2736\n▁କୋଭିଡ\t-2737\n▁ଶିକାର\t-2738\nମାଳ\t-2739\nଗୁଡ଼ିକୁ\t-2740\n▁ଘଟଣାରେ\t-2741\n▁ଦୃଷ୍ଟିରେ\t-2742\n▁srujanika\t-2743\nଚୁ\t-2744\n▁ନୋ\t-2745\nଶ୍ବର\t-2746\n▁ଉପା\t-2747\n▁ଗ୍ରୀ\t-2748\n▁ପ୍ରତିଯୋଗ\t-2749\nୃତ୍ୱ\t-2750\n▁ତାପ\t-2751\n▁ଆଜିର\t-2752\n▁କ୍ଷମତା\t-2753\n▁ହୋଇଯିବ\t-2754\nତ୍ନ\t-2755\nନେଇ\t-2756\nଜପୁର\t-2757\nଲାଣି\t-2758\n▁ବିଳ\t-2759\nକ୍ରିୟ\t-2760\n▁ଅତିରିକ୍ତ\t-2761\nଆରୀ\t-2762\nଶ୍ରମ\t-2763\n▁ସାବ\t-2764\n▁ପରିଷ\t-2765\n▁ସ୍ଥିତ\t-2766\n▁ସାମାନ୍ୟ\t-2767\nଗାଁ\t-2768\nମୋହନ\t-2769\n▁ଉଠ\t-2770\n▁ଡ଼\t-2771\nଥମିକ\t-2772\n▁ଗରି\t-2773\n▁ମନ୍ତ\t-2774\nଷ୍ଣୁ\t-2775\n▁ଅଫ୍\t-2776\n▁ବଢି\t-2777\n▁ସୀମା\t-2778\n▁ନେବାକୁ\t-2779\n▁ତୀ\t-2780\nଲକ୍ଷ\t-2781\nହଜାର\t-2782\n▁ଅଂଚ\t-2783\n▁କୁହାଯାଉଛି\t-2784\n▁ଉପରକୁ\t-2785\n▁ଗୁରୁବାର\t-2786\nମୂ\t-2787\n୍ଯ\t-2788\n▁O\t-2789\n▁୨୮\t-2790\nବଙ୍ଗ\t-2791\nମିତା\t-2792\n▁ଗ୍ରେ\t-2793\n▁ପଜିଟି\t-2794\nଫୋ\t-2795\n▁ଅଣ\t-2796\n▁ରଣ\t-2797\n▁ଚିନ୍ତ\t-2798\n▁କାହାଣୀ\t-2799\n▁ରହିଥିଲା\t-2800\nଢି\t-2801\nଉନ୍\t-2802\nଚରଣ\t-2803\nପାଠ\t-2804\n▁ନେଇଛନ୍ତି\t-2805\n▁ନିୟନ୍ତ୍ରଣ\t-2806\nଅଛୁ\t-2807\n▁ମାଇ\t-2808\nରୁଦ୍ଧ\t-2809\n▁ବ୍ରି\t-2810\n▁ରୁଜୁ\t-2811\n▁ସେହିଭଳି\t-2812\n▁ସାମ୍ନାକୁ\t-2813\n▁ଏସ୍\t-2814\n▁ନଗର\t-2815\n▁ଜଙ୍ଗଲ\t-2816\n▁କୋହ\t-2817\n▁ମାତ\t-2818\n▁ଯାଏଁ\t-2819\n▁ଆଶଙ୍କା\t-2820\n▁ଯେମିତି\t-2821\n▁ଶ୍ରେଣୀ\t-2822\nଶେ\t-2823\nଷ୍ଣ\t-2824\n▁କଠ\t-2825\nଲାନ୍ସ\t-2826\n▁ଠାକୁର\t-2827\n▁ଏପରିକି\t-2828\n▁ରହିଥାଏ\t-2829\n▁ଆପଣଙ୍କର\t-2830\n▁ଶୁଣ\t-2831\n▁ସଂଗଠନ\t-2832\n▁ଛାତ୍ରୀ\t-2833\n▁ଡାକ୍ତରଖ\t-2834\n▁ଇନ\t-2835\n▁ଖୁବ\t-2836\n▁ଗାଁର\t-2837\n▁ଦେଖିବା\t-2838\n▁ନାଗରିକ\t-2839\n▁ପଶୁ\t-2840\n▁ବେଳକୁ\t-2841\nରବ\t-2842\nନିଆ\t-2843\n▁ଶତ\t-2844\n▁ଦୀପ\t-2845\n▁ଗୁଡ଼\t-2846\n▁ପଦାର୍ଥ\t-2847\n▁ନିର୍ବାଚନରେ\t-2848\n▁a\t-2849\nନଙ୍କ\t-2850\n▁ଉତ୍ସା\t-2851\nମତ\t-2852\n▁ପଡିବ\t-2853\n▁ପ୍ରସାଦ\t-2854\n▁ଶନିବାର\t-2855\n▁ହୋଇଗଲା\t-2856\nପ୍ରିୟ\t-2857\n▁କଲ୍ୟାଣ\t-2858\n▁କାଠ\t-2859\n▁ଟ୍ରା\t-2860\n▁ପ୍ରଣ\t-2861\n▁ମିଶାଇ\t-2862\n▁ଗୋଷ୍ଠୀ\t-2863\n▁ଅଧିକାର\t-2864\n▁ପଥ\t-2865\n▁୨୭\t-2866\n▁ପ୍ରାଥମିକ\t-2867\n▁ମସିହା\t-2868\nହକ\t-2869\nସାଇ\t-2870\n▁ଜାନୁ\t-2871\nେନ୍ଦ୍ର\t-2872\n▁ନେତୃତ୍ୱ\t-2873\n▁ପ୍ରସ୍ତୁତି\t-2874\n▁ଗତି\t-2875\n▁ସତର୍କ\t-2876\nପର୍ଯ୍ୟନ୍ତ\t-2877\nେତା\t-2878\n▁ଗଭ\t-2879\nଙ୍ଗୀ\t-2880\n▁ମତେ\t-2881\nକାନ୍ତ\t-2882\nଧିକାରୀ\t-2883\n▁ବିଳମ୍ବ\t-2884\nଦେହ\t-2885\nାତ୍ମକ\t-2886\n▁କଟକଣା\t-2887\nୁଥିବା\t-2888\n▁ଏପ୍ର\t-2889\n▁ମାର୍ଚ୍ଚ\t-2890\nକଥା\t-2891\nମ୍ନ\t-2892\n▁ବାର୍\t-2893\n▁ଅବଶ୍ୟ\t-2894\n▁ପରିସର\t-2895\n▁ରଖିଛନ୍ତି\t-2896\nଉଣୀ\t-2897\n୍ତି\t-2898\n▁ଅଜ\t-2899\nୃତ୍ୟ\t-2900\n▁ବଜେ\t-2901\nବନ୍ଧୁ\t-2902\n▁କ୍ରୀ\t-2903\n▁ପାଇବ\t-2904\n▁କାର୍ଯ୍ୟାଳୟ\t-2905\nଏଁ\t-2906\n▁ପୁଲିସ୍\t-2907\n▁ଆସିଥିବା\t-2908\n▁ଲେଖିଛନ୍ତି\t-2909\nହତ\t-2910\nଳାଇ\t-2911\nଧର୍ମ\t-2912\n▁ଠିକ\t-2913\nଉଥିବା\t-2914\n▁ବିସ୍\t-2915\n▁ମିଶି\t-2916\n▁ଭାଇରାଲ\t-2917\n▁ଦ୍ବିତୀୟ\t-2918\n▁ପ୍ରକ୍ରିୟା\t-2919\n୍ୱି\t-2920\nଶାଳୀ\t-2921\nସ୍ପର\t-2922\n▁॥\t-2923\n▁ଘରର\t-2924\nବିଆଇ\t-2925\nମିଟର\t-2926\n▁ଭେଟି\t-2927\n▁ପ୍ରକାଶିତ\t-2928\n▁ସମର୍ଥନ\t-2929\n▁ସମ୍ମିଳ\t-2930\n▁କରିପାରି\t-2931\n▁ପ୍\t-2932\n▁କାନ\t-2933\n▁ଶୀତ\t-2934\n▁ଖୋର୍\t-2935\n▁ଯାଇଥିବା\t-2936\n▁ଶାସ୍ତ୍ର\t-2937\nକ୍ଷମ\t-2938\n▁ମୁକାବି\t-2939\n▁ଅଧିକାଂଶ\t-2940\nପୂ\t-2941\nମଧ୍ୟ\t-2942\n▁and\t-2943\n▁ସାର୍\t-2944\n▁କାହାର\t-2945\n▁ଭଦ୍ରକ\t-2946\n▁ନିର୍ଭର\t-2947\nପୁଟ\t-2948\n▁ଟିକେ\t-2949\n▁ରହିବେ\t-2950\n▁ଚାକିରି\t-2951\n▁ଶ୍ରୀମନ୍ଦିର\t-2952\nଡରେ\t-2953\nଯାନ\t-2954\n▁ବାବ\t-2955\n▁ମୁତାବ\t-2956\n▁ଛେ\t-2957\n▁ଯୌ\t-2958\nକ୍ଟ\t-2959\nନୀୟ\t-2960\nଶୋଧ\t-2961\nଦେବା\t-2962\n▁ଥୁଲ\t-2963\n▁ବଂଶ\t-2964\n▁ପ୍ରକାରର\t-2965\n▁ସ୍ବତନ୍ତ୍ର\t-2966\n▁ପବ\t-2967\n▁କାଟି\t-2968\n▁ବୁଲି\t-2969\nଡ଼ିଆ\t-2970\nସନ୍ଧ\t-2971\nଅଛନ୍ତି\t-2972\n▁ବୁଦ୍ଧି\t-2973\nଙ୍କି\t-2974\n▁ଶାନ୍ତ\t-2975\n▁କୋଭିଡ୍\t-2976\nଏଲ\t-2977\n▁ମଦ\t-2978\n▁ଘରୁ\t-2979\nଚ୍ଛେଦ\t-2980\n▁ସ୍ତରରେ\t-2981\n▁ବାହାରକୁ\t-2982\n▁ସଂପର୍କରେ\t-2983\nବାବୁ\t-2984\nଶ୍ରୁ\t-2985\n▁ଆଦର୍ଶ\t-2986\n▁ଆସୁଥିବା\t-2987\n▁ସ୍ବାଧୀନ\t-2988\n▁ସମ୍ମିଳନୀ\t-2989\n▁T\t-2990\nୁଲାଇ\t-2991\nେଶ୍ୱର\t-2992\n▁ୟୁନି\t-2993\n▁ଯାଞ୍ଚ\t-2994\n▁ଗି\t-2995\n▁ରାଜନୀତି\t-2996\nଲ୍ଲା\t-2997\n▁ଆୟୋଜିତ\t-2998\n୍ବେ\t-2999\n▁ଶୂ\t-3000\n▁ଅରୁ\t-3001\n▁ହୃଦ\t-3002\n▁ସବୁଠୁ\t-3003\n▁କେନ୍ଦୁ\t-3004\n▁ଆଫ\t-3005\n▁କରାଇ\t-3006\n▁ସ୍ଥାପନ\t-3007\n▁ମୁକାବିଲା\t-3008\n▁ଘଟଣାସ୍ଥଳରେ\t-3009\nମ୍ୟ\t-3010\n▁କୂ\t-3011\nପାଗ\t-3012\n▁ଓହ\t-3013\n▁ଶର୍ମା\t-3014\n▁ବାବା\t-3015\nଯାତ୍ରା\t-3016\n▁ଯାଉଥିବା\t-3017\n▁୩୧\t-3018\nକ୍କା\t-3019\n▁ଟିକ\t-3020\n▁ସ୍ବପ୍ନ\t-3021\nଧୂ\t-3022\n▁୨୯\t-3023\n▁ଡ୍ରାଇ\t-3024\n▁ଅନୁଷ୍ଠାନ\t-3025\n▁ମହିଳାଙ୍କ\t-3026\ning\t-3027\n▁ଟଙ୍କ\t-3028\n▁କେଉଁଠି\t-3029\n▁ଯୋଗଦେଇ\t-3030\n▁ପ୍ରତିନି\t-3031\n▁ଲଭ\t-3032\nପଞ୍ଚ\t-3033\nନ୍ଧ୍ର\t-3034\n▁ଅଫିସ\t-3035\nହାଣ୍ଡି\t-3036\n▁ଏହିପରି\t-3037\n▁କହନ୍ତି\t-3038\nମାନଙ୍କରେ\t-3039\nନୋ\t-3040\n▁ଭୂମିକା\t-3041\n▁ମଙ୍ଗଳବାର\t-3042\n▁ଆଡ\t-3043\n▁ତମ\t-3044\n▁ମୃ\t-3045\nଡ଼ରେ\t-3046\n▁ଜଣଙ୍କୁ\t-3047\n▁ସମ୍ପାଦକ\t-3048\nଉଛି\t-3049\n▁ମାଟି\t-3050\n▁ଜରୁରୀ\t-3051\n▁ହିନ୍ଦୁ\t-3052\nଳିକ\t-3053\n▁ଜାଗ\t-3054\n▁ଥାଇ\t-3055\n▁ସଂସ\t-3056\n▁୧୦୦\t-3057\n▁ବସ୍ତ\t-3058\n▁ଉତ୍କ\t-3059\n▁କିଲୋ\t-3060\n▁ମୃତଦେହ\t-3061\n▁ପ୍ରାପ୍ତ\t-3062\nୟମ\t-3063\nନିର୍\t-3064\nେଷ୍ଟ\t-3065\nରିତ\t-3066\nହୀନ\t-3067\n▁20\t-3068\n▁ସବ\t-3069\n▁କନ୍ଧ\t-3070\nଗ୍ରହଣ\t-3071\nଶ୍ମୀର\t-3072\n▁ଏକ୍ସ\t-3073\n▁ରିଲି\t-3074\nସିଲ\t-3075\n୍ୟତା\t-3076\n▁ଅବା\t-3077\n▁ମୁହ\t-3078\n▁ବିବେ\t-3079\n▁ଉଚିତ୍\t-3080\n▁ପେଟ୍ର\t-3081\n▁ବିରାଟ\t-3082\n▁ମହାରାଷ୍ଟ୍ର\t-3083\n▁ଖ୍ରୀ\t-3084\n▁ପଇସା\t-3085\n▁ମୋଦୀ\t-3086\nସଭ\t-3087\n▁ଧୋ\t-3088\nଆଡ଼େ\t-3089\nଭଞ୍ଜ\t-3090\n▁ବସ୍\t-3091\nଶାନ୍ତ\t-3092\n▁ବିଶ୍ୱାସ\t-3093\n▁ଭାଇର\t-3094\n▁ଶକ୍ତ\t-3095\n▁ଗୁଡ଼ିକ\t-3096\n▁ପୌ\t-3097\nତ୍ତା\t-3098\n▁ଅଟକ\t-3099\n▁ଡାକ\t-3100\n▁ରାଉତ\t-3101\n▁ବୁଧବାର\t-3102\n▁ଯାଜପୁର\t-3103\nମାସ\t-3104\n▁ଦାଶ\t-3105\n▁ସ୍ୱାମୀ\t-3106\n▁ପ୍ରତିଦିନ\t-3107\n▁ବାଲେଶ୍ୱର\t-3108\n▁ହସ୍ପିଟାଲରେ\t-3109\nଦିଆ\t-3110\n▁ଅହ\t-3111\n▁ଆଜ\t-3112\n▁ପଟେ\t-3113\n▁ଦିନକୁ\t-3114\n▁ନିଶ୍ଚିତ\t-3115\n▁d\t-3116\n▁ଏମ\t-3117\n▁ସଂକ\t-3118\n▁ସାଂ\t-3119\n▁ଗରିବ\t-3120\n▁ପରିଣତ\t-3121\n▁ମାଲିକ\t-3122\n▁B\t-3123\nଗ୍ନ\t-3124\nଲ୍ରେ\t-3125\n▁ଷ୍ଟେସନ\t-3126\n▁କେନ୍ଦ୍ରା\t-3127\n▁କାର୍ଯ୍ୟକାରୀ\t-3128\nନ୍ତେ\t-3129\n▁ଏଥିସହ\t-3130\n▁ବ୍ୟାପକ\t-3131\n▁ମୁତାବକ\t-3132\n▁ଆକ୍ରାନ୍ତଙ୍କ\t-3133\n▁ଗୁରୁତ୍ୱପୂର୍ଣ୍ଣ\t-3134\n▁ସଞ୍ଜ\t-3135\n▁ପ୍ରୟାସ\t-3136\n▁ଚମ\t-3137\n▁କେଶ\t-3138\n▁ଯିବ\t-3139\nଶିକ୍ଷ\t-3140\n▁ମାଗି\t-3141\n▁ଆସୁଛି\t-3142\n▁ଭୁବନେଶ୍ବର\t-3143\n▁?\"\t-3144\n▁ନର୍\t-3145\n▁ଅଗଷ୍ଟ\t-3146\n▁ସେୟାର\t-3147\n▁ହୋଇଯାଏ\t-3148\n▁ମହାପ୍ରଭୁ\t-3149\n▁ମିଳିଥିବା\t-3150\nଦଶ\t-3151\nମଣି\t-3152\n▁ଋଣ\t-3153\n▁ଧା\t-3154\n▁ରଜ\t-3155\n▁ଜ୍ୟୋ\t-3156\n୧୦\t-3157\nଳରୁ\t-3158\nୀକାର\t-3159\n▁ଆଲୋକ\t-3160\nଓଡ଼ିଶା\t-3161\n▁ପାଲଟି\t-3162\n▁ଆଦିବାସୀ\t-3163\n▁କହିବାକୁ\t-3164\n▁ମହାମାରୀ\t-3165\n▁ଗଲା\t-3166\n▁ଯିବାକୁ\t-3167\n▁ହୋଇପାରି\t-3168\n▁ଚାଲିଥିବା\t-3169\n▁M\t-3170\nଆର୍\t-3171\n▁କଅ\t-3172\nଣ୍ଣି\t-3173\nଲକାନ\t-3174\n▁ମିଳେ\t-3175\n▁ଦୁହେଁ\t-3176\n▁ସହରରେ\t-3177\n▁m\t-3178\nକୂଳ\t-3179\n▁ଲଗି\t-3180\nସିଦ୍ଧ\t-3181\n▁ଙ୍କୁ\t-3182\n▁ସଙ୍ଗୀତ\t-3183\n▁ପ୍ରାଚୀନ\t-3184\n▁ପଠା\t-3185\n▁ବନ୍ୟା\t-3186\nଲମ୍\t-3187\n▁ଧାରଣ\t-3188\n▁ସ୍ନେ\t-3189\n▁ବ୍ୟସ୍ତ\t-3190\n▁ମୂର୍ତ୍ତି\t-3191\nଟ୍ରେ\t-3192\n▁ରଖୁ\t-3193\nଗୁଡ଼ା\t-3194\n▁ସ୍ତର\t-3195\nଦ୍ୱାରା\t-3196\nରାଉ\t-3197\nଷଣା\t-3198\nାମୁ\t-3199\n▁ସହି\t-3200\nହତ୍ୟା\t-3201\n▁ଆକାଉ\t-3202\n▁ଖୋଲା\t-3203\n▁ପ୍ରି\t-3204\n▁ଆଧୁନିକ\t-3205\n▁ପାଣିପାଗ\t-3206\nୀତା\t-3207\n▁ଜୁଲାଇ\t-3208\n▁ପରସ୍ପର\t-3209\nୋଭ\t-3210\n▁ଦାଖଲ\t-3211\nହାଁନ୍ତି\t-3212\n▁ଗ୍ରାମରେ\t-3213\nିତ୍ୟ\t-3214\nାରମ୍ଭ\t-3215\n▁ସୁପର\t-3216\n▁ଦେବତା\t-3217\n▁ଉପସ୍ଥା\t-3218\n▁ପଞ୍ଜାବ\t-3219\n▁ପ୍ରକୃତି\t-3220\n▁ଭୀ\t-3221\nଅନ୍ତୁ\t-3222\n▁ନିର୍ଦ୍ଦେଶକ\t-3223\n▁to\t-3224\nକ୍ତା\t-3225\nଣାରେ\t-3226\n▁ଗାଈ\t-3227\n▁୨୦୧୯\t-3228\n▁ଉତ୍ତମ\t-3229\n▁ଭାଗ୍ୟ\t-3230\n▁ସକାଳୁ\t-3231\n▁ସୂତ୍ର\t-3232\n▁ପଣ୍ଡିତ\t-3233\n▁ରାସ୍ତାରେ\t-3234\n▁ତତ୍\t-3235\n▁ଅସୁବିଧା\t-3236\n▁ହୁଅନ୍ତି\t-3237\n▁ସରକାରଙ୍କୁ\t-3238\n▁ହୋଇଥିବାରୁ\t-3239\n▁ଝୁ\t-3240\n▁ଶୈ\t-3241\n▁ରୁଷ\t-3242\n▁ସିନେ\t-3243\n▁ସମସ୍ୟ\t-3244\nଗୁଡ଼ିକର\t-3245\n▁ନିଶ୍ଚୟ\t-3246\nle\t-3247\n▁ତୁଳନା\t-3248\n▁ଉପଲବ୍ଧ\t-3249\nଲକାନଗିରି\t-3250\n▁ଅଭିନେତା\t-3251\n▁ଅଛ\t-3252\nକ୍ଷେତ୍ର\t-3253\n▁ଧନ୍ୟବାଦ\t-3254\n▁ସମ୍ମୁଖୀନ\t-3255\nଗୁଣ\t-3256\nପ୍ଟେ\t-3257\n▁ବାଘ\t-3258\n▁ଭୁଲି\t-3259\n▁ଉତ୍ସବ\t-3260\n▁ଅନ୍ୟପଟେ\t-3261\n▁ଦେଇଥିଲା\t-3262\nଦବ\t-3263\nରାୟ\t-3264\nହ୍ୟ\t-3265\nୋଜନ\t-3266\n▁ଦୌ\t-3267\n▁ବଲ\t-3268\n▁ଆଗରୁ\t-3269\n▁ଗ୍ୟା\t-3270\nଟକୁ\t-3271\nଣୀର\t-3272\n▁୩୫\t-3273\nଖାପା\t-3274\n▁ଧାରଣା\t-3275\n▁ମିଳିତ\t-3276\nଆସି\t-3277\n▁ଜର୍\t-3278\n▁ହୁଏତ\t-3279\n▁ଯଥେଷ୍ଟ\t-3280\n▁ବର୍ଣ୍ଣନା\t-3281\n▁ଘା\t-3282\n▁ନାଟକ\t-3283\n▁କାମରେ\t-3284\n▁ଉପନ୍ୟା\t-3285\nଗମ\t-3286\nଣିଆ\t-3287\nଲିକା\t-3288\nାନାରେ\t-3289\n▁ଅଧ୍ବ\t-3290\n▁ନିଷ୍\t-3291\n▁ଆଡ଼କୁ\t-3292\n▁ସହାୟକ\t-3293\n▁ପରିବାରର\t-3294\nଟଣା\t-3295\n▁୬୦\t-3296\nମ୍ବୁ\t-3297\n▁ମନା\t-3298\n▁ଲେଖକ\t-3299\n୧୨\t-3300\nମାରେ\t-3301\n▁ନଭେ\t-3302\n୍ୟାନେ\t-3303\n▁ଯେବେ\t-3304\n▁ରହିଲେ\t-3305\nାମି\t-3306\nାର୍କ\t-3307\n▁ନୟା\t-3308\n▁ବାଣି\t-3309\n୦୦୦\t-3310\n▁କନ\t-3311\ntion\t-3312\n▁ଗାଇ\t-3313\n▁ଆଶ୍ର\t-3314\nଥୀ\t-3315\n▁ଗୋପ\t-3316\n▁ମଝିରେ\t-3317\n▁ସଂସାର\t-3318\nଦ୍ଦିଷ୍ଟ\t-3319\n▁ମୁକ୍ତି\t-3320\nନ୍ଦୀ\t-3321\n▁ତମେ\t-3322\n▁ବୋଇ\t-3323\n▁ବାକି\t-3324\n▁ମାତା\t-3325\n▁ପ୍ରଜା\t-3326\n▁ଟାଇ\t-3327\n▁ଭ୍ର\t-3328\nତ୍ତ୍ବ\t-3329\n▁ଏୟାର\t-3330\n▁ବୃକ୍ଷ\t-3331\n▁ଗବେଷଣା\t-3332\n▁କେନ୍ଦୁଝର\t-3333\n▁ବ୍ୟବସାୟୀ\t-3334\nଲେଇ\t-3335\nହିକ\t-3336\n▁ଅଙ୍କ\t-3337\n▁ବଦଳି\t-3338\nସେମ୍ବର\t-3339\n▁ଏହାସହ\t-3340\n▁h\t-3341\nସିତ\t-3342\n▁ସାତ\t-3343\nମୁଖରେ\t-3344\n▁ଲକ୍ଷଣ\t-3345\n▁ସକାଳେ\t-3346\n▁ଭିତରକୁ\t-3347\n▁ସଂସ୍କାର\t-3348\nଖେ\t-3349\nଲ୍ଡ\t-3350\nଶୁର\t-3351\n▁ଟ୍ୱି\t-3352\n▁ଭଉଣୀ\t-3353\n▁ଶ୍ରେଷ୍ଠ\t-3354\n▁ବ୍ୟକ୍ତିଙ୍କ\t-3355\nତାପ\t-3356\n▁କଏ\t-3357\nଭାବେ\t-3358\n▁ମିଳ\t-3359\n▁ବାଲି\t-3360\n▁ଶତାବ\t-3361\n▁ଅଫିସର\t-3362\n▁ଯୁକ୍ତ\t-3363\n▁ଯାହାକି\t-3364\n▁ଜଣାଇ\t-3365\n▁ପ୍ରଦ\t-3366\nଙ୍କାନା\t-3367\n▁ଅତ\t-3368\n▁ରୋକ\t-3369\nମାତ୍ର\t-3370\n▁କୃଷକ\t-3371\n▁ପ୍ରଫେ\t-3372\n▁ଯେଉଁଠି\t-3373\n▁ଜନସାଧାରଣ\t-3374\nଏକ\t-3375\nସାବ\t-3376\n▁କାଲି\t-3377\n▁ହେବାରୁ\t-3378\n▁ପ୍ରତ୍ୟା\t-3379\n▁ରହୁଥିବା\t-3380\nରଣର\t-3381\nଲେକ\t-3382\nତ୍ତର\t-3383\n▁ଆମ୍ଭ\t-3384\n▁ଦିଆଯାଇ\t-3385\n▁ପଢ଼ି\t-3386\n▁ବୋର୍\t-3387\n▁ଶତାବ୍\t-3388\n▁ଅପର\t-3389\n▁ପିତ\t-3390\n▁ଗାନ୍ଧୀ\t-3391\n▁ଶୁଣାଣି\t-3392\n▁ବିରୁଦ୍ଧ\t-3393\n▁ଉଦ୍ଦେଶ୍ୟରେ\t-3394\nନକୁ\t-3395\n▁।'\t-3396\n▁ସମୁ\t-3397\n▁ପରିବ\t-3398\n▁ବିପଦ\t-3399\n▁ପ୍ରାଣୀ\t-3400\n▁ଆଲ\t-3401\n▁ଖଣି\t-3402\n▁ମାଛ\t-3403\nଜାତୀୟ\t-3404\n▁ନୃତ୍ୟ\t-3405\n▁ଆକର୍ଷଣ\t-3406\n▁ବାତ୍ୟା\t-3407\nଚକ\t-3408\nୂର୍ତ୍ତ\t-3409\n▁ଆଇପିଏ\t-3410\nସିଂହପୁର\t-3411\n▁ଇତିହାସ\t-3412\n▁ରହିବାକୁ\t-3413\n▁ରାଜଧାନୀ\t-3414\n▁ବ୍ରାହ୍ମଣ\t-3415\n▁ଚକ\t-3416\nଣାଳୟ\t-3417\n୍ୟାଗ\t-3418\nଙ୍ଗପୁର\t-3419\n▁ସଂସ୍କୃତି\t-3420\n▁N\t-3421\nଭାଇ\t-3422\nୃଷ୍ଟ\t-3423\n▁ଆସେ\t-3424\n▁ଉଦା\t-3425\n▁ଏଠି\t-3426\n▁ସଂର\t-3427\n▁ଡିଜି\t-3428\nପ୍ରାପ୍ତ\t-3429\n▁ଖାଇବାକୁ\t-3430\n▁ଅର\t-3431\n▁ଝାର\t-3432\n▁ଯବାନ\t-3433\n▁ଗଜପତି\t-3434\n▁ପରିଷଦ\t-3435\n▁ବାଣିଜ୍ୟ\t-3436\nଚ୍ୟୁ\t-3437\n▁ଉପର\t-3438\n▁ଶହେ\t-3439\n▁ଆମ୍ଭେ\t-3440\n▁ଦୁଇଜଣ\t-3441\n▁ବାସ୍ତବ\t-3442\n▁ଏପର୍ଯ୍ୟନ୍ତ\t-3443\nଲଭ\t-3444\nଜନା\t-3445\nବାବ\t-3446\n▁ଇଂଲ\t-3447\n▁ବିସ୍ତ\t-3448\n▁ପକ୍ଷରେ\t-3449\nନ୍ରେ\t-3450\n▁ଚରଣ\t-3451\n▁ବାଦ\t-3452\nାନ୍ତୁ\t-3453\nଭୁବନେଶ୍ୱରଃ\t-3454\nନାନ\t-3455\n▁ଓଁ\t-3456\nକ୍ସି\t-3457\n▁ଜମା\t-3458\nୃପକ୍ଷ\t-3459\n▁ପୁତ୍ର\t-3460\n▁ଏମାନଙ୍କ\t-3461\n▁କ୍ରିକେଟ\t-3462\n▁ସମ୍ବନ୍ଧରେ\t-3463\nra\t-3464\n▁କୃତ\t-3465\nଷ୍କାର\t-3466\n▁ତୁମର\t-3467\n▁ଆସିଲା\t-3468\n▁ଇନ୍ଦ୍ର\t-3469\n▁ମାନଙ୍କର\t-3470\n▁ରଖିବାକୁ\t-3471\n▁ବ୍ୟକ୍ତିଗତ\t-3472\n▁ଚଉ\t-3473\n▁ସିଏ\t-3474\nଲ୍ଲିକ\t-3475\n▁ଆନ୍ତ\t-3476\n▁ରୋଜ\t-3477\n▁ଦ୍ରୁ\t-3478\n▁ମାଡ଼\t-3479\n▁ଗୋପାଳ\t-3480\n▁ନାରାୟଣ\t-3481\n▁ମିଳୁଛି\t-3482\nତିର\t-3483\nମ୍ର\t-3484\n▁ବୀ\t-3485\nବାସୀଙ୍କ\t-3486\nୟଂ\t-3487\n▁ଗହ\t-3488\nକର୍ତ୍ତା\t-3489\n▁ଆସନ୍ତୁ\t-3490\nଡିଏ\t-3491\nଡିତ\t-3492\nରୁଷ\t-3493\n▁ଟିକି\t-3494\n▁ଧାରା\t-3495\n▁ସହରର\t-3496\n▁ରହିଥିଲେ\t-3497\n▁ହୋଇପାରିବ\t-3498\nକୌ\t-3499\nମଣ\t-3500\nଡିଆ\t-3501\n▁ଲଗା\t-3502\n▁ଗଭୀର\t-3503\n▁ଶତ୍ର\t-3504\n▁ଅର୍ପଣ\t-3505\n▁କାରବାର\t-3506\n▁ହେଉଥିଲା\t-3507\n▁ଆତ୍ମହତ୍ୟା\t-3508\n▁ବ୍ରହ୍ମପୁର\t-3509\nଶିର\t-3510\n▁ଅନା\t-3511\n▁ନାରୀ\t-3512\nଗ୍ରସ୍ତ\t-3513\n▁ରୂପରେ\t-3514\n▁ହିସାବ\t-3515\n▁ପ୍ରଶଂସ\t-3516\n▁ସମ୍ପତ୍ତି\t-3517\nଟାଲ\t-3518\n▁ଭୂମି\t-3519\n▁ନ୍ୟାୟ\t-3520\nଚ୍ଚିତ୍ର\t-3521\n▁ମାମଲାର\t-3522\n▁ଦେଖାଯାଏ\t-3523\n▁କେତେବେଳେ\t-3524\n▁ପ୍ରସଙ୍ଗରେ\t-3525\n▁ନିରା\t-3526\n▁ଦୂରେଇ\t-3527\n▁ଆସିଥିଲା\t-3528\nଖିତ\t-3529\nହୃତ\t-3530\n▁ୟେ\t-3531\n▁ଅଧୀନ\t-3532\n▁ଓଡିଶ\t-3533\n▁ଆୟୋଜନ\t-3534\n▁କାଳରେ\t-3535\n▁ଡକ୍ଟର\t-3536\n▁ଉଦାହରଣ\t-3537\nବିଧ\t-3538\nତ୍କାର\t-3539\n▁ଖୋଜି\t-3540\n▁ହାତୀ\t-3541\n▁ଏକାଠି\t-3542\n▁ବଞ୍ଚି\t-3543\n▁ଅଷ୍ଟ୍ରେ\t-3544\nସୋ\t-3545\n୧୧\t-3546\n▁ରଘୁ\t-3547\n▁୧୯୯\t-3548\n▁ପ୍ରଭୃତି\t-3549\n▁ଯୋଜନାରେ\t-3550\n▁ଦେଉଛନ୍ତି\t-3551\n▁ଦୋ\t-3552\nଜୀବନ\t-3553\n▁କ୍ୱ\t-3554\n▁ତିନ\t-3555\n▁ଦୂରରେ\t-3556\n▁ଶରୀରରେ\t-3557\n▁ସେତିକି\t-3558\n▁ଚଳଚ୍ଚିତ୍ର\t-3559\n▁ବିରୁଦ୍ଧରେ\t-3560\n▁ପ୍ରତିକ୍ରିୟା\t-3561\n~~\t-3562\nent\t-3563\nୀନ୍\t-3564\n▁ଥୁ\t-3565\n▁ଅବି\t-3566\n▁ରାଇ\t-3567\n▁କୋରା\t-3568\n▁ନାବା\t-3569\n▁ଅପରାଧ\t-3570\n▁ଆଗ୍ରହ\t-3571\n▁ନଥିବା\t-3572\n▁ଶୃଙ୍ଖ\t-3573\n▁ପାଖାପା\t-3574\n▁ସେପ୍ଟେ\t-3575\n▁ସୁଶାନ୍ତ\t-3576\n▁କଳାହାଣ୍ଡି\t-3577\n▁ପର୍ଯ୍ୟାୟରେ\t-3578\nଯେ\t-3579\n▁ବାଟ\t-3580\n▁ବୀର\t-3581\n▁ଏଥିରୁ\t-3582\n▁କ୍ଷୁଦ୍ର\t-3583\n▁ମନ୍ତ୍ରଣାଳୟ\t-3584\nଲରୁ\t-3585\n▁ପ୍ରତିର\t-3586\n▁ନଭେମ୍ବର\t-3587\nଲାଣ\t-3588\n▁ଛକ\t-3589\nବେଲେ\t-3590\n▁ପ୍ୟା\t-3591\n▁କ୍ରମେ\t-3592\n▁ଚତୁର୍ଥ\t-3593\n▁ପ୍ରଦୀପ\t-3594\n▁ବନ୍ଧୁଗଣ\t-3595\n▁ଭବିଷ୍ୟତ\t-3596\nଦୀର\t-3597\n▁ଛଡ଼ା\t-3598\n▁ଦୃଢ଼\t-3599\n▁ସାହିତ୍ୟର\t-3600\nମୁଁ\t-3601\n▁ଘୋ\t-3602\nଟୋବର\t-3603\n▁ପ୍ଲ\t-3604\n▁ପଦବୀ\t-3605\n▁ଅନନ୍ତ\t-3606\n▁ଲାଗିଲା\t-3607\n▁ସାକ୍ଷାତ\t-3608\n▁ଗ୍ରାମବାସୀ\t-3609\n▁ପ୍ରତିଶ୍ରୁ\t-3610\nରଣା\t-3611\nତ୍ସବ\t-3612\n▁ଦୁର୍ବଳ\t-3613\n▁ବୈଜ୍ଞାନ\t-3614\n▁ଡିସେମ୍ବର\t-3615\nପାଇ\t-3616\n▁୪୫\t-3617\n▁ଛୁଆ\t-3618\nଚିତ୍ର\t-3619\n▁ନାଁରେ\t-3620\n▁ଆକାଉଣ୍ଟ\t-3621\n▁ଗୁରୁତ୍ବ\t-3622\n▁ଥିବାବେଳେ\t-3623\n▁ଅଧିକାରୀଙ୍କ\t-3624\n।\\\t-3625\n▁ଖୋଜ\t-3626\n▁କାଗଜ\t-3627\n▁ପବିତ୍ର\t-3628\net\t-3629\nସ୍କୃତ\t-3630\n▁କରୁଛୁ\t-3631\nଉଳ\t-3632\n▁ଅକ\t-3633\n▁ପାଈ\t-3634\n▁ଦର୍ଶକ\t-3635\n▁ବର୍ଷରେ\t-3636\n▁ଅଧିନାୟକ\t-3637\n▁ଅନୁସନ୍ଧ\t-3638\n▁ମିଳିଥାଏ\t-3639\n▁ସାଙ୍ଗରେ\t-3640\nଦାସ\t-3641\nଷ୍ମ\t-3642\n▁କୈ\t-3643\n▁ମର\t-3644\nନ୍ସି\t-3645\n▁ଧର୍\t-3646\n▁ସେଠ\t-3647\nମ୍ଭୀର\t-3648\n▁ଦିଆଯାଇଥିଲା\t-3649\nଶନ\t-3650\n▁ଅନୁମାନ\t-3651\n▁ପରୀକ୍ଷ\t-3652\n▁3\t-3653\n▁କଂ\t-3654\n▁ଚର\t-3655\n▁ଅଶୋକ\t-3656\n▁ପିନ୍ଧି\t-3657\n▁ସମୀକ୍ଷା\t-3658\nକଳା\t-3659\n▁୨୦୨୦\t-3660\n▁କଳ୍ପନା\t-3661\n▁ୟୁକ୍ରେ\t-3662\n▁ଲୋକପ୍ରିୟ\t-3663\nପତ\t-3664\nଭଲ\t-3665\n▁ଲକ\t-3666\n▁ରଖିଛି\t-3667\n▁ଡିଗ୍ରୀ\t-3668\n▁ପୋଲିସ୍\t-3669\n▁ଜଣାଇଛନ୍ତି\t-3670\n▁;\t-3671\n▁ଘନ\t-3672\nଙ୍ଗର\t-3673\n▁ପାଠକ\t-3674\n▁ଭୋଟର\t-3675\n▁ପାଣ୍ଡ\t-3676\n▁ବାନ୍ଧି\t-3677\n▁ଭାଷାରେ\t-3678\n▁ଗରମ\t-3679\n▁ଭିଜି\t-3680\n▁ସେବନ\t-3681\n▁ଅକ୍ଟୋବର\t-3682\n▁ଦାୟିତ୍ବ\t-3683\nବୀର\t-3684\nମ୍ମଦ\t-3685\n▁ଆଦୌ\t-3686\n▁ମାଈ\t-3687\n▁ଦୁନି\t-3688\n▁କ୍ଷୀର\t-3689\n▁ବସ୍ତ୍ର\t-3690\nଭୁବନେଶ୍ବର\t-3691\n▁କଂଗ୍ରେସର\t-3692\nରେଜ\t-3693\nଣ୍ଡାର\t-3694\n▁ବୟାନ\t-3695\n▁ସ୍ନେହ\t-3696\n▁ପ୍ରଫେସର\t-3697\n▁ମେଡିକାଲ\t-3698\n▁ପ୍ରତିନିଧି\t-3699\n▁ତେଲ\t-3700\n▁ଇଣ୍ଡ\t-3701\n▁ବାଧା\t-3702\n▁ଖୋର୍ଦ୍ଧା\t-3703\nକୁଳ\t-3704\nଳାରେ\t-3705\n▁ସତେ\t-3706\n▁ବିଶ୍ୱର\t-3707\n▁ସିବିଆଇ\t-3708\n▁ଆସିଛନ୍ତି\t-3709\n▁ହାଇକୋର୍ଟ\t-3710\nଲ୍ସ\t-3711\n▁ପଡୁ\t-3712\n▁ଡାକି\t-3713\n▁ନୁହଁ\t-3714\n▁ବେଶୀ\t-3715\n▁ବ୍ରେ\t-3716\n▁ସହଜରେ\t-3717\n▁ପାରନ୍ତି\t-3718\n▁ସେପ୍ଟେମ୍ବର\t-3719\n▁ବେଦ\t-3720\nନଥିଲେ\t-3721\n▁କହୁଛନ୍ତି\t-3722\nପଡ଼\t-3723\nଣ୍ଠି\t-3724\nଥିବୀ\t-3725\nସହିତ\t-3726\n▁ଚଳା\t-3727\n▁୨୦୨୨\t-3728\n▁ପାର୍ଟି\t-3729\n▁ଉପଯୁକ୍ତ\t-3730\n▁ସମସ୍ୟାର\t-3731\nଥଲ\t-3732\nିକିଆ\t-3733\n▁ମ୍ୟାଚରେ\t-3734\n▁ଆଠ\t-3735\n▁ଦାମ\t-3736\n▁ମରି\t-3737\nଙ୍କଠାରୁ\t-3738\n▁ସର୍ବଦା\t-3739\n▁ପାଖାପାଖି\t-3740\nଶର\t-3741\nଶଳ\t-3742\n▁ଚରି\t-3743\n▁ଅଭିଯାନ\t-3744\n▁ସଂସ୍କୃତ\t-3745\n▁ପ୍ରତିଶ୍ରୁତି\t-3746\nol\t-3747\n▁ଏତ\t-3748\nଟାରେ\t-3749\nଠାକୁ\t-3750\n▁କୋଣ\t-3751\n▁ଏକଥା\t-3752\n▁ତ୍ୟାଗ\t-3753\n▁ହେବାରେ\t-3754\n▁ପଶ୍ଚିମବଙ୍ଗ\t-3755\nସିଏ\t-3756\nତ୍ମା\t-3757\n▁ଥାଉ\t-3758\nପୂର୍ବ\t-3759\nଭୁକ୍ତ\t-3760\n▁କ୍ରି\t-3761\n▁ଯାଉଛନ୍ତି\t-3762\n▁ଅନୁସନ୍ଧାନ\t-3763\nୃହ\t-3764\n▁L\t-3765\n▁n\t-3766\nଜକୁ\t-3767\n୍ଲାଇ\t-3768\n▁ପର୍ବ\t-3769\n▁ଫୋନ୍\t-3770\n▁ଗ୍ରାହକ\t-3771\n▁ବିଶେଷଜ୍ଞ\t-3772\n▁ସଦା\t-3773\n▁ଆଦେଶ\t-3774\n▁ଦୁଷ୍\t-3775\n▁ଦେହରେ\t-3776\n▁ସର୍ବୋ\t-3777\n▁ଏପ୍ରିଲ\t-3778\n▁ଅପରାହ୍ନ\t-3779\n▁କିଲୋମିଟର\t-3780\nକାରେ\t-3781\n▁କ୍ୟ\t-3782\n▁ଗଡ଼\t-3783\n▁ଲେଖୁ\t-3784\n▁ପ୍ରତୀ\t-3785\n▁ବୃଦ୍ଧ\t-3786\n▁ଆନ୍ଧ୍ର\t-3787\n▁ସନ୍ତୋଷ\t-3788\n▁ଯନ୍ତ୍ରଣା\t-3789\n▁ସମ୍ମୁଖରେ\t-3790\n▁ମାଲକାନଗିରି\t-3791\nଢ଼ୀ\t-3792\nନିଂ\t-3793\n▁ଶ୍\t-3794\n▁ଲେକେ\t-3795\n▁ସଂଯୋଗ\t-3796\n▁ଦୁର୍ନୀତି\t-3797\n▁ପର୍ଯ୍ୟାୟ\t-3798\nନଗର\t-3799\nୋଜିତ\t-3800\n▁ପୂରା\t-3801\n▁ସକ୍ରିୟ\t-3802\n▁ଧୂ\t-3803\n▁ଘଣ୍ଟ\t-3804\n▁ଟିକିଏ\t-3805\n▁ଆମେରିକାର\t-3806\nରର\t-3807\n▁ଅଛୁ\t-3808\n▁ନାଟ\t-3809\nୱେ\t-3810\nମିଲ\t-3811\n▁ସୁଧ\t-3812\n▁କ୍ଷେ\t-3813\n▁ମମତା\t-3814\n▁ସିନା\t-3815\nମାତ୍ରା\t-3816\n▁କାର୍ଡ\t-3817\nୀତି\t-3818\n▁ତହ\t-3819\nବଦ୍ଧ\t-3820\n▁ମଞ୍ଚ\t-3821\n▁ଶତ୍ରୁ\t-3822\n▁ସମାଜର\t-3823\n▁ଗ୍ରନ୍ଥ\t-3824\nଣର\t-3825\nଦାତା\t-3826\n▁ପାଗ\t-3827\n▁ମୋହ\t-3828\n▁ଇସ୍ତ\t-3829\n▁ଭବରେ\t-3830\n▁ପୃଥିବୀ\t-3831\n▁ବିଦ୍ୟୁତ\t-3832\nve\t-3833\nଗୁଳ\t-3834\nଜାଇ\t-3835\nଗୁଡା\t-3836\n▁ଇନ୍\t-3837\n▁ନାଇ\t-3838\nଉଥିଲେ\t-3839\nପୁରରେ\t-3840\n▁ଚାନ୍\t-3841\n▁ଦ୍ରୁତ\t-3842\nମୁମ୍ବାଇ\t-3843\n▁ବିଷ୍ଣୁ\t-3844\n▁ସ୍ମୃତି\t-3845\nନ୍ଦର୍ଯ୍ୟ\t-3846\n▁ସଂପୂର୍ଣ୍ଣ\t-3847\n▁କରିପାରନ୍ତି\t-3848\nth\t-3849\nୱାର\t-3850\nସ୍କୁ\t-3851\n▁୨ଟି\t-3852\nସଭାରେ\t-3853\n▁ଆଗେଇ\t-3854\n▁ଛୁଇଁ\t-3855\nନ୍ତୋଷ\t-3856\nମୁଖ୍ୟ\t-3857\n▁ଉନ୍ନତ\t-3858\n▁ପରିଶ୍ରମ\t-3859\n▁ଅନ୍ୟପକ୍ଷରେ\t-3860\n୧୮\t-3861\nଡ଼କ\t-3862\nଣିକ\t-3863\nମ୍ଳ\t-3864\n▁୭୦\t-3865\nନ୍ତର\t-3866\n▁କଅଣ\t-3867\nଶିଷ୍ଟ\t-3868\nଶ୍ବାସ\t-3869\n▁ଯେପର\t-3870\n▁ମୁହଁରେ\t-3871\n▁କୋରାପୁଟ\t-3872\n▁ଦୃଷ୍ଟିରୁ\t-3873\nଡ଼ର\t-3874\nପର୍\t-3875\n▁ଦର୍\t-3876\nାର୍ଥୀ\t-3877\n▁ବେଶି\t-3878\n▁ମିଲି\t-3879\n▁ଆତ୍ମା\t-3880\n▁କୋହଲି\t-3881\n▁ଲେଖାଏଁ\t-3882\n▁ନିର୍ଦ୍ଦିଷ୍ଟ\t-3883\nପଟ\t-3884\nବୋ\t-3885\nତାରି\t-3886\nମ୍ପି\t-3887\nହ୍ମା\t-3888\n▁ଭବନ\t-3889\nେଶ୍ବର\t-3890\n▁ଯାଦବ\t-3891\n▁ପରମ୍ପ\t-3892\n▁ଜ୍ୟୋତି\t-3893\n▁ତାପମାତ୍ରା\t-3894\n୨୨\t-3895\nଗତି\t-3896\n▁କଥ\t-3897\n▁ବ୍\t-3898\nଞ୍ଚା\t-3899\nସାଗର\t-3900\n▁ମୋହନ\t-3901\n▁ନେଇଛି\t-3902\n▁କମିଶନର\t-3903\n▁ହୋଇଗଲେ\t-3904\n▁ଦେଖାଦେଇ\t-3905\n▁ଢେଙ୍କାନା\t-3906\n▁ଯାଇଛନ୍ତି\t-3907\nce\t-3908\n▁ଐତି\t-3909\n▁ମାଓ\t-3910\n▁୩ଟି\t-3911\n▁କବିତ\t-3912\n▁ଚାଲାଣ\t-3913\n▁ବର୍ଷୀୟ\t-3914\n▁ପ୍ରତିବାଦ\t-3915\n▁ସୁରକ୍ଷିତ\t-3916\n▁ଯାନ\t-3917\n▁ଚୀନ୍\t-3918\n▁ରହସ୍ୟ\t-3919\n▁ଦିଅନ୍ତି\t-3920\nଦଳ\t-3921\n▁ଉଠୁ\t-3922\n▁ସରଳ\t-3923\n▁ପ୍ଲେ\t-3924\n▁ସାହି\t-3925\n▁କିଶୋର\t-3926\n▁ହୋଇଥାନ୍ତି\t-3927\n▁ସୁପ୍ରିମକୋର୍ଟ\t-3928\new\t-3929\nଲତ\t-3930\nଦୂର\t-3931\n▁ଯଶ\t-3932\nତ୍ତମ\t-3933\n▁ଅତ୍ୟା\t-3934\n▁ଦୈନିକ\t-3935\n▁କଳାକାର\t-3936\n▁ପେଟ୍ରୋଲ\t-3937\nକେଟ\t-3938\nଟିଆ\t-3939\nଡ଼ିଓ\t-3940\nଲ୍ୟା\t-3941\n▁ଫୁଟ\t-3942\nବେଳକୁ\t-3943\nଭାବିକ\t-3944\n▁ମୟୂର\t-3945\n▁ସାଧୁ\t-3946\n▁ମାସ୍କ\t-3947\n▁ଅଟନ୍ତି\t-3948\n▁ଘଟିଥିବା\t-3949\n▁ରଖାଯାଇଛି\t-3950\n▁କର୍ତ୍ତୃପକ୍ଷ\t-3951\nଡ଼ାଇ\t-3952\nସଂଖ୍ୟା\t-3953\n▁ଜଣଙ୍କର\t-3954\n▁ହୋଇନାହିଁ\t-3955\n▁ନମ\t-3956\n▁ଶୂନ୍ୟ\t-3957\n▁ସରପଞ୍ଚ\t-3958\n▁ପରିସରରେ\t-3959\n▁ସୌନ୍ଦର୍ଯ୍ୟ\t-3960\nst\t-3961\nଜୀବୀ\t-3962\n▁ଜିତ\t-3963\n▁ଫସଲ\t-3964\n▁ସମୃ\t-3965\n▁ଆଣିବା\t-3966\n▁ବିଜ୍ଞ\t-3967\n▁ସ୍ଥିତିରେ\t-3968\n▁ପରିସ୍ଥିତିରେ\t-3969\nଂଜ\t-3970\nଘନ\t-3971\nଠିତ\t-3972\n▁ଅମର\t-3973\n▁ଅକ୍ଷୟ\t-3974\n▁ବିଜୟୀ\t-3975\n▁ପ୍ରତିଷ୍ଠ\t-3976\n▁ପ୍ରସିଦ୍ଧ\t-3977\nଅର\t-3978\nଷ୍ଟେ\t-3979\n୍ୟାମ\t-3980\n୍ୟ୍ୟ\t-3981\n▁ବିତ\t-3982\n▁ଯୋଗ୍ୟ\t-3983\n▁ବାର୍ଷିକ\t-3984\n୨୧\t-3985\nଉଠି\t-3986\n▁ଡାଇ\t-3987\n▁ପଂଚ\t-3988\nନଥିବା\t-3989\nାର୍ଗେ\t-3990\n▁ଗଣେଶ\t-3991\n▁ରୋଜଗାର\t-3992\n▁ପହଞ୍ଚିଛି\t-3993\n୍ରୋ\t-3994\nକ୍ଷକ\t-3995\nମ୍ଭବ\t-3996\nମ୍ମୁ\t-3997\nଳୀରେ\t-3998\n▁ଅଦା\t-3999\n▁ଖାନ\t-4000\n▁କଡ଼ା\t-4001\n▁ପିଟି\t-4002\n▁ଈଶ୍ବର\t-4003\n▁ବନ୍ଦର\t-4004\n▁ପାଣିରେ\t-4005\n▁ପ୍ରତାପ\t-4006\n▁ପ୍ରକୃତରେ\t-4007\n▁ବଲାଙ୍ଗିର\t-4008\n▁ଉଇ\t-4009\n▁ତୁମ୍ଭ\t-4010\nଷ୍ଟ୍ରୀୟ\t-4011\nam\t-4012\nଫର\t-4013\nାକ\t-4014\n▁ମଇ\t-4015\n▁ମଜ\t-4016\n▁ପୂଜ\t-4017\n▁ବ୍ୟୟ\t-4018\n▁ପୃଷ୍ଠ\t-4019\n▁ବୟସରେ\t-4020\n▁ମିଡିଆ\t-4021\n▁ସୈନ୍ୟ\t-4022\n▁ସାମନ୍ତ\t-4023\n▁ନେଇଥିବା\t-4024\n▁5\t-4025\n▁ହଳ\t-4026\n▁ଚାଇ\t-4027\n▁ନିଃ\t-4028\n▁ସଂଯ\t-4029\nକାଣ୍ଡ\t-4030\n▁ତାଲି\t-4031\n▁ଭ୍ରମ\t-4032\nପ୍ରକାଶ\t-4033\n▁ଗଙ୍ଗା\t-4034\n▁ଝାରସୁ\t-4035\nଯାଉଥିବା\t-4036\n▁ଟାର୍ଗେ\t-4037\n▁କରୁଥିଲା\t-4038\n▁ପାରିବାର\t-4039\n▁ସଂପୃକ୍ତ\t-4040\n▁ବୈଜ୍ଞାନିକ\t-4041\nକୀ\t-4042\nଶମ\t-4043\nଭ୍ୟ\t-4044\n▁ଋଷ\t-4045\nଡ଼ିଏ\t-4046\nଡ଼ିତ\t-4047\nଡିକୁ\t-4048\nଢ଼େଇ\t-4049\nିତ୍ତ\t-4050\nକୁମାର\t-4051\nହାସିକ\t-4052\n▁ଭାରି\t-4053\nଥାନ୍ତା\t-4054\n▁ଇସ୍ତଫା\t-4055\n▁ପ୍ରସାର\t-4056\n▁ସେଠାରୁ\t-4057\nବିର\t-4058\n▁ଆନୁ\t-4059\n▁ଟିମ\t-4060\n▁ଶତକ\t-4061\n▁ଆକାର\t-4062\n▁ପାରା\t-4063\n▁ହୋଟେ\t-4064\n▁ଘଟଣାକୁ\t-4065\n▁ବଜାରରେ\t-4066\n▁ଆତଙ୍କବାଦୀ\t-4067\n▁ପ୍ରାର୍ଥନା\t-4068\n।\"\t-4069\nଶହ\t-4070\n▁ଚୂ\t-4071\nକ୍ସନ\t-4072\nପାଲି\t-4073\n▁ସଫା\t-4074\nଓଡ଼ିଆ\t-4075\nବସ୍ଥା\t-4076\n▁ଏତିକି\t-4077\n▁ପାଇଲେ\t-4078\n▁ଯୁବତୀ\t-4079\n▁ସମିତି\t-4080\n▁ରଖନ୍ତୁ\t-4081\n୩୦\t-4082\n▁ପଣ\t-4083\n▁ଋତୁ\t-4084\n▁ବାମ\t-4085\nଡାଉନ୍\t-4086\n▁କଥାରେ\t-4087\n▁ଭୟଙ୍କର\t-4088\n▁ସମ୍ପୃକ୍ତ\t-4089\n▁୩୨\t-4090\nସ୍କର\t-4091\n▁ଏପର\t-4092\n▁ଅର୍ଜୁ\t-4093\n▁ଚରିତ୍ର\t-4094\n▁କରିପାରେ\t-4095\n▁କେନ୍ଦ୍ରୀୟ\t-4096\nଘାଟନ\t-4097\nନ୍ତୀ\t-4098\nଲାଣ୍ଡ\t-4099\n▁ଗ୍ରୁ\t-4100\n▁ସ୍ନାନ\t-4101\nଚାର୍ଯ୍ୟ\t-4102\nଏହି\t-4103\nଜ୍ଜ\t-4104\n୪ରେ\t-4105\n▁ସଠ\t-4106\nଣ୍ଟନ\t-4107\nାୟତର\t-4108\n▁ନାକ\t-4109\n▁ପୁଷ୍ପ\t-4110\n▁ବଦଳରେ\t-4111\n▁ବିଦ୍ୟ\t-4112\n▁ରଞ୍ଜନ\t-4113\n▁ସେମିତି\t-4114\n▁କରିବାରୁ\t-4115\n▁ଜାଣିବାକୁ\t-4116\n▁ଘୃ\t-4117\n▁ଭରି\t-4118\n▁ଆଘାତ\t-4119\n▁ଛନ୍ଦ\t-4120\n▁ପିଛା\t-4121\n▁ବକ୍ତ\t-4122\nଦ୍ବାରା\t-4123\n▁ପରିବାରକୁ\t-4124\n▁ବାଲେଶ୍ବର\t-4125\nଏନ\t-4126\nଏହା\t-4127\nଗୋଳ\t-4128\nସ୍ତେ\t-4129\nହିନୀ\t-4130\n▁ଡ଼ା\t-4131\n▁ହେଉଛୁ\t-4132\nମେନ୍ଦ୍ର\t-4133\n▁ଭିଜିଲାନ୍ସ\t-4134\nଚରା\t-4135\n▁ଆଖ\t-4136\n▁ମାଝ\t-4137\n▁କଣ୍ଟ\t-4138\n▁ବାଜି\t-4139\n▁ସଂଶୋଧ\t-4140\n▁ସଂଘର୍ଷ\t-4141\n▁ଅଧ୍ୟାପକ\t-4142\nel\t-4143\nାଳ\t-4144\nଣ୍ଢ\t-4145\nବେଗ\t-4146\n▁ଧମକ\t-4147\n▁ଧାର୍\t-4148\n▁ହୋଇଛୁ\t-4149\n▁ତିନୋଟି\t-4150\n▁ପାଣିଗ୍ରା\t-4151\n▁ସନ୍ଧ୍ୟାରେ\t-4152\n--\t-4153\nଟିକ\t-4154\nସାମ\t-4155\nପନ୍ନ\t-4156\nବାଣୀ\t-4157\n▁ଜନ୍\t-4158\n▁ଭଞ୍ଜ\t-4159\n▁ସମ୍ବ\t-4160\n▁୨୦୨୧\t-4161\nସ୍ତରୀୟ\t-4162\n▁ତାଲିକ\t-4163\n▁ସ୍ଥଳରେ\t-4164\n▁ସ୍ବରୂପ\t-4165\n▁ଫିଲ୍ମରେ\t-4166\n▁ହୁଅନ୍ତୁ\t-4167\n▁ରାଜସ୍ଥାନ\t-4168\n▁ସାହାଯ୍ୟରେ\t-4169\nଜାନ\t-4170\nଥାଉ\t-4171\n▁ବହ\t-4172\nୀକରଣ\t-4173\n୍ରାମ\t-4174\n▁ମନକୁ\t-4175\n▁ମିଟର\t-4176\nପ୍ରକାର\t-4177\n▁କରିଦେ\t-4178\n▁ଭିତରୁ\t-4179\n▁ମଣିଷର\t-4180\n▁ଯୋଗାଣ\t-4181\nକାର୍ଯ୍ୟ\t-4182\n▁ସାଧାରଣତଃ\t-4183\n▁ହସ୍ପିଟାଲ\t-4184\nଭବନ\t-4185\nଭୁତ\t-4186\nମାନ୍\t-4187\nମିନି\t-4188\n▁ଓଜନ\t-4189\n▁କେର\t-4190\n▁ଅନ୍ୟତମ\t-4191\n▁ପଡ଼ିଛି\t-4192\n▁ଠକ\t-4193\nସେନା\t-4194\n▁ତାଳ\t-4195\n▁ଇଞ୍ଜ\t-4196\n▁ଛୁଟି\t-4197\n▁ଯୋଗେ\t-4198\n▁ମୁଣ୍ଡରେ\t-4199\n▁ନେତୃତ୍ୱରେ\t-4200\n▁ଆସନ୍ତାକାଲି\t-4201\n▁ଜୈ\t-4202\n▁କରୋ\t-4203\n▁ପାପ\t-4204\n▁ଫୋନ\t-4205\n▁ରହେ\t-4206\n▁ଦିନର\t-4207\n▁ଦେଲା\t-4208\n▁ସେଠି\t-4209\n▁ସମ୍ବାଦ\t-4210\n▁ପ୍ରାକୃତ\t-4211\n▁ମନ୍ତବ୍ୟ\t-4212\n▁ଅଭିଯୋଗରେ\t-4213\n▁ବିଦ୍ୟୁତ୍\t-4214\n୧୫\t-4215\nପଦା\t-4216\nବେଦ\t-4217\nସକୁ\t-4218\n▁କପି\t-4219\n▁ଚୟନ\t-4220\n▁ନେବେ\t-4221\n▁ବର୍ଗ\t-4222\n▁ବିବର\t-4223\n▁ଗୋବିନ୍ଦ\t-4224\n▁ନିୟୋଜିତ\t-4225\n▁ପିଲାଙ୍କ\t-4226\n▁ରଖିଥିଲେ\t-4227\n▁ମୁଖ୍ୟମନ୍ତ୍ରୀଙ୍କ\t-4228\nରତ\t-4229\n▁ଧ୍\t-4230\n▁ୱା\t-4231\nଗୁରୁ\t-4232\nଥିଲୁ\t-4233\n▁କଲ୍\t-4234\n▁ଭାବୁ\t-4235\n▁ନୟାଗଡ଼\t-4236\n▁ବିଶ୍ୱକ\t-4237\n▁ଡ୍ରାଇଭର\t-4238\n▁୮୦\t-4239\nଶ୍ୱର\t-4240\n▁ହିତା\t-4241\n▁ତୀବ୍ର\t-4242\n▁କୁହନ୍ତି\t-4243\n▁ଘଟିଥିଲା\t-4244\nମଳ\t-4245\nଚାପ\t-4246\nତନା\t-4247\nବସାଇ\t-4248\nସମ୍ପ\t-4249\n▁ଆଣ୍ଡ\t-4250\n▁ପାଟି\t-4251\n▁ବସନ୍ତ\t-4252\n▁ରାଜନୀ\t-4253\n▁ଆମେରିକ\t-4254\n▁ତାଲିକା\t-4255\n▁ପରିବହନ\t-4256\n▁କନ୍ଧମାଳ\t-4257\n▁ବ୍ୟାଟିଂ\t-4258\nକ୍ଷେତ୍ରରେ\t-4259\n▁ରାଜ୍ୟପାଳ\t-4260\nକଲ\t-4261\nଶୟ\t-4262\n▁F\t-4263\n▁e\t-4264\nକିଲ\t-4265\n▁୩୭\t-4266\nକାଶେ\t-4267\n▁ତଳକୁ\t-4268\n▁ବ୍ଲକର\t-4269\n▁ଭକ୍ତି\t-4270\n▁ନିୟମିତ\t-4271\n▁ବାର୍ତ୍ତା\t-4272\n▁ପାରିବାରିକ\t-4273\nଏଲ୍\t-4274\nରହି\t-4275\nଶାଳ\t-4276\n▁ଦିଏ\t-4277\n▁ପଦକ\t-4278\n▁ଶପଥ\t-4279\n▁ଆଦାୟ\t-4280\n▁ଚଳାଇ\t-4281\n▁ଦଳକୁ\t-4282\n▁ବାସି\t-4283\nଯାଇଥାଏ\t-4284\n▁ପାଇଥିଲେ\t-4285\n▁ସ୍କୁଲରେ\t-4286\n▁ସ୍ଥାନକୁ\t-4287\n▁ଆୟୁ\t-4288\n▁ଶୁଭେ\t-4289\n▁କ୍ଷମା\t-4290\n▁ଗ୍ୟାସ\t-4291\n▁ବିକ୍ଷ\t-4292\n▁ଇଂଲଣ୍ଡ\t-4293\n▁ପରିଚିତ\t-4294\n▁ମୋବାଇଲ\t-4295\nଷେ\t-4296\n▁କହଲ\t-4297\nପୁରୁଷ\t-4298\n▁ମନୋର\t-4299\n▁ସନ୍ତୁ\t-4300\n▁ଦ୍ରବ୍ୟ\t-4301\n▁ମଲ୍ଲିକ\t-4302\n▁ପରମ୍ପରା\t-4303\n▁କେନ୍ଦ୍ରରେ\t-4304\nia\t-4305\nro\t-4306\nହାନ\t-4307\n▁ସଲ\t-4308\nପୁରୀ\t-4309\n▁ଚକ୍ର\t-4310\n▁ମଞ୍ଜ\t-4311\n▁କିଭଳି\t-4312\n▁କଥାବାର\t-4313\n▁ମହିଳାଙ୍କୁ\t-4314\n▁୭୫\t-4315\nତ୍ରୀ\t-4316\n▁ଗର୍\t-4317\n▁ବାଣ\t-4318\nଙ୍ଗରେ\t-4319\nଧାନରେ\t-4320\nସ୍ଥାର\t-4321\n▁ଜୁନ୍\t-4322\n▁କ୍ରାଇ\t-4323\n▁ବର୍ଷରୁ\t-4324\n▁ବ୍ୟବହୃତ\t-4325\n▁ବ୍ୟୁରୋଃ\t-4326\n▁ଯୋଗାଯୋଗ\t-4327\n▁ସାହିତ୍ୟରେ\t-4328\n▁th\t-4329\n▁ଡୋ\t-4330\n▁ୟା\t-4331\nପ୍ରଦାୟ\t-4332\n▁କୁମ୍ଭ\t-4333\n▁ବାବଦରେ\t-4334\n▁ବିଜେପିର\t-4335\nଭୋ\t-4336\n▁E\t-4337\nବାନ୍\t-4338\n▁ପରମା\t-4339\nବ୍ୟୁରୋ\t-4340\n▁ତାମିଲ\t-4341\n▁ଦେଶକୁ\t-4342\n▁କମ୍ପାନ\t-4343\n▁ମିନିଟ୍\t-4344\n▁ଅଳଙ୍କାର\t-4345\n▁ସଂଗ୍ରାମ\t-4346\n▁ସୁନ୍ଦରଗଡ଼\t-4347\nଳଖ\t-4348\nଂଚି\t-4349\n▁କଦ\t-4350\nତ୍ମକ\t-4351\n▁ଅନ୍\t-4352\n▁ବାପ\t-4353\nିତ୍ରୀ\t-4354\n▁ଅପରେ\t-4355\n▁ଧରଣର\t-4356\n▁ନେତୃ\t-4357\n▁ଅସ୍ଥି\t-4358\n▁ତହସିଲ\t-4359\n▁ହିଂସା\t-4360\n▁ଆଧାରରେ\t-4361\n▁ଭିତ୍ତି\t-4362\n▁ଷ୍ଟାଡି\t-4363\n▁ଜାନୁଆରୀ\t-4364\n▁ବ୍ୟକ୍ତିଙ୍କୁ\t-4365\n▁ନଗ\t-4366\n▁ପହଁ\t-4367\nଶଙ୍କର\t-4368\n▁ଅର୍ଜନ\t-4369\n▁ଇଣ୍ଟର\t-4370\n▁ସମାଜରେ\t-4371\n▁କର୍ଣ୍ଣା\t-4372\n▁ଦିନିକିଆ\t-4373\nୀରା\t-4374\nଭାରତ\t-4375\n▁ଧନୁ\t-4376\n▁ଉତ୍ତେ\t-4377\nଯାଇଥିଲେ\t-4378\n▁ଗାନ୍ଧି\t-4379\n▁କରାଯାଇଥାଏ\t-4380\nବ୍ରତ\t-4381\n▁ଅଟୋ\t-4382\n▁ପଛେ\t-4383\n▁ରଜା\t-4384\n▁ନକରି\t-4385\n▁ନଥାଏ\t-4386\n▁ମତରେ\t-4387\n▁ସାଗର\t-4388\nଯନ୍ତ୍ର\t-4389\n▁ସ୍ମରଣ\t-4390\n▁ଅସୁସ୍ଥ\t-4391\n▁ପର୍ଯ୍ୟଟକ\t-4392\nାବୁ\t-4393\n▁ନଅ\t-4394\nିତ୍ୱ\t-4395\n▁ବଳି\t-4396\nଷ୍ଟିକ\t-4397\n▁ଗୋଡ଼\t-4398\n▁ବାକ୍ୟ\t-4399\n▁ରାଶିର\t-4400\n▁ଉପନ୍ୟାସ\t-4401\n▁ଟଙ୍କାରେ\t-4402\n▁ହୋଇପଡ଼ି\t-4403\n▁ବିଚାରପତି\t-4404\n▁H\t-4405\nପାଖ\t-4406\n▁ଓଲ\t-4407\nପୁରର\t-4408\n▁ନିମ\t-4409\n▁ଶୋକ\t-4410\n▁ଆକଳନ\t-4411\n▁ମୁକୁ\t-4412\n▁ସମର୍\t-4413\nଯିବାକୁ\t-4414\n▁ଫେରାର\t-4415\n▁ବୋଧହୁଏ\t-4416\nଜଗ\t-4417\nୟିକ\t-4418\nସିଟି\t-4419\n▁ସଜା\t-4420\nଲମ୍ବନ\t-4421\n▁ଚାଲୁ\t-4422\n▁ଦଳରେ\t-4423\n▁ବିଫଳ\t-4424\n▁ଗ୍ରନ୍\t-4425\n▁ବୈଷ୍ଣ\t-4426\n▁ଶଙ୍କର\t-4427\n▁ସକ୍ଷମ\t-4428\n▁ସଭାରେ\t-4429\nଥାଆନ୍ତି\t-4430\nଢ଼େ\t-4431\n▁ଗର\t-4432\n▁ପଳ\t-4433\n▁ଏଜେ\t-4434\n▁ନବର\t-4435\n▁୧୯୭\t-4436\n▁ଜନିତ\t-4437\n▁ସେଦିନ\t-4438\n▁ବିଦେଶୀ\t-4439\n▁କୋର୍ଟରେ\t-4440\n▁ଅନ୍ୟମାନଙ୍କ\t-4441\nୟମ୍\t-4442\n▁କହେ\t-4443\n▁କୋଇ\t-4444\n▁ବୃହ\t-4445\n▁ମନର\t-4446\n▁ଲକ୍\t-4447\n▁୫ଟି\t-4448\n▁ଅତିକ\t-4449\n▁ଉପଯୋଗ\t-4450\n▁ଉନ୍ନୟନ\t-4451\n▁ମହମ୍ମଦ\t-4452\n▁ଲୋକସଭା\t-4453\n▁ଧାର୍ମିକ\t-4454\n▁ପ୍ରସନ୍ନ\t-4455\n▁ମାନ୍ୟତା\t-4456\n▁ଅଷ୍ଟ୍ରେଲିଆ\t-4457\n▁4\t-4458\n▁ନୃ\t-4459\nତ୍ସା\t-4460\n▁କବା\t-4461\n▁ହେଁ\t-4462\n▁ଗ୍ରହ\t-4463\n▁ଭୂତା\t-4464\nବର୍ଣ୍ଣ\t-4465\nର୍ଯ୍ୟା\t-4466\n▁ଇଂରେଜ\t-4467\n▁ଲିଖିତ\t-4468\n▁ତୁଳନାରେ\t-4469\n▁ନିର୍ମିତ\t-4470\nକକୁ\t-4471\nଥ୍ୟା\t-4472\n▁ଅଣା\t-4473\nକୌଣସି\t-4474\nବାହିତ\t-4475\nଲ୍ଲୀର\t-4476\n▁ଅନୁପ\t-4477\n▁ସମୟର\t-4478\n▁ତାପରେ\t-4479\n▁ସିନେମା\t-4480\n▁ଉତ୍ତରପ୍ରଦେଶ\t-4481\nୁଆ\t-4482\nକେଲ\t-4483\n▁ଅଯ\t-4484\nଟାରୁ\t-4485\n▁୧୯୮\t-4486\nଭାଗ୍ୟ\t-4487\n▁ଓଡିଆ\t-4488\n▁ଦାୟୀ\t-4489\n▁ପଦ୍ଧ\t-4490\n▁ଗୁପ୍ତ\t-4491\n▁ପ୍ରମୋ\t-4492\n▁ଦାସଙ୍କ\t-4493\n▁ଶିକ୍ଷୟ\t-4494\n▁ପ୍ରଶାନ୍ତ\t-4495\n▁ବିଶ୍ୱବିଦ୍ୟାଳୟ\t-4496\nୱାଲ\t-4497\n▁୩୩\t-4498\nବ୍ରା\t-4499\n▁କଳି\t-4500\n▁ଧଳା\t-4501\n▁ଫ୍ର\t-4502\n▁ମହୋ\t-4503\nକ୍ରମେ\t-4504\n▁ଏମିତ\t-4505\n▁ଗନ୍ଧ\t-4506\n▁ସଚେତନତା\t-4507\n▁ପରିବର୍ତ୍ତ\t-4508\nଏନ୍\t-4509\nକରୁ\t-4510\nଖରୀ\t-4511\nକ୍ଷୁ\t-4512\nଧାରା\t-4513\n▁ଏସି\t-4514\nାନନ୍ଦ\t-4515\n▁କଥାକୁ\t-4516\n▁ବୃତ୍ତ\t-4517\n▁ମାର୍ଗ\t-4518\n▁ସଂଖ୍ୟ\t-4519\n▁ସଞ୍ଜୟ\t-4520\n▁ବ୍ରହ୍ମା\t-4521\n▁ରୋଗୀଙ୍କ\t-4522\n▁ପ୍ରତିକାର\t-4523\nକ୍ଷି\t-4524\nାର୍ଜ\t-4525\n▁ବୋଉ\t-4526\nସ୍ପତି\t-4527\n▁କୋଲକ\t-4528\n▁ଫେବୃ\t-4529\n▁ରୋହିତ\t-4530\n▁ଲମ୍ବା\t-4531\n▁ଲାଗିଲେ\t-4532\n▁ସଂଖ୍ୟାରେ\t-4533\n▁ଶ୍ରୀକୃଷ୍ଣ\t-4534\n▁ଧର୍ମେନ୍ଦ୍ର\t-4535\nସମ୍\t-4536\n▁ତୃ\t-4537\n▁ନଜ\t-4538\nଯିବେ\t-4539\n▁ଡଲାର\t-4540\n▁ଦିବ୍ୟ\t-4541\nକ୍ଷୀ\t-4542\n▁ଅଲି\t-4543\nଅନ୍ତି\t-4544\nମୀଙ୍କ\t-4545\n▁ଅବହେ\t-4546\n▁ଚଢ଼ା\t-4547\n▁ଲାଇକ\t-4548\nଶିକ୍ଷା\t-4549\n▁ମୁହାଁ\t-4550\n▁ସ୍କୋର\t-4551\n▁କ୍ରିକେ\t-4552\n▁ନିଯୁକ୍ତ\t-4553\n▁ଦିଲ୍ଲୀରେ\t-4554\n▁ଜଗତସିଂହପୁର\t-4555\nଝା\t-4556\n▁[\t-4557\nପାଲ\t-4558\nଷାର\t-4559\nବନ୍ଦ\t-4560\nଶୁଣା\t-4561\nନଥିଲା\t-4562\nନ୍ନାଇ\t-4563\n୍ୟାନ୍\t-4564\n▁ଅବଗତ\t-4565\n▁ଶୋଭା\t-4566\n▁ସକାଶେ\t-4567\nରାଷ୍ଟ୍ର\t-4568\n▁ଅଭ୍ୟାସ\t-4569\n▁ଉତ୍ସାହ\t-4570\n▁ଭାରତକୁ\t-4571\n▁ହେଉଥିଲେ\t-4572\n▁ଆସୁଛନ୍ତି\t-4573\n▁କର୍ମଚାରୀଙ୍କ\t-4574\nir\t-4575\n▁G\t-4576\nକିଂ\t-4577\nଥଲେ\t-4578\nନାଶ\t-4579\nଷିତ\t-4580\n▁୩୬\t-4581\nଛନ୍ତ\t-4582\nବାଚନ\t-4583\n▁ଲୁହ\t-4584\n▁ଲଗାତ\t-4585\n▁ବିକ୍ଷୋଭ\t-4586\n▁ପହଞ୍ଚିବା\t-4587\n▁ଲୋକମାନଙ୍କୁ\t-4588\nଗୁଆ\t-4589\nସ୍ମ\t-4590\nଖାନା\t-4591\n▁ଜେଲ\t-4592\n▁ଟ୍ୟା\t-4593\n▁ଶାସକ\t-4594\n▁ସପ୍ତ\t-4595\n▁୨୦୧୮\t-4596\n▁ଆସିବେ\t-4597\n▁ବୋର୍ଡ\t-4598\n▁ହୋଇଯାଇଥିଲା\t-4599\n୨୩\t-4600\nୂଳକ\t-4601\nପଡ଼ି\t-4602\nବର୍ତ\t-4603\nହାତି\t-4604\nଶକ୍ତି\t-4605\n▁ଅଟକି\t-4606\n▁ପୁନଃ\t-4607\n▁ଯଜ୍ଞ\t-4608\n▁ଜାପାନ\t-4609\n▁ବିସ୍ଫୋ\t-4610\n▁ହିତାଧିକାରୀ\t-4611\n▁ଏଲ\t-4612\nଣ୍ଟ୍\t-4613\nାବଳୀ\t-4614\n▁ଉନ୍\t-4615\n▁କପର\t-4616\n▁ପିଲ\t-4617\n▁ଦଳୀୟ\t-4618\n▁ବାସୁ\t-4619\n▁ଭଙ୍ଗ\t-4620\n▁ମୋଟ୍\t-4621\n▁ରାତ୍ର\t-4622\nାତ୍ରାରେ\t-4623\n▁ଅନ୍ଧାର\t-4624\n▁ଅସନ୍ତୋଷ\t-4625\n▁କାଶ୍ମୀର\t-4626\n▁ଉପସ୍ଥାପନ\t-4627\nରକେ\t-4628\n▁ଏକର\t-4629\n▁ପେଟ\t-4630\n▁ବହନ\t-4631\n▁ଲଘୁ\t-4632\nଲକ୍ଷ୍\t-4633\n▁ଅମୃତ\t-4634\n▁କଠିନ\t-4635\n▁ଗୌରବ\t-4636\n▁ବେଆଇ\t-4637\n▁ଥଣ୍ଡା\t-4638\n▁ଶୀର୍ଷ\t-4639\n▁ଉପସ୍ଥି\t-4640\n▁ପଜିଟିଭ\t-4641\n▁ମ୍ୟାନେ\t-4642\n▁ୱେବସାଇ\t-4643\n▁ପଜିଟିଭ୍\t-4644\nnd\t-4645\nଏଫ\t-4646\nସକ\t-4647\nish\t-4648\nଯୁଗ\t-4649\nାବ୍\t-4650\nଏଣ୍ଟ\t-4651\nଗଡ଼ା\t-4652\nଭୂତି\t-4653\n▁ଟୁଇ\t-4654\n▁ରୋଷ\t-4655\n▁ନେଲେ\t-4656\n▁ବିନି\t-4657\n▁କୁକୁର\t-4658\n▁ଦେଉଛି\t-4659\n▁ରୋଗରେ\t-4660\n▁ନାବାଳିକା\t-4661\n▁ମୟୂରଭଞ୍ଜ\t-4662\n▁କରିବାପାଇଁ\t-4663\n▁ଲୋକମାନଙ୍କ\t-4664\n▁ସ୍ବାଧୀନତା\t-4665\nଚୀ\t-4666\n୍ରିକ\t-4667\n▁ଆପେ\t-4668\n▁ଦୟା\t-4669\n▁ନୀଳ\t-4670\n▁ବରା\t-4671\nଞ୍ଚଳିକ\t-4672\n▁ଧର୍ମର\t-4673\n▁ବରଗଡ଼\t-4674\n▁ଗମ୍ଭୀର\t-4675\n▁ପ୍ରାକୃତିକ\t-4676\nur\t-4677\n୧୬\t-4678\nାବୀ\t-4679\n▁ଝର\t-4680\nକ୍ଷର\t-4681\nରାରେ\t-4682\nସିବି\t-4683\nାମିନ\t-4684\n▁ଅଧା\t-4685\n▁କେବଲ\t-4686\n▁ଗୋଟି\t-4687\n▁ଚୋରି\t-4688\n▁ମାଂସ\t-4689\n▁ପ୍ରବନ୍ଧ\t-4690\n▁ମହାତ୍ମା\t-4691\n▁ଗଣମାଧ୍ୟମ\t-4692\n▁ସମ୍ମାନିତ\t-4693\n▁ପଠ\t-4694\n▁୩୪\t-4695\nଫଳରେ\t-4696\nସନରେ\t-4697\n▁ଗୋଲ\t-4698\nଶ୍ୱାସ\t-4699\n▁ଜାଗା\t-4700\n▁ଟ୍ରେନ\t-4701\n▁ପଡିଛି\t-4702\n▁ଜୀବନକୁ\t-4703\n▁ଉତ୍ସବରେ\t-4704\n▁ମାତ୍ରାରେ\t-4705\n▁ଡାକ୍ତରଖାନାରେ\t-4706\nନାମ\t-4707\nୟାରୀ\t-4708\n▁ଅଧୁ\t-4709\n▁ଆପ୍\t-4710\n▁ପଢି\t-4711\n▁ଶୁଖି\t-4712\n▁ସାଜି\t-4713\n▁ଖଣ୍ଡେ\t-4714\n▁ଧକ୍କା\t-4715\n▁ପଚରାଉ\t-4716\n▁ବଢ଼ାଇ\t-4717\n▁ଅଧିବେଶ\t-4718\n▁କଥାବାର୍ତ୍ତା\t-4719\nୋଶୀ\t-4720\n▁ଛଅ\t-4721\n....\t-4722\nଣ୍ଟାଇ\t-4723\n▁ଆସାମ\t-4724\n▁କହିଲ\t-4725\nପ୍ରସାଦ\t-4726\nଷ୍ଟ୍ରି\t-4727\n▁ଫେରିବା\t-4728\n▁ପର୍ଯ୍ୟଟନ\t-4729\n▁ଶୁଣିବାକୁ\t-4730\nଏଫ୍\t-4731\nଡିଂ\t-4732\nସମା\t-4733\nିକ୍ସ\t-4734\n▁ଯଦିଓ\t-4735\n▁ନିବେଦନ\t-4736\n▁ସମୁଦାୟ\t-4737\n▁ଆଞ୍ଚଳିକ\t-4738\n▁ସ୍ୱାଧୀନ\t-4739\n୫୦\t-4740\nter\t-4741\nଂଶୁ\t-4742\n▁ସଦର\t-4743\nବିଶେଷ\t-4744\n▁ଟିଭି\t-4745\n▁ବିଲ୍\t-4746\n▁ଭୋଟ୍\t-4747\nବ୍ରହ୍ମ\t-4748\n▁ଆଖିରେ\t-4749\n▁ଅର୍ଥାତ\t-4750\n▁ଅସମ୍ଭବ\t-4751\n▁ଏକମାତ୍ର\t-4752\n▁ଚମତ୍କାର\t-4753\n▁ଦିଆଯାଇଥିବା\t-4754\nଭୋଗ\t-4755\nସେବା\t-4756\n▁ଆଲେ\t-4757\n▁ଧୋଇ\t-4758\n▁ମାଗ\t-4759\n▁ଅନ୍ନ\t-4760\n▁ଚାଉଳ\t-4761\n▁ପଡ଼େ\t-4762\n▁ଫେରୁ\t-4763\n▁ହେଲ୍\t-4764\nଥିବାବେଳେ\t-4765\n▁ପୁଣିଥରେ\t-4766\n▁ଶତାବ୍ଦୀ\t-4767\n▁ମନ୍ଦିରରେ\t-4768\n▁•\t-4769\nକଲେ\t-4770\nକେଶ\t-4771\nଣାର\t-4772\nଲାରେ\t-4773\n▁କେତ\t-4774\nମିତ୍ତ\t-4775\n▁ଦେବାର\t-4776\n▁ବୟସ୍କ\t-4777\n▁କୁଆଡ଼େ\t-4778\n▁ସଂଶୋଧନ\t-4779\nପାଣି\t-4780\n▁ଚାପ\t-4781\n▁୪ଟି\t-4782\n▁ମଧୁର\t-4783\nନ୍ଦ୍ରା\t-4784\n▁ଅନ୍ତି\t-4785\n▁ନିମ୍ନ\t-4786\n▁ଭୂମିକ\t-4787\n▁ମଣ୍ଡଳ\t-4788\n▁ଅଧୀନରେ\t-4789\n▁ଟ୍ରେନ୍\t-4790\n▁ପାହାଡ଼\t-4791\n▁ବ୍ୟାପି\t-4792\n▁ବିଭାଗୀୟ\t-4793\n▁ଦୁଷ୍କର୍ମ\t-4794\n▁ପିଲାମାନେ\t-4795\n▁ପ୍ରାପ୍ତି\t-4796\n▁ପାଣିଗ୍ରାହୀ\t-4797\nଂଜନ\t-4798\nବରଣ\t-4799\nଳାର\t-4800\n▁୩୮\t-4801\nିକାଲ\t-4802\n▁ଶୋଷ\t-4803\n▁ହକି\t-4804\nଲାଇନ୍\t-4805\n▁ପାଠପ\t-4806\n▁କର୍ତ୍ତବ୍ୟ\t-4807\nକୂ\t-4808\nଘରେ\t-4809\nତିଶ\t-4810\nବରଦ\t-4811\nରେଶ\t-4812\nହେର\t-4813\nଣ୍ଟେ\t-4814\n▁କଠୋର\t-4815\n▁କପୁର\t-4816\n▁ଗଣ୍ଡ\t-4817\n▁ଛତ୍ର\t-4818\n▁ମନ୍ଦ\t-4819\n▁ସହଯୋଗୀ\t-4820\n▁ଅନ୍ତର୍ଜାତୀୟ\t-4821\n▁ଠେ\t-4822\n▁ହାଉ\t-4823\nଜ୍ଞତା\t-4824\nଞ୍ଜଳି\t-4825\n▁ନାଇଁ\t-4826\n▁ଯିବେ\t-4827\nବୃଦ୍ଧି\t-4828\n▁ଆଶ୍ରୟ\t-4829\n▁କାମନା\t-4830\n▁ପ୍ରଣା\t-4831\n▁ମତଦାନ\t-4832\n▁ଜାଗାରେ\t-4833\n▁ଆନ\t-4834\n▁ନଂ\t-4835\nହେତୁ\t-4836\nପାଟଣା\t-4837\n▁ଅଥବା\t-4838\n▁ମାନସ\t-4839\n▁କ୍ୟାରି\t-4840\n▁ନିକଟସ୍ଥ\t-4841\n▁ପାଇଥିଲା\t-4842\n▁ସ୍ବୀକାର\t-4843\n▁ନିଆଯାଇଛି\t-4844\nଳାସ\t-4845\nଛଡ଼ା\t-4846\nବାନୁ\t-4847\nନିଷ୍ଠ\t-4848\nାମୂଳକ\t-4849\n▁ନୋଟି\t-4850\n▁ବାଳକ\t-4851\n▁ସର୍ଭେ\t-4852\n▁ମିଳିବା\t-4853\n▁ବିଳମ୍ବିତ\t-4854\n▁ଢେଙ୍କାନାଳ\t-4855\nଗୃହ\t-4856\nଦାମ\t-4857\n▁ହଇ\t-4858\nଭଦ୍ର\t-4859\nମାନା\t-4860\nିତ୍ବ\t-4861\n▁ବୋଝ\t-4862\n▁ଶାରୀ\t-4863\n▁ଆଣ୍ଟି\t-4864\nବାସୀଙ୍କୁ\t-4865\n▁ସ୍ମାର୍ଟ\t-4866\n▁ବିପକ୍ଷରେ\t-4867\n▁ସମାଲୋଚନା\t-4868\n▁ସମ୍ମିଳନୀରେ\t-4869\nଢେ\t-4870\nବିକ\t-4871\n▁ଗଲ\t-4872\n▁ଆଲୋ\t-4873\n▁ଘୋର\t-4874\n▁ବାନ\t-4875\n▁ମାଲ\t-4876\nବସ୍ତୁ\t-4877\n▁ଅପଡେ\t-4878\n▁ଅମ୍ଳ\t-4879\nାଇବାରେ\t-4880\n▁ଇଣ୍ଡି\t-4881\n▁ସପ୍ତାହରେ\t-4882\n▁ଭୁବନେଶ୍ୱରରେ\t-4883\nଆସୁ\t-4884\nର୍ଟ\t-4885\nେଇଁ\t-4886\n▁୩୯\t-4887\nଜ୍ଜା\t-4888\nନାହଁ\t-4889\n▁ଅନି\t-4890\n▁ଅସି\t-4891\n▁ଅସ୍ତ\t-4892\n▁ଆମ୍ବ\t-4893\n▁ମନୋଜ\t-4894\n▁ମାସର\t-4895\nୁଛନ୍ତି\t-4896\n▁ଜଷ୍ଟି\t-4897\n▁ପଞ୍ଚମ\t-4898\n▁ଭୂତାଣୁ\t-4899\n▁ମିଥ୍ୟା\t-4900\n▁ସତ୍ତ୍ୱ\t-4901\n▁ସେଥିରୁ\t-4902\nଗୁଡ଼ିକରେ\t-4903\n▁ଶାରୀରିକ\t-4904\n▁ବିସ୍ଫୋରଣ\t-4905\nବୈ\t-4906\nଧାସ\t-4907\nାୟା\t-4908\n▁ମଠ\t-4909\n▁ଉଦୟ\t-4910\n▁ଚଢା\t-4911\n▁ପାଟ\t-4912\n▁ମହେ\t-4913\nଶୀଘ୍ର\t-4914\n▁ନାହି\t-4915\n▁ଯେତିକି\t-4916\n▁ସ୍ୱାଗତ\t-4917\n▁ଯେକୌଣସି\t-4918\n▁ପରିବାରରେ\t-4919\n▁ବଲାଙ୍ଗୀର\t-4920\n▁ଅଧ୍ୟକ୍ଷତାରେ\t-4921\n▁କର୍ମଚାରୀଙ୍କୁ\t-4922\nହଲ\t-4923\nଠନର\t-4924\nାୟନ\t-4925\n▁ତୈ\t-4926\n▁ସୃ\t-4927\nଫର୍ମ\t-4928\nଲେଣି\t-4929\nସୂଦନ\t-4930\n▁ପୋଲ\t-4931\nଦ୍ରୋହ\t-4932\n▁ଦରମା\t-4933\n▁ବୁଲୁ\t-4934\n▁ବଲିଉଡ\t-4935\n▁ସାହେବ\t-4936\n▁ଅନୁଗୁଳ\t-4937\nri\t-4938\n▁W\t-4939\n▁୪୨\t-4940\n▁୪୪\t-4941\n▁୯୦\t-4942\nଓ୍ବା\t-4943\n▁ଋଷି\t-4944\n▁ମତ୍\t-4945\n▁ମାଡ\t-4946\n▁ମାଳ\t-4947\n▁ରାଜି\t-4948\n▁ଆନ୍ତର୍\t-4949\n▁ବିପର୍ଯ୍ୟ\t-4950\n▁ରାଷ୍ଟ୍ରୀୟ\t-4951\n▁ସଂକ୍ରମିତଙ୍କ\t-4952\nହୂ\t-4953\n▁*\t-4954\nହାୟ\t-4955\nାମେ\t-4956\n▁ସୋନ\t-4957\n▁ଜେନେ\t-4958\n▁ପାଇଲ\t-4959\n▁ଭୁଲ୍\t-4960\n▁ଆମ୍ବୁ\t-4961\n▁ଦାନ୍ତ\t-4962\n▁ପ୍ରଦୂ\t-4963\n▁ମହାକାଶ\t-4964\n▁ସ୍ପର୍ଶ\t-4965\n▁ଟିକାକରଣ\t-4966\n▁ତାରିଖରୁ\t-4967\n▁କରିପାରିବ\t-4968\n▁ଡାକ୍ତରଖାନା\t-4969\n▁କଟ\t-4970\nଡାଉନ\t-4971\nର୍ଗତ\t-4972\n▁ମରୁ\t-4973\n▁ଲାଲ\t-4974\n▁୧୯୪\t-4975\n▁ଶ୍ରମ\t-4976\n▁ଚଢ଼ାଉ\t-4977\n▁କାନ୍ଦି\t-4978\n▁ପରମାଣୁ\t-4979\n▁ଖ୍ରୀଷ୍ଟ\t-4980\n▁ନିର୍ବାଚିତ\t-4981\n▁ମହିଳାମାନେ\t-4982\nଷୁ\t-4983\nହୋ\t-4984\nଘାଟ\t-4985\nଦିଏ\t-4986\nବର୍ଗ\t-4987\n▁ଧରା\t-4988\n▁ଶତା\t-4989\n▁ଅରୁଣ\t-4990\n▁ଏସପି\t-4991\n▁ରେଖା\t-4992\n▁୨୦୧୭\t-4993\n▁ପରିବେ\t-4994\n▁ହାତକୁ\t-4995\nଦ୍ରାବାଦ\t-4996\n▁ଉର୍ଦ୍ଧ\t-4997\n▁ପ୍ରଭାବରେ\t-4998\n▁ମଧ୍ୟପ୍ରଦେଶ\t-4999\nac\t-5000\n▁୪୮\t-5001\nବୋଧନ\t-5002\nୋର୍ସ\t-5003\n▁ଇଡି\t-5004\n▁ଓ୍ବ\t-5005\n▁କର୍କ\t-5006\n▁ପ୍ରତ\t-5007\n▁ଲାଗେ\t-5008\n▁ସାହସ\t-5009\n▁ହାରି\t-5010\n▁ବୈଷ୍ଣବ\t-5011\n▁ଚାଷୀଙ୍କ\t-5012\n▁ମୃତକଙ୍କ\t-5013\n▁ତାଲିକାରେ\t-5014\n▁ପ୍ରତ୍ୟକ୍ଷ\t-5015\n▁ବିଦ୍ୟାଳୟର\t-5016\nପୀଠ\t-5017\nମ୍ୱ\t-5018\nସ୍ଟ\t-5019\n▁ତପ\t-5020\n▁୫୫\t-5021\nଚାଲି\t-5022\nପାଠୀ\t-5023\nବ୍ରେ\t-5024\nେଇବା\t-5025\n▁ଭରତ\t-5026\n▁ସକଳ\t-5027\n▁ସତ୍ତ\t-5028\n▁ପାନୀୟ\t-5029\n▁ଯୋଡ଼ି\t-5030\n▁ରହିଲା\t-5031\n▁ଶରୀରର\t-5032\n▁ବାହାଘର\t-5033\n▁ଅବଲମ୍ବନ\t-5034\n▁ଭିଡିଓରେ\t-5035\n▁ପରିମାଣରେ\t-5036\news\t-5037\nକଟକ\t-5038\n୍ୱା\t-5039\n▁ଅଚ\t-5040\n▁ହଟ\t-5041\nକୋଟି\t-5042\nନ୍ଧା\t-5043\n▁ମିଛ\t-5044\nହନ୍ତୁ\t-5045\n▁ଡିଜେ\t-5046\n▁ବଳରେ\t-5047\n▁ଶ୍ରୀଯୁକ୍ତ\t-5048\n▁କେନ୍ଦ୍ରମନ୍ତ୍ରୀ\t-5049\nଫ୍ଟ\t-5050\nବ୍ଲ\t-5051\nିକର\t-5052\n▁ଓଭ\t-5053\nକିଛି\t-5054\nଟାକୁ\t-5055\nପ୍ଟର\t-5056\n▁ଭଜନ\t-5057\n▁ମଜା\t-5058\n▁ଷଡ଼\t-5059\n▁ଓକିଲ\t-5060\n▁ସଂସଦ\t-5061\n▁ତୁମକୁ\t-5062\n▁ମାର୍କ\t-5063\n▁ଫିଲ୍ମର\t-5064\n▁ଏଥିସହିତ\t-5065\n▁କରିପାରୁ\t-5066\n▁ନେତାଙ୍କ\t-5067\n▁ପ୍ରମାଣିତ\t-5068\n▁ଚାହୁଁଛନ୍ତି\t-5069\nଧନ\t-5070\nଫାଇ\t-5071\nଲାମ\t-5072\n▁ତଳ\t-5073\nକଳ୍ପ\t-5074\nଦାୟକ\t-5075\nନ୍ଧୁ\t-5076\nସିପି\t-5077\n▁ଇଲେ\t-5078\n▁ଉପୁ\t-5079\n▁ନାଥ\t-5080\n▁ଶାହ\t-5081\nକର୍ମୀ\t-5082\n▁ପାଳିତ\t-5083\n▁ସିଧାସ\t-5084\n▁ଦେଇଥାଏ\t-5085\n▁କିଛିଦିନ\t-5086\n▁ବାସିନ୍ଦା\t-5087\n00\t-5088\n▁ଇର\t-5089\nବନ୍ତ\t-5090\n▁ଅଜୟ\t-5091\n▁ଗ୍ଲ\t-5092\n▁ନିତ\t-5093\n▁ଲୁଚ\t-5094\n▁ଫାର୍\t-5095\n▁ଯୁବତ\t-5096\n▁ହ୍ୟା\t-5097\n▁ଅର୍ଜୁନ\t-5098\n▁ଓହ୍ଲାଇ\t-5099\n▁ମହାପ୍ରଭୁଙ୍କ\t-5100\n▁y\t-5101\nତେବେ\t-5102\nନ୍ୟୁ\t-5103\nସଙ୍ଗ\t-5104\n▁ଉଠା\t-5105\n▁ଭାଜ\t-5106\nଲାଦେଶ\t-5107\n▁ଅଭ୍ୟ\t-5108\n▁ଉପରୁ\t-5109\n▁ଜାଣେ\t-5110\n▁ନିଶା\t-5111\n▁ମାନି\t-5112\n▁କହୁଛି\t-5113\n▁ଶବ୍ଦର\t-5114\n▁କରିଯାଇ\t-5115\n▁ଗସ୍ତରେ\t-5116\n▁ସ୍ୱାଇଁ\t-5117\n▁ମଧୁସୂଦନ\t-5118\n▁ପ୍ରାରମ୍ଭ\t-5119\n୧୭\t-5120\n▁ବଣ\t-5121\nଗ୍ରେ\t-5122\nମ୍ପର\t-5123\nସ୍ରେ\t-5124\n▁ସାପ\t-5125\n▁ରାଜୁ\t-5126\n▁ସିଧା\t-5127\nଦେବଙ୍କ\t-5128\n▁କାନ୍ଧ\t-5129\n▁ପାୱାର\t-5130\n▁ବାନ୍ଧ\t-5131\n▁ଯାଉଛୁ\t-5132\n▁ଅନ୍ତିମ\t-5133\n▁ରଖିଥିବା\t-5134\n▁ଲୋକଙ୍କର\t-5135\n▁ସିଧାସଳଖ\t-5136\nଖୀ\t-5137\n▁K\t-5138\nଣିତ\t-5139\nପନା\t-5140\nବିତ\t-5141\n▁ନୌ\t-5142\n▁ବାଚ\t-5143\n▁ସବୁଜ\t-5144\n▁ସମନ୍\t-5145\n▁ଚନ୍ଦନ\t-5146\n▁ୱାର୍ଡ\t-5147\n▁ସ୍କୁଲ୍\t-5148\nନିର୍ବାଚନ\t-5149\n▁ପଞ୍ଚାୟତର\t-5150\nଅନ\t-5151\nଲତା\t-5152\n▁ପଡ\t-5153\n▁ଅଥଚ\t-5154\n▁ଚୌଧ\t-5155\n▁ଫଳା\t-5156\n▁ବାଂ\t-5157\n▁ଅମିତ\t-5158\n▁ଜବାବ\t-5159\n▁ଜାହା\t-5160\n▁ମ୍ୟୁ\t-5161\n▁ସିଲି\t-5162\n▁ନାଟ୍ୟ\t-5163\n▁ଅଂଚଳରେ\t-5164\n▁ଅଧ୍ୟୟନ\t-5165\n▁ନିକଟକୁ\t-5166\n▁ସେଣ୍ଟର\t-5167\n▁ସ୍ଥାୟୀ\t-5168\n▁ଚେନ୍ନାଇ\t-5169\n▁କରାଯାଉଥିବା\t-5170\n▁ହୋଇପାରନ୍ତି\t-5171\nୂହ\t-5172\n▁୪୩\t-5173\nଦେବୀ\t-5174\nମୂଳକ\t-5175\nରୂପେ\t-5176\n▁ଆରା\t-5177\n▁ପୌର\t-5178\n▁ବ୍ରଜ\t-5179\n▁ବ୍ରତ\t-5180\n▁ଉପହାର\t-5181\n▁ଗାଁକୁ\t-5182\n▁ଜମିରେ\t-5183\n▁ଫାଇଦା\t-5184\n▁ସମ୍ଭା\t-5185\n▁ବାହିନୀ\t-5186\n▁ଯାଇଥାଏ\t-5187\n▁ବିସ୍ତାର\t-5188\n▁ବ୍ୟାପାର\t-5189\nid\t-5190\nଂଟ\t-5191\nଏବ\t-5192\nଯୋ\t-5193\nସର୍\t-5194\nାତୁ\t-5195\nଦଣ୍ଡ\t-5196\n▁କଡା\t-5197\n▁ହନୁ\t-5198\nବିଧାନ\t-5199\n▁ଜଡିତ\t-5200\n▁ଜବରଦ\t-5201\n▁ମାରୁ\t-5202\n▁ମୁସଲ\t-5203\n▁ପୋଷାକ\t-5204\n▁ଶେୟାର\t-5205\n▁ପୂର୍ବେ\t-5206\n▁ବିଶ୍ୱରେ\t-5207\n▁ହୋଇସାରି\t-5208\n▁ଦିଆଯାଉଛି\t-5209\n▁ପଚରାଉଚରା\t-5210\nକିଆ\t-5211\nକ୍ଟି\t-5212\nବହାର\t-5213\n▁ଦୃଢ\t-5214\n▁ବାଗ\t-5215\n▁ବେଲ\t-5216\n▁ଆସନରେ\t-5217\n▁କରିବି\t-5218\nଜଗନ୍ନାଥ\t-5219\n▁ଏହିଭଳି\t-5220\n▁ଯୁକ୍ତି\t-5221\n▁ନୁହନ୍ତି\t-5222\n▁ସଂରକ୍ଷଣ\t-5223\n▁ମହେନ୍ଦ୍ର\t-5224\nad\t-5225\nଧୀ\t-5226\nଡାଇ\t-5227\n▁କରଣ\t-5228\n▁ପୀଠ\t-5229\nଶିଳ୍ପ\t-5230\n▁ଅଜଣା\t-5231\n▁ଲଣ୍ଡ\t-5232\n▁ଅପମାନ\t-5233\n▁ପିଇବା\t-5234\nସ୍କୃତିକ\t-5235\n▁ବିଜୁଳି\t-5236\n▁କରିଦେଲେ\t-5237\n▁ପ୍ରେରଣା\t-5238\n▁ପଡ଼ିଥିଲା\t-5239\n▁ନବରଙ୍ଗପୁର\t-5240\nରିଂ\t-5241\n▁ନଈ\t-5242\nବାନୀ\t-5243\n୍ୱାନ\t-5244\n▁ଆଚରଣ\t-5245\n▁ଗୁଜୁ\t-5246\n▁ବାରି\t-5247\n▁ଯତ୍ନ\t-5248\n▁ରତ୍ନ\t-5249\n▁ସେକେ\t-5250\n▁କ୍ୟାବ\t-5251\n▁ଆସିଥାଏ\t-5252\n▁କ୍ରାଇମ\t-5253\n▁ମତ୍ସ୍ୟ\t-5254\n▁ଓଡ଼ିଆରେ\t-5255\n▁ଅନୁଧ୍ୟାନ\t-5256\n▁ଆଚାର୍ଯ୍ୟ\t-5257\n▁ପ୍ରତିବର୍ଷ\t-5258\nଢୀ\t-5259\n▁ଲଢ\t-5260\n▁କୁଳ\t-5261\n▁ନାଶ\t-5262\n▁ମାଧ\t-5263\n▁ଟାଣି\t-5264\n▁ବାହୁ\t-5265\n▁ଲୁହା\t-5266\n▁ଅବଦାନ\t-5267\n▁ନିବେଶ\t-5268\n▁ପକ୍ଷୀ\t-5269\n▁ପହଂଚି\t-5270\n▁ଶ୍ରୀଲ\t-5271\n▁ଯେଉଁଠାରେ\t-5272\nଚ୍ଛି\t-5273\n▁ଛତିଶ\t-5274\n▁ରମେଶ\t-5275\n▁ଉପକୂଳ\t-5276\n▁ଦ୍ୱାର\t-5277\n▁ପ୍ରଭୃ\t-5278\n▁ବଜେଟ୍\t-5279\n▁ରଙ୍ଗର\t-5280\n▁ଶ୍ୟାମ\t-5281\n▁ପଡ଼ିଲା\t-5282\n▁ରଘୁନାଥ\t-5283\n▁ଅବସ୍ଥିତ\t-5284\n▁ସାଙ୍ଗକୁ\t-5285\n▁ବ୍ୟବସାୟରେ\t-5286\n▁ସୁରେନ୍ଦ୍ର\t-5287\n▁ସ୍ଥାନାନ୍ତର\t-5288\nus\t-5289\n▁ରୂ\t-5290\n▁ଆଲୁ\t-5291\n▁ବିପ\t-5292\nଆନ୍ତୁ\t-5293\nଶ୍ଚିତ\t-5294\n▁ନ୍ୟା\t-5295\n▁ପଳାଇ\t-5296\n▁ମିଶନ\t-5297\n▁ସଞ୍ଚ\t-5298\n▁ହାଜର\t-5299\n▁ପାର୍ଥ\t-5300\n▁କ୍ୟାମେ\t-5301\n▁ରାଉରକେ\t-5302\n▁କାର୍ତ୍ତ\t-5303\n▁କ୍ୟାମ୍ପ\t-5304\n▁ଯାଉଥିଲେ\t-5305\n▁ସତ୍ତ୍ୱେ\t-5306\n▁ହିସାବରେ\t-5307\n▁ଶକ୍ତିଶାଳୀ\t-5308\n▁ସମ୍ପର୍କୀୟ\t-5309\n▁ସାଂସ୍କୃତିକ\t-5310\n▁ଅନ୍ତର୍ଭୁକ୍ତ\t-5311\n▁ହୋଇଥିବାବେଳେ\t-5312\n୧୪\t-5313\n୯୯\t-5314\nଧ୍ବ\t-5315\nପ୍ଳ\t-5316\nଲାଲ\t-5317\nଳତା\t-5318\n▁ସଫ\t-5319\nମୁନା\t-5320\n▁ଏଥୁ\t-5321\n▁ଭୋର\t-5322\n▁ଗଢ଼ି\t-5323\n▁ଆଦିତ୍ୟ\t-5324\n▁ତାମିଲନା\t-5325\n▁ଯୁଦ୍ଧରେ\t-5326\n▁ସୂତ୍ରରୁ\t-5327\n▁ଅଧିକାରୀମାନେ\t-5328\nec\t-5329\nଜଳ\t-5330\n▁ବଜାୟ\t-5331\n▁ଯୁଗର\t-5332\n▁ଯୋଡି\t-5333\n▁କେତୋଟି\t-5334\n▁ତାହେଲେ\t-5335\n▁ପ୍ରଦର୍ଶ\t-5336\n▁ଉପେନ୍ଦ୍ର\t-5337\nଶୂ\t-5338\nଖାଇ\t-5339\nଖିଆ\t-5340\n▁||\t-5341\n▁କୋଠ\t-5342\n▁ମୋହର\t-5343\n▁ଚାଲିବ\t-5344\n▁ଟିକେଟ\t-5345\n▁ସଂଯୋଜ\t-5346\n▁ସ୍ବର୍ଗ\t-5347\n▁କରିସାରି\t-5348\n▁ସାମ୍ନାରେ\t-5349\n▁ମୁହୂର୍ତ୍ତ\t-5350\n୧୩\t-5351\nଡ଼ୁ\t-5352\nମାଲ\t-5353\n▁Od\t-5354\n▁ପତ\t-5355\nନ୍ନା\t-5356\nମୁଖେ\t-5357\nକମାନେ\t-5358\nଖ୍ୟାତ\t-5359\n▁ସୁଧା\t-5360\n▁ୱାର୍\t-5361\n▁ସାବ୍ୟ\t-5362\n▁ହେଲାଣି\t-5363\n▁ନଥିବାରୁ\t-5364\n▁ପ୍ରଦେଶର\t-5365\n▁ପ୍ରଶ୍ନର\t-5366\n▁ମନ୍ଦିରର\t-5367\n▁ମୋଦିଙ୍କ\t-5368\n▁ରହୁଥିଲେ\t-5369\n▁ପିଲାଙ୍କୁ\t-5370\nRA\t-5371\nଆଜ\t-5372\nକୀର\t-5373\nଭରେ\t-5374\n▁ଡଃ\t-5375\n▁ହଜ\t-5376\nଟ୍ରୋ\t-5377\nଶକ୍ତ\t-5378\nସ୍ବା\t-5379\n▁କଲି\t-5380\n୍ୟାଲେ\t-5381\n▁ଉଦ୍ଧ\t-5382\n▁ପ୍ରାଇ\t-5383\n▁ମୌଳିକ\t-5384\n▁ଷ୍ଟାର\t-5385\n▁ପଡ଼ିଲେ\t-5386\n▁ସଙ୍ଗରେ\t-5387\n▁ଶ୍ରେଣୀର\t-5388\n▁ଜଣାଇଥିଲେ\t-5389\n▁ନାହାଁନ୍ତି\t-5390\nଆଜି\t-5391\nକତା\t-5392\nନରୁ\t-5393\nରାଗ\t-5394\nରୁଣ\t-5395\nିଲ୍\t-5396\n▁202\t-5397\n▁ବାହ\t-5398\nଯିବାର\t-5399\n▁ଇତିହା\t-5400\n▁ଚେତାବ\t-5401\n▁ଝିଅକୁ\t-5402\n▁ବିବେକ\t-5403\n▁ଶିଷ୍ୟ\t-5404\n▁ଜୋରଦାର\t-5405\n▁ମୁଦ୍ରା\t-5406\n▁ସ୍ୱପ୍ନ\t-5407\n▁ହୋଇଅଛି\t-5408\n▁କରୋନାରେ\t-5409\n▁ଐତିହାସିକ\t-5410\n▁ଡାକ୍ତରଙ୍କ\t-5411\n▁ପ୍ରଶାସନିକ\t-5412\n▁ହାଇଦ୍ରାବାଦ\t-5413\n▁ପଟ୍ଟନାୟକଙ୍କ\t-5414\n▁g\t-5415\nୀକା\t-5416\n▁ଅଦ\t-5417\n▁୪୬\t-5418\n▁ଆଫଗ\t-5419\n▁ସାଉ\t-5420\nପୋର୍ଟ\t-5421\n▁ମାତୃ\t-5422\n▁ଅବତାର\t-5423\n▁ଖଣ୍ଡି\t-5424\n▁ପ୍ରତୀକ\t-5425\n▁ହିନ୍ଦୀ\t-5426\n▁ଅଗ୍ନିଶମ\t-5427\n▁ଅତିକ୍ରମ\t-5428\n▁କେନ୍ଦ୍ରର\t-5429\n▁କରିଅଛନ୍ତି\t-5430\nୋଦ\t-5431\nigh\t-5432\n▁ସକ\t-5433\nଜ୍ୟର\t-5434\n▁ରେଡ୍\t-5435\nଥାନ୍ତେ\t-5436\nମଧ୍ୟରେ\t-5437\n▁ଅନୁମୋ\t-5438\n▁ବଣ୍ଟନ\t-5439\n▁ସାବ୍ୟସ୍ତ\t-5440\n▁ପାକିସ୍ତାନର\t-5441\n▁ଅଧିକାରୀଙ୍କୁ\t-5442\nay\t-5443\nଆଉ\t-5444\nୋଣ\t-5445\nଲାଭ\t-5446\n▁ଆଳ\t-5447\n▁ଫର\t-5448\n▁ନିଖ\t-5449\n▁ନୀର\t-5450\nଠାକାର\t-5451\n▁ଗତିରେ\t-5452\n▁ଫଳାଫଳ\t-5453\n▁ଯୁଗରେ\t-5454\n▁ଅର୍ଦ୍ଧ\t-5455\n▁ପରିମାଣର\t-5456\n▁ବିଶ୍ୱାଳ\t-5457\n▁ଉତ୍ତେଜନା\t-5458\n▁ସମସ୍ତଙ୍କର\t-5459\n▁ବିଶ୍ବବିଦ୍ୟାଳୟ\t-5460\nଗଛ\t-5461\nଶଃ\t-5462\nଡ଼େଇ\t-5463\nଲ୍ଲଭ\t-5464\n▁କନକ\t-5465\n▁ମେଘ\t-5466\n▁ରାଓ\t-5467\nଚ୍ୟୁତ\t-5468\n▁କୃପା\t-5469\n▁ବୁଝା\t-5470\n▁ଶତପଥ\t-5471\nଥିପାଇଁ\t-5472\n▁ଚନ୍ତା\t-5473\n▁ବେଆଇନ\t-5474\n▁ସେସବୁ\t-5475\n▁ସ୍ବୟଂ\t-5476\n▁କରାଗଲା\t-5477\n▁ଜଣାଯାଏ\t-5478\n▁ଯଥାର୍ଥ\t-5479\n▁ବାପାଙ୍କ\t-5480\n▁ବାବୁଙ୍କ\t-5481\n▁ସମ୍ବନ୍ଧୀୟ\t-5482\nଛେ\t-5483\n▁on\t-5484\n▁ତନ\t-5485\n▁ଶୌ\t-5486\nବାୟୁ\t-5487\nିକରଣ\t-5488\n▁ଡ଼ି\t-5489\n▁ବାଦ୍\t-5490\n▁ସମ୍ଭ\t-5491\n▁ହିମା\t-5492\n▁ବୁଢ଼ା\t-5493\n▁ସହସ୍ର\t-5494\n▁ଊର୍ଦ୍ଧ\t-5495\n▁ସଂଖ୍ୟକ\t-5496\n▁ସୁପ୍ରି\t-5497\n▁ଆସିବାକୁ\t-5498\n▁ଜାଣନ୍ତି\t-5499\n▁ଜାଣନ୍ତୁ\t-5500\n▁ଦିଲ୍ଲୀର\t-5501\n▁ଦେଉଥିବା\t-5502\n▁ନିର୍ଦେଶ\t-5503\n▁ପାର୍ଶ୍ବ\t-5504\n▁ପ୍ରତିଭା\t-5505\n▁କ୍ୟାବିନେ\t-5506\n▁ତହସିଲଦାର\t-5507\n▁ବାସ୍ତବରେ\t-5508\nୌପ\t-5509\nଆଡେ\t-5510\nଗଡା\t-5511\n▁ବଧ\t-5512\n▁ଲହ\t-5513\nମ୍ପା\t-5514\nାତ୍ୟ\t-5515\n▁କୋର\t-5516\n▁ଥିବ\t-5517\n▁ପଦା\t-5518\n▁ଭିଟ\t-5519\n▁ମୁତ\t-5520\n▁୧୯୬\t-5521\nଖ୍ୟାନ\t-5522\n▁ଟିକସ\t-5523\n▁ଷ୍ଟି\t-5524\n▁ଲାଗିବ\t-5525\n▁ଗତବର୍ଷ\t-5526\n▁ଭିଡ଼ିଓ\t-5527\n▁ଏକ୍ସପ୍ରେ\t-5528\n▁ଗୋପବନ୍ଧୁ\t-5529\n▁ଶ୍ରଦ୍ଧାଳୁ\t-5530\nge\t-5531\nly\t-5532\nist\t-5533\nଅନୁ\t-5534\nବୁଲ\t-5535\n▁is\t-5536\n▁୪୧\t-5537\nଲାଇନ\t-5538\n▁କାଶ\t-5539\n▁ଚୋର\t-5540\n▁ନେବ\t-5541\n▁ଭୋଇ\t-5542\n▁ଶରତ\t-5543\n▁୧୯୫\t-5544\n▁୫୦୦\t-5545\n▁ଠିକା\t-5546\n▁ଧାଇଁ\t-5547\n▁ରିୟା\t-5548\n▁କାନ୍ଦ\t-5549\n▁ପାଠ୍ୟ\t-5550\nକେନ୍ଦ୍ର\t-5551\nଯାଉଥିଲା\t-5552\n▁ସାମ୍ନା\t-5553\n▁ଧାର୍ଯ୍ୟ\t-5554\n▁ପାଇବାକୁ\t-5555\n▁ହୋଇଯାଇଥିଲେ\t-5556\n▁ପ୍ରତିଯୋଗିତା\t-5557\nଷୀ\t-5558\nଦକୁ\t-5559\nନୀତ\t-5560\n▁ଏଫ\t-5561\n▁ଜଲ\t-5562\nକୃତି\t-5563\nଧାରୀ\t-5564\nବାଦର\t-5565\nମାର୍\t-5566\n▁ଆଖୁ\t-5567\n▁ଆଶୀ\t-5568\n▁ଛଡା\t-5569\n▁ତେଜ\t-5570\n▁ବଚନ\t-5571\n▁ଯାତ\t-5572\n▁ପଛକୁ\t-5573\n▁ବାଣୀ\t-5574\n▁ଲୁଗା\t-5575\nବିଦ୍ୟା\t-5576\nମୁଣ୍ଡି\t-5577\n▁ଓଭରରେ\t-5578\n▁ଦର୍ଶା\t-5579\n▁ପାଖରୁ\t-5580\n▁ଫେରାଇ\t-5581\n▁ମହିମା\t-5582\n▁ସୁନ୍ଦ\t-5583\n▁ଓଡିଶାର\t-5584\n▁ଦକ୍ଷତା\t-5585\nମୂର୍ତ୍ତି\t-5586\n▁ଚେତାବନୀ\t-5587\n▁ଟାର୍ଗେଟ\t-5588\n▁ପାଇଥିବା\t-5589\n▁ଏହାଦ୍ୱାରା\t-5590\n▁ଚିକିତ୍ସିତ\t-5591\n▁ରାଜନୀତିରେ\t-5592\n▁ଦୁର୍ଘଟଣାରେ\t-5593\nIN\t-5594\nଂକ\t-5595\nଝି\t-5596\n୨୫\t-5597\nଶିତ\t-5598\nସିର\t-5599\nଟରରେ\t-5600\nଣିକି\t-5601\n▁ନାଚ\t-5602\n▁ନିଦ\t-5603\n▁ମାଉ\t-5604\n▁ସେଥ\t-5605\nଦେଶିକ\t-5606\n▁କିମି\t-5607\n▁ରାଣୀ\t-5608\n▁ସେଠୀ\t-5609\n▁ହୀରା\t-5610\n▁ଗ୍ରେଗ\t-5611\n▁ପୁଣ୍ୟ\t-5612\n▁ପରାସ୍ତ\t-5613\n▁ବସ୍ତୁର\t-5614\n▁ଘଣ୍ଟାରେ\t-5615\n▁ନିଷ୍ପତି\t-5616\n▁ବାଚସ୍ପତି\t-5617\nୀବ\t-5618\nୟୋ\t-5619\nଜନିତ\t-5620\nଶ୍ରେ\t-5621\n▁ଭୀମ\t-5622\n▁ଗଠିତ\t-5623\n▁ନିରୀ\t-5624\n▁ବଜ୍ର\t-5625\n▁ଅନ୍ତର\t-5626\n▁ଫାଇନା\t-5627\n▁ବ୍ଲକ୍\t-5628\n▁ପାଣ୍ଠି\t-5629\n▁ପ୍ରତିରୋଧ\t-5630\nପୃ\t-5631\n▁6\t-5632\nକାମ\t-5633\nହେଉ\t-5634\nଡିପି\t-5635\nହାରୁ\t-5636\n▁ପତି\t-5637\n▁ପାଏ\t-5638\n▁ମଦନ\t-5639\nକ୍ଷରେ\t-5640\n▁ଏତଲା\t-5641\n▁ଲିଟର\t-5642\n▁ପୃଥ୍ବ\t-5643\n▁କରୋନାର\t-5644\n▁ନ୍ୟୁଜି\t-5645\n▁ପ୍ରଦୂଷଣ\t-5646\n▁ପ୍ରେମିକ\t-5647\n▁ଜଣାଯାଇଛି\t-5648\n▁ସିଲିଣ୍ଡର\t-5649\n▁ଅର୍ଥନୈତିକ\t-5650\n▁କର୍ଣ୍ଣାଟକ\t-5651\nଣ୍ତ\t-5652\nପୁଅ\t-5653\nଶିଷ\t-5654\n▁ର୍\t-5655\nଲୁରୁ\t-5656\n▁ଚଢ଼\t-5657\n▁ଚିର\t-5658\n▁ଦଶମ\t-5659\nଦୃଶ୍ୟ\t-5660\n▁ଅଗ୍ନ\t-5661\n▁ଶିଖି\t-5662\nବୃତ୍ତି\t-5663\n▁ଆର୍ଯ୍ୟ\t-5664\n▁ପ୍ରବାହ\t-5665\n▁ମହଙ୍ଗା\t-5666\n▁ଶୃଙ୍ଖଳା\t-5667\n▁ପ୍ରୋତ୍ସା\t-5668\n୨୮\t-5669\n▁J\t-5670\n▁Q\t-5671\nour\t-5672\nଘ୍ନ\t-5673\n▁ମଉ\t-5674\nଡାରେ\t-5675\n▁ଆହ୍\t-5676\n▁ବଦଳ\t-5677\n▁୬ଟି\t-5678\n▁ଅତୀତ\t-5679\n▁ଆୟୋଗ\t-5680\n▁କ୍ଷୟ\t-5681\n▁ସାନି\t-5682\nବ୍ୟାପୀ\t-5683\n▁ଅସହାୟ\t-5684\n▁ନିଖୋଜ\t-5685\n▁ଅର୍ଥନୀତି\t-5686\n▁ପାଇଛନ୍ତି\t-5687\n▁ନୂଆଦିଲ୍ଲୀ\t-5688\nସଂଘ\t-5689\nନ୍ଥା\t-5690\n▁ଆବି\t-5691\n▁କାଉ\t-5692\n▁ବନା\t-5693\n▁ବେଗ\t-5694\nଜ୍ୟରେ\t-5695\nଣ୍ଟରେ\t-5696\nମୁକ୍ତ\t-5697\nର୍ବାଦ\t-5698\n▁ଆଡକୁ\t-5699\n▁ଆପଣା\t-5700\n▁ଖାନ୍\t-5701\n▁ଗୀତା\t-5702\n▁ଦାର୍\t-5703\n▁ପାସ୍\t-5704\n▁ଫୁଟି\t-5705\n▁ଅଧ୍ବକ\t-5706\n▁ଗୃହରେ\t-5707\n▁ଜମ୍ମୁ\t-5708\n▁ନହେଲେ\t-5709\n▁ଲିଙ୍ଗ\t-5710\n▁ଦର୍ଶାଇ\t-5711\n▁ପଂଚାୟତ\t-5712\n▁ବିକଳ୍ପ\t-5713\n▁ପାଲଟିଛି\t-5714\n▁ଆମ୍ଭେମାନେ\t-5715\n▁ବିଧାନସଭାରେ\t-5716\nng\t-5717\nସାନ\t-5718\n▁ଅଉ\t-5719\n▁ଦଉ\t-5720\nଟ୍ଟା\t-5721\nଦାନୀ\t-5722\n▁ଦମ୍\t-5723\n▁ଦେଲ\t-5724\n▁ଦ୍ବି\t-5725\n▁ଧୋନୀ\t-5726\n▁ସଂଘର\t-5727\nସ୍ତ୍ରୀ\t-5728\n▁ଚଳାଚଳ\t-5729\n▁ମୃତ୍ୟ\t-5730\n▁ଏକାନ୍ତ\t-5731\n▁ତୁମ୍ଭେ\t-5732\n▁ଦେଉଥିଲେ\t-5733\n▁ଆତଙ୍କବାଦ\t-5734\n▁କ୍ରିକେଟ୍\t-5735\n▁ଲେଖିଥିଲେ\t-5736\nver\t-5737\nଥାଇ\t-5738\nବେଳ\t-5739\nମୂଳ\t-5740\nାସେ\t-5741\n▁PP\t-5742\nୋଗ୍ୟ\t-5743\n▁for\t-5744\n▁ବୁଲ\t-5745\n▁ଲୁଣ\t-5746\nଥିବୀର\t-5747\nାର୍ଜୀ\t-5748\n▁କୁକୁ\t-5749\n▁ନାଏକ\t-5750\n▁ଭୁମେ\t-5751\n▁ମୌସୁ\t-5752\n▁ରାଧା\t-5753\n▁ଲ୍ୟା\t-5754\n▁ସେବକ\t-5755\nମୁଣ୍ଡା\t-5756\n▁କର୍କଟ\t-5757\n▁ବୁଦ୍ଧ\t-5758\n▁ଅନୁଭୂତ\t-5759\n▁ଚାମ୍ପି\t-5760\n▁ପୋଲିସର\t-5761\n▁ସଂପାଦକ\t-5762\n▁ଜଣାଶୁଣା\t-5763\n▁ଥାଆନ୍ତି\t-5764\n▁ଉତ୍ସାହିତ\t-5765\n▁ତ୍ରିପାଠୀ\t-5766\n▁ସନ୍ତୁଷ୍ଟ\t-5767\n▁ଭିତ୍ତିଭୂମି\t-5768\n▁ଅର୍ଥମନ୍ତ୍ରୀ\t-5769\n୍ୱେ\t-5770\nରାଜୀ\t-5771\n▁ସରୁ\t-5772\nation\t-5773\n▁ଆସିଲ\t-5774\n▁ଭୋଜନ\t-5775\n▁ସେଥୁ\t-5776\n▁ଦୁର୍ଗ\t-5777\n▁ଅଫିସରେ\t-5778\n▁ମହାରଣା\t-5779\n▁ବାସୁଦେବ\t-5780\n▁ମୁସଲମାନ\t-5781\n▁ଯାଉଥିଲା\t-5782\n▁ଅଂଶଗ୍ରହଣ\t-5783\n▁ସମ୍ବର୍ଦ୍ଧ\t-5784\n▁ପ୍ରତ୍ୟାହାର\t-5785\nCH\t-5786\nଟ୍ସ\t-5787\nଳାପ\t-5788\n▁ଟୋ\t-5789\nମୁଖୀ\t-5790\n▁ଆରେ\t-5791\n▁କଟା\t-5792\n▁କରଇ\t-5793\n▁ବିଚ\t-5794\n▁ଅଶ୍ବ\t-5795\n▁ବିପୁ\t-5796\n▁ଶାଖା\t-5797\n▁ପଏଣ୍ଟ\t-5798\n▁ମୁତୟନ\t-5799\n▁ସେନେଇ\t-5800\n▁ଆହ୍ୱାନ\t-5801\n▁କମିଟିର\t-5802\n▁ଥାନ୍ତି\t-5803\n▁ବିବରଣୀ\t-5804\n▁ମାତ୍ରେ\t-5805\n▁ରୋକିବା\t-5806\n▁ହଜାରରୁ\t-5807\n▁ନିଅନ୍ତୁ\t-5808\n▁ବିଜ୍ଞପ୍ତି\t-5809\nନୌ\t-5810\nୀଣ\t-5811\nୟେ\t-5812\nଫୁଲ\t-5813\nଯାତ\t-5814\nଶାଲ\t-5815\nସନର\t-5816\n▁ଖର\t-5817\n▁ଭୃ\t-5818\nଚନାର\t-5819\n▁ଗୌର\t-5820\n▁ସେଲ\t-5821\n▁କୌଶଳ\t-5822\n▁ଖେଳୁ\t-5823\n▁ଗିରି\t-5824\n▁ଚଢାଉ\t-5825\n▁ଦାବୀ\t-5826\n▁ପୁଷ୍\t-5827\n▁ଲଞ୍ଚ\t-5828\nମିନିଟ୍\t-5829\n▁ଶୁଦ୍ଧ\t-5830\n▁ମିଳିଲା\t-5831\n▁ଚାଲିଥିଲା\t-5832\n▁ଯାହାଫଳରେ\t-5833\n▁ପରୀକ୍ଷାରେ\t-5834\n▁ପ୍ରତିଷ୍ଠିତ\t-5835\now\t-5836\nul\t-5837\n▁·\t-5838\nଯାକ\t-5839\n▁ମସ\t-5840\nଗଳ୍ପ\t-5841\n▁ପଲି\t-5842\n▁ବଗି\t-5843\n▁ମିନ\t-5844\nଦ୍ୱାର\t-5845\n୍ରାଳୟ\t-5846\n▁ଉଲ୍ଲ\t-5847\n▁ସଂସ୍\t-5848\n▁୨୦୨୩\t-5849\n▁ପୁଅକୁ\t-5850\n▁ବାରିକ\t-5851\n▁ସୀମିତ\t-5852\n▁ଉତ୍ତରା\t-5853\n▁ଉଭୟଙ୍କ\t-5854\n▁ଲଗାଇବା\t-5855\n▁ଅଭିମୁଖେ\t-5856\n▁ପ୍ରବାସୀ\t-5857\n▁ବିଜେଡିର\t-5858\n▁ସମ୍ପନ୍ନ\t-5859\n▁କମ୍ପାନୀର\t-5860\n▁କର୍ମଚାରୀମାନେ\t-5861\n??\t-5862\nut\t-5863\nଇଓ\t-5864\n▁/\t-5865\ness\t-5866\nପୂର\t-5867\nାମତ\t-5868\n▁SO\t-5869\n▁୫୬\t-5870\nଙ୍କନ\t-5871\n▁ଘେନ\t-5872\n▁ବାଈ\t-5873\nଫୁଲ୍ଲ\t-5874\nଲିଙ୍ଗ\t-5875\n▁ତ୍ରୁ\t-5876\n▁ସନ୍ନ\t-5877\n▁ସାଧନ\t-5878\n▁କଣ୍ଟା\t-5879\n▁ଭାବନା\t-5880\n▁ଭିତ୍ତ\t-5881\n▁ମଧ୍ୟମ\t-5882\n▁ରଖିବେ\t-5883\n▁ଶୁଣିବା\t-5884\n▁ବାଂଲାଦେଶ\t-5885\n▁ସର୍ବୋଚ୍ଚ\t-5886\n▁ଚର୍ଚ୍ଚାରେ\t-5887\n▁କ୍ରାଇମବ୍ରା\t-5888\n▁ଶିକ୍ଷୟିତ୍ରୀ\t-5889\nୟତ\t-5890\nସେନ\t-5891\nୋକି\t-5892\n▁ପଈ\t-5893\n▁ପଶ\t-5894\nଭୂଷଣ\t-5895\n▁ମକର\t-5896\n▁ଆଫ୍ର\t-5897\n▁କହିବ\t-5898\n▁କ୍ଷତ\t-5899\n▁କ୍ଷୁ\t-5900\n▁ନୁହେ\t-5901\n▁ବ୍ଲା\t-5902\n▁ମାଗୁ\t-5903\n▁ସଡ଼କ\t-5904\nସ୍ପର୍ଶ\t-5905\n▁ବାଟରେ\t-5906\n▁ବୋଇଲେ\t-5907\n▁ଉତ୍ପାଦ\t-5908\n▁ପ୍ରମୋଦ\t-5909\n▁କ୍ରୀଡ଼ା\t-5910\n▁ପୋଲିସକୁ\t-5911\n▁ବିଶ୍ରାମ\t-5912\n▁ଗାନ୍ଧୀଙ୍କ\t-5913\n,\"\t-5914\n୨୪\t-5915\nଟରୁ\t-5916\nଭେଦ\t-5917\nସରଣ\t-5918\nସିନ\t-5919\n▁ସଜ\t-5920\n▁୫୪\t-5921\nପରିକ\t-5922\n▁ଡକା\t-5923\n▁ମିଲ\t-5924\nମଣ୍ଡଳ\t-5925\n▁ଟାଟା\t-5926\n▁ଧିରେ\t-5927\n▁ପୋର୍\t-5928\nସିସିଆଇ\t-5929\n▁ପୁନର୍\t-5930\n▁ସବୁଦିନ\t-5931\n▁ସାଙ୍ଗେ\t-5932\n▁ଆଣିବାକୁ\t-5933\n▁ଗ୍ରୀଷ୍ମ\t-5934\n▁ଦେଖନ୍ତୁ\t-5935\n▁ଆମ୍ବୁଲାନ୍ସ\t-5936\nକୃ\t-5937\nହାଣ\t-5938\n▁be\t-5939\n▁ନୈ\t-5940\n▁ଅଦ୍\t-5941\n▁ଖରା\t-5942\n▁ଧାମ\t-5943\n▁ବିଲ\t-5944\n▁ସିକ\t-5945\n▁ଚିହ୍\t-5946\n▁ଟେକ୍\t-5947\n▁ଦୋଷୀ\t-5948\n▁ନକଲି\t-5949\n▁ପ୍ରବ\t-5950\nଧ୍ୟକ୍ଷ\t-5951\n▁ଚାନ୍ଦ\t-5952\n▁ଚେତନା\t-5953\n▁ବୃନ୍ଦ\t-5954\nକାରୀଙ୍କ\t-5955\nକୋର୍ଟରେ\t-5956\n▁ପରଠାରୁ\t-5957\n▁ଯୋଦ୍ଧା\t-5958\n▁ପଡିଥିଲା\t-5959\n▁ପୃଥିବୀର\t-5960\n▁ସପକ୍ଷରେ\t-5961\nim\t-5962\nଜୀବ\t-5963\nମହା\t-5964\nଷମା\t-5965\n▁ଝି\t-5966\n▁୫୭\t-5967\nCHIN\t-5968\nଳିବା\t-5969\nଳେଷଣ\t-5970\n▁SOA\t-5971\n▁ଅଖି\t-5972\n▁ଅଭୟ\t-5973\n▁ଝଡ଼\t-5974\n▁ଦୁର\t-5975\n▁ମଡେ\t-5976\n▁PPRA\t-5977\n▁ଗୋପୀ\t-5978\n▁ପଟେଲ\t-5979\n▁ବାଛି\t-5980\n▁ବାତି\t-5981\n▁ଭୟରେ\t-5982\nକ୍ଷୀରେ\t-5983\n▁ବନ୍ଦୀ\t-5984\n▁ବିଶାଳ\t-5985\n▁ଭ୍ରମଣ\t-5986\n▁ସିନ୍ଦ\t-5987\n▁ଚୁକ୍ତି\t-5988\n▁ଟ୍ରମ୍ପ\t-5989\n▁ନିଆଯିବ\t-5990\n▁ମହାନଦୀ\t-5991\n▁ଲାଗୁଛି\t-5992\n▁ସାବଧାନ\t-5993\n▁ଅଞ୍ଚଳକୁ\t-5994\n▁ଗ୍ରାମ୍ୟ\t-5995\n▁ମୁଖ୍ୟତଃ\t-5996\n▁PPRACHIN\t-5997\n▁ପଡ଼ିଥିବା\t-5998\n▁ବର୍ତ୍ତମାନର\t-5999\n▁ମହାବିଦ୍ୟାଳୟ\t-6000\nଏଇ\t-6001\nବୟ\t-6002\nଆପଣ\t-6003\nଆମେ\t-6004\nଭର୍\t-6005\nାନକ\t-6006\nାନର\t-6007\nଗୋଟି\t-6008\nଲୁକା\t-6009\n▁ବାଳ\t-6010\n▁ବିନ\t-6011\n▁ସୌର\t-6012\nଟ୍ରିକ\t-6013\n▁ଆଇନ୍\t-6014\n▁କନ୍ଦ\t-6015\n▁କ୍ଲି\t-6016\n▁ମର୍ମ\t-6017\n▁ମତାମତ\t-6018\nଶାସ୍ତ୍ର\t-6019\n▁ବିପକ୍ଷ\t-6020\n▁ଆଣିଛନ୍ତି\t-6021\n▁କର୍ମକର୍ତ୍ତା\t-6022\n'।\t-6023\nତୁର\t-6024\nମିର\t-6025\nମ୍ରେ\t-6026\n▁କଟି\t-6027\nବର୍ଷର\t-6028\n▁କେସ୍\t-6029\n▁ବୟସର\t-6030\n▁ସାମଲ\t-6031\n▁ସ୍ଥଳ\t-6032\n▁ଅକ୍ଷର\t-6033\n▁ପୋଖରୀ\t-6034\n▁ଉଚ୍ଛେଦ\t-6035\n▁କର୍ମରେ\t-6036\n▁ଚ୍ୟାଲେ\t-6037\n▁ବିଭନ୍ନ\t-6038\n▁ସୀମାରେ\t-6039\n▁ଆପଣମାନେ\t-6040\n▁ଯୁବକଙ୍କ\t-6041\n▁ରାଜ୍ୟକୁ\t-6042\n▁କାର୍ତ୍ତିକ\t-6043\n▁ଦୁର୍ବୃତ୍ତ\t-6044\n▁କାର୍ଯ୍ୟକ୍ରମକୁ\t-6045\n୨୭\t-6046\n୍ବି\t-6047\n▁ଏହ\t-6048\n▁ହଲ\t-6049\nରାଲ୍\t-6050\nିକତା\t-6051\n▁ତୁଳ\t-6052\n▁ମହୁ\t-6053\n▁ଅଧିନ\t-6054\n▁ଉପାସ\t-6055\n▁କ୍ରମ\t-6056\n▁କ୍ଲା\t-6057\n▁ଚାହି\t-6058\n▁ମାହା\t-6059\n▁କାଣ୍ଡ\t-6060\n▁ଖାଇଲେ\t-6061\n▁ଫାଷ୍ଟ\t-6062\n▁ରୋଷେଇ\t-6063\n▁ସୁରେଶ\t-6064\n▁କରାଇବା\t-6065\n▁ପ୍ରଚୁର\t-6066\n▁ପରୀକ୍ଷଣ\t-6067\n▁ଉପନିର୍ବାଚନ\t-6068\nୀପ\t-6069\n▁୪୯\t-6070\n▁ଓଭର\t-6071\n▁କାଲ\t-6072\n▁କେଜ\t-6073\n▁ଗାଧ\t-6074\n▁ତୈଳ\t-6075\n▁ବଲ୍\t-6076\n▁ଶିଖ\t-6077\nିତାରେ\t-6078\n▁ଭାଲୁ\t-6079\n▁ସୁଦୃ\t-6080\n▁ଆଧ୍ୟା\t-6081\n▁ବିଧାୟ\t-6082\n▁ବୋଲିଂ\t-6083\n▁ଭଙ୍ଗା\t-6084\n▁ବିଭାଗରେ\t-6085\n▁ହାଇସ୍କୁ\t-6086\n▁ଲାଗିଥିଲା\t-6087\n▁ଶୁଭାରମ୍ଭ\t-6088\n▁କରିଥିବାରୁ\t-6089\n▁ବିଦ୍ୟାଳୟରେ\t-6090\n▁କେନ୍ଦ୍ରାପଡ଼ା\t-6091\n▁ଛାତ୍ରଛାତ୍ରୀଙ୍କ\t-6092\nୃଶ\t-6093\n▁ଚୈ\t-6094\n▁୪୭\t-6095\n▁ସ୍ର\t-6096\nକାଳରେ\t-6097\n▁ଅଜ୍ଞ\t-6098\n▁ଡେଇଁ\t-6099\n▁ସୁନି\t-6100\n▁କମିଶନ\t-6101\n▁ଚକ୍ଷୁ\t-6102\n▁ଭୁମ୍ଭ\t-6103\n▁ଶଙ୍କା\t-6104\n▁ୱିକେଟ\t-6105\n▁ଇଂରାଜୀ\t-6106\n▁ସମାପ୍ତ\t-6107\nଖେମୁଣ୍ଡି\t-6108\n▁ଅତ୍ୟଧିକ\t-6109\n▁ଦୁନିଆରେ\t-6110\n▁ବିବାଦୀୟ\t-6111\n▁ପ୍ରିୟଙ୍କା\t-6112\n▁ରିପୋର୍ଟରେ\t-6113\n▁କର୍ମକ୍ଷେତ୍ରରେ\t-6114\n୍ୟର\t-6115\n▁ଥଲ\t-6116\nକାରି\t-6117\nଶେଖର\t-6118\n▁ଆଭି\t-6119\n▁ଦିବ\t-6120\nବିଧାର\t-6121\n▁ଅଣ୍ଡା\t-6122\n▁ଏଠାକୁ\t-6123\n▁କମ୍ବା\t-6124\n▁ପୁର୍ବ\t-6125\n▁ବିଦାୟ\t-6126\n▁ଚେହେରା\t-6127\n▁ପାରିଲେ\t-6128\n▁ବନ୍ଧୁକ\t-6129\n▁ମୌସୁମୀ\t-6130\nାନ୍ତ୍ରିକ\t-6131\n▁ଅଞ୍ଚଳରୁ\t-6132\n▁ନିରଞ୍ଜନ\t-6133\n▁ଶ୍ରୀମତୀ\t-6134\n▁କାର୍ଯ୍ୟକୁ\t-6135\n▁ଶ୍ରୀଲଙ୍କା\t-6136\n▁ସଂକ୍ରାନ୍ତ\t-6137\nଥୁନ\t-6138\n▁ହଉ\t-6139\nୃଙ୍ଖ\t-6140\n୍ୟରେ\t-6141\n▁ଆଗେ\t-6142\n▁ଜିଓ\t-6143\n▁ଭେଦ\t-6144\n▁ଯୌନ\t-6145\n▁ଲତା\t-6146\nକାରରେ\t-6147\n▁ଆଇଆଇ\t-6148\n▁ଜୀବା\t-6149\n▁ଦୀପକ\t-6150\n▁ଭାହା\t-6151\n▁ସ୍ନା\t-6152\n▁ଗୁଣ୍ଡ\t-6153\n▁ଧାଡ଼ି\t-6154\n▁ବୁଝାଇ\t-6155\n▁ଯେମିତ\t-6156\n▁ସୁଧାର\t-6157\n▁ଅନୁବାଦ\t-6158\n▁ଆଫଗାନି\t-6159\n▁ଆରୋଗ୍ୟ\t-6160\n▁ଏକାଧିକ\t-6161\n▁ରିଲିଜ୍\t-6162\n▁ଶତାଧିକ\t-6163\n▁ଯାଆନ୍ତି\t-6164\n▁ସଂସ୍କରଣ\t-6165\n▁ସତ୍ତ୍ବେ\t-6166\n▁ମାନଙ୍କରେ\t-6167\n▁କ୍ରାଇମବ୍ରାଞ୍ଚ\t-6168\nହଳ\t-6169\nପଥର\t-6170\nହିର\t-6171\nୂପେ\t-6172\nନ୍ଧି\t-6173\nଲପୁର\t-6174\nଲେଟ୍\t-6175\n▁ଜଟି\t-6176\n▁ବସୁ\t-6177\nବନ୍ଦର\t-6178\n▁ଅପରି\t-6179\n▁ହାର୍\t-6180\nସଭାପତି\t-6181\n▁ଏସ୍ପି\t-6182\n▁ଭାଜପା\t-6183\n▁ରାଜପଥ\t-6184\n▁କ୍ୱାରେ\t-6185\n▁ଗୁପ୍ତା\t-6186\n▁ବ୍ରିଟି\t-6187\n▁ଆଶୀର୍ବାଦ\t-6188\n▁ଭୂମିକାରେ\t-6189\n▁ଲାଗିଥିବା\t-6190\nts\t-6191\nଗନ\t-6192\nୂତ\t-6193\n▁19\t-6194\nନାମା\t-6195\nର୍ମୁ\t-6196\n▁ବାଧ\t-6197\n▁ଠକେଇ\t-6198\n▁ବଢାଇ\t-6199\n▁ସୀତା\t-6200\nଜିଲ୍ଲା\t-6201\nମଧ୍ୟରୁ\t-6202\n▁ଅମିତା\t-6203\n▁ଆଜ୍ଞା\t-6204\n▁ଉପଦେଶ\t-6205\n▁ଦୁହିଁ\t-6206\n▁ଶଯ୍ୟା\t-6207\n▁ସଂଗୀତ\t-6208\nଲକ୍ଷ୍ମୀ\t-6209\n▁ଉଦ୍ୟୋଗ\t-6210\n▁କଲେଜରେ\t-6211\n▁କଷ୍ଟକର\t-6212\n▁ଭଣ୍ଡାର\t-6213\n▁ସ୍ବାଗତ\t-6214\n▁ପରିବେଷଣ\t-6215\n▁ମୃତ୍ୟୁର\t-6216\n▁ଶ୍ରୀରାମ\t-6217\n▁ଯୁବକଙ୍କୁ\t-6218\n▁ପ୍ରତିବନ୍ଧ\t-6219\n▁ହସ୍ତାନ୍ତର\t-6220\nଅପ୍\t-6221\nକାଲ\t-6222\n▁ମଳ\t-6223\n▁ସନ\t-6224\nଜାଣି\t-6225\nଞ୍ଜର\t-6226\nପାର୍\t-6227\nବ୍ରି\t-6228\nମାନର\t-6229\nଲିରେ\t-6230\n▁ଖାଉ\t-6231\n▁ବୈଦ\t-6232\n▁ବୈଷ\t-6233\n▁ସୀତ\t-6234\n▁ସୁଭ\t-6235\nପକ୍ଷେ\t-6236\nପେକ୍ଷ\t-6237\nମାନ୍ତ\t-6238\nିନିୟର\t-6239\n▁ଝିଅର\t-6240\n▁ଟୀକା\t-6241\n▁ପକେଇ\t-6242\n▁ମୁନି\t-6243\nତାମୂଳକ\t-6244\n▁ଦୂରତା\t-6245\n▁ବେଗରେ\t-6246\n▁ମାଙ୍କ\t-6247\n▁ମେହେର\t-6248\n▁ଲିଙ୍କ\t-6249\n▁ଲଗାତାର\t-6250\n▁ଶୁଣିଲେ\t-6251\nଙ୍ଗାଲୁରୁ\t-6252\nମୁମ୍ବାଇଃ\t-6253\n▁ଜିଲାପାଳ\t-6254\n▁ଦେଖାଯାଉ\t-6255\n▁ମୋବାଇଲ୍\t-6256\nପ୍ରେକ୍ଷୀରେ\t-6257\n▁ଶ୍ରୀଜଗନ୍ନାଥ\t-6258\nୋଳ\t-6259\n୩୫\t-6260\nଲ୍ଟ\t-6261\n▁୬୪\t-6262\nଥୁଲି\t-6263\nନେତା\t-6264\nପୋରେ\t-6265\nଲିୟନ\t-6266\n▁con\t-6267\n▁ଗୁର\t-6268\n▁ତ୍ର\t-6269\nତ୍ୟାଗ\t-6270\nଭାବରେ\t-6271\nରାଜିତ\t-6272\nଶେଷରେ\t-6273\n▁ବୋଲର\t-6274\nସ୍ଥାୟୀ\t-6275\n▁ଚିତ୍ତ\t-6276\n▁ପଡୁଛି\t-6277\n▁ପ୍ରଭା\t-6278\n▁ବନ୍ଧା\t-6279\n▁ମଧ୍ୟା\t-6280\nପୂର୍ବରୁ\t-6281\n▁ବ୍ରିଟେ\t-6282\n▁ସମୃଦ୍ଧ\t-6283\n▁ହୃଦୟରେ\t-6284\n▁ବସିଥିବା\t-6285\n▁ସ୍ବାର୍ଥ\t-6286\n▁ସେଗୁଡ଼ିକ\t-6287\n▁ପ୍ରତିରକ୍ଷା\t-6288\n▁କରାଯାଇପାରିବ\t-6289\nod\t-6290\nop\t-6291\nୌକ\t-6292\nଫରେ\t-6293\n▁ଅଇ\t-6294\nପ୍ରଦ\t-6295\nସତ୍ୟ\t-6296\n▁ଆମ୍\t-6297\n▁ଭର୍\t-6298\n▁ଶ୍ୱ\t-6299\n▁ଜେରା\t-6300\n▁ଦ୍ୱି\t-6301\n▁ପଥରେ\t-6302\n▁ମିଳନ\t-6303\n▁ସ୍ପେ\t-6304\nପେକ୍ଟର\t-6305\n▁ବଦଳାଇ\t-6306\nଯାଇପାରେ\t-6307\n▁ଖୁସିରେ\t-6308\n▁ଖୋଲିବା\t-6309\n▁ଚୌଧୁରୀ\t-6310\n▁ପରିଣାମ\t-6311\n▁ମାତ୍ରା\t-6312\n▁କାର୍ୟ୍ୟ\t-6313\n▁ପ୍ରଣାଳୀ\t-6314\n▁ଚିନ୍ତାରେ\t-6315\n▁ମିଶ୍ରଙ୍କ\t-6316\nଓ୍\t-6317\n୨୬\t-6318\nସରୁ\t-6319\nମିଟେ\t-6320\n▁ଗଦା\t-6321\n▁ଚାଲ\t-6322\n▁ହିର\t-6323\nକ୍ଷ୍ମ\t-6324\nିକାଲି\t-6325\n▁କବାଟ\t-6326\n▁ଗୋରୁ\t-6327\n▁ନକ୍ଷ\t-6328\n▁ପିପି\t-6329\n▁ଫକୀର\t-6330\n▁ଉପକରଣ\t-6331\n▁ଉପକୃତ\t-6332\n▁କାନ୍ଥ\t-6333\n▁ମିତ୍ର\t-6334\n▁ରଖିଲେ\t-6335\n▁ଅନୁସରଣ\t-6336\n▁ଉଦଘାଟନ\t-6337\n▁ବାସଭବନ\t-6338\n▁ବାହାରୁ\t-6339\n▁ଲଘୁଚାପ\t-6340\n▁ସାଇକେଲ\t-6341\n▁ଅମ୍ଳଜାନ\t-6342\n▁ନେଇଥିଲା\t-6343\n▁ପଢ଼ନ୍ତୁ\t-6344\n▁ଲାଗିଥାଏ\t-6345\n▁କ୍ରିକେଟର\t-6346\n▁ମନ୍ତ୍ରାଳୟ\t-6347\n▁ସେତେବେଳକୁ\t-6348\n▁କାର୍ଯ୍ୟାଳୟରେ\t-6349\nେଶା\t-6350\n୍ତା\t-6351\n▁re\t-6352\n▁ଧୈ\t-6353\nଗାରେ\t-6354\n▁ଧନୀ\t-6355\nୋତ୍ତମ\t-6356\n▁ଗର୍ବ\t-6357\n▁ତହିଁ\t-6358\n▁ଭାଷଣ\t-6359\n▁ମେସେ\t-6360\n▁ରୋମା\t-6361\n▁କ୍ରମା\t-6362\n▁ବିପୁଳ\t-6363\n▁ମାଗଣା\t-6364\nକ୍ଷିପ୍ତ\t-6365\n▁ଉପାଦାନ\t-6366\n▁ଜୀବନ୍ତ\t-6367\n▁ଯୋଜନାର\t-6368\n▁ସଙ୍କେତ\t-6369\n▁ଅନୁମୋଦନ\t-6370\n▁ବିସିସିଆଇ\t-6371\nବଂଶ\t-6372\nାବନ\t-6373\nନ୍ଦି\t-6374\nମ୍ବେ\t-6375\nଳିରେ\t-6376\n▁ନିଶ\t-6377\n▁ଫସି\t-6378\n▁ଆଇସି\t-6379\n▁ଉଜ୍ଜ\t-6380\n▁ଗଛରେ\t-6381\n▁ବର୍ତ\t-6382\nସାହିତ୍ୟ\t-6383\n▁ଅବହେଳା\t-6384\n▁ଅର୍ଚ୍ଚ\t-6385\n▁କେତେଜଣ\t-6386\n▁ଗଞ୍ଜେଇ\t-6387\n▁ଦୁର୍ଗା\t-6388\n▁ବିଭକ୍ତ\t-6389\n▁ସମାଜସେ\t-6390\n▁ଅଗ୍ନିକା\t-6391\n▁ନେତାମାନେ\t-6392\n▁ମେଡିକାଲରେ\t-6393\not\t-6394\nମହ\t-6395\nୁଁ\t-6396\nଧନା\t-6397\nବଡ଼\t-6398\n▁ଗ୍\t-6399\n▁ନୟ\t-6400\n▁୫୧\t-6401\nରାଣୀ\t-6402\nୂର୍ବ\t-6403\n▁ମନି\t-6404\nଷ୍ଟାର\t-6405\n▁ଏସିଆ\t-6406\n▁ଚାଳକ\t-6407\n▁ସେଲ୍\t-6408\n▁କୂଳରେ\t-6409\n▁ଚତୁର୍\t-6410\n▁ପରିବା\t-6411\n▁ଶୋଇବା\t-6412\n▁ସୈନିକ\t-6413\n▁ହୋଇଗଲ\t-6414\n▁ଲକ୍ଷରୁ\t-6415\n▁ସନ୍ଧାନ\t-6416\n▁ସ୍କୁଲର\t-6417\n▁ହୋଇପଡି\t-6418\nଯାଇଛନ୍ତି\t-6419\n▁ଓଡିଶାରେ\t-6420\n▁ଆଫଗାନିସ୍ତାନ\t-6421\nଟୀ\t-6422\nଉଁଶ\t-6423\nରାଣ\t-6424\nଷେଧ\t-6425\n▁):\t-6426\nବାହୀ\t-6427\n▁ଆଦର\t-6428\n▁ଥିଲ\t-6429\n▁ଧ୍ବ\t-6430\nାମିନ୍\t-6431\n▁ବୋମା\t-6432\n▁ସେମି\t-6433\n▁ଏଣିକି\t-6434\n▁ପୁରାଣ\t-6435\n▁କବିତାର\t-6436\n▁କ୍ରିୟା\t-6437\n▁ଗ୍ରାମା\t-6438\n▁ବ୍ଲକରେ\t-6439\n▁ସ୍ତମ୍ଭ\t-6440\n▁ପଡ଼ିଥାଏ\t-6441\n▁ପ୍ରକାରେ\t-6442\n▁ଯନ୍ତ୍ରୀ\t-6443\nill\t-6444\nାୟୁ\t-6445\n▁ଠୁ\t-6446\nବସ୍ତ\t-6447\nଶୁଣି\t-6448\nସେଚନ\t-6449\n▁ଚାର\t-6450\n▁ତୋର\t-6451\nତାରିଖ\t-6452\n▁ଉଠିଲ\t-6453\n▁କରୁଣ\t-6454\n▁ଜଗତର\t-6455\n▁ବିଷା\t-6456\n୍ୟାନ୍ସ\t-6457\n▁ଉପକାର\t-6458\n▁ଛିଡ଼ା\t-6459\n▁ଜନ୍ତୁ\t-6460\n▁ଥିବାର\t-6461\n▁ପାଶ୍ଚ\t-6462\n▁ଶତପଥୀ\t-6463\n▁ସେଇଠି\t-6464\n▁ସ୍ୱରୂପ\t-6465\n▁କୋଣାର୍କ\t-6466\n▁ଫେବୃଆରୀ\t-6467\n▁ୟୁକ୍ରେନ\t-6468\n▁ଏମାନଙ୍କୁ\t-6469\n▁ପ୍ରଫୁଲ୍ଲ\t-6470\nep\t-6471\nଥେ\t-6472\nଲମ\t-6473\nଏସି\t-6474\nବଳୀ\t-6475\n▁ଉଲ\t-6476\n▁୫୯\t-6477\nତନ୍ୟ\t-6478\nଧାନା\t-6479\nମାନୁ\t-6480\n▁ଅସଂ\t-6481\nତ୍ୟାର\t-6482\nମୁହିଁ\t-6483\n▁ଘେରି\t-6484\n▁ମାୟା\t-6485\n▁ମୃତକ\t-6486\n▁ମୟୁର\t-6487\n▁ଶାଶୁ\t-6488\n▁ଶୁକ୍\t-6489\n▁କୁଣ୍ଡ\t-6490\n▁ଜିଲାର\t-6491\n▁ଟ୍ରକ୍\t-6492\n▁ଦିନରୁ\t-6493\n▁ପର୍ବତ\t-6494\n▁ରହୁଛି\t-6495\n▁ସମ୍ବଳ\t-6496\n▁ସିରିଜ\t-6497\n▁ଅପଡେଟ୍\t-6498\n▁ତ୍ରୁଟି\t-6499\n▁ପ୍ରଭୃତ\t-6500\n▁ବିଜ୍ଞା\t-6501\n▁ମହାନ୍ତ\t-6502\n▁ରେଳବାଇ\t-6503\n▁ବକ୍ତବ୍ୟ\t-6504\n▁ବିଦ୍ରୋହ\t-6505\n▁ମଧ୍ୟାହ୍ନ\t-6506\n▁ମୁଖପାତ୍ର\t-6507\n▁ବ୍ୟବଚ୍ଛେଦ\t-6508\n▁ସାହିତ୍ୟିକ\t-6509\nଜେନ\t-6510\nରୀଣ\t-6511\n▁ଷଡ\t-6512\n▁୫୩\t-6513\nପୂରଣ\t-6514\nଭିଜନ\t-6515\n▁ଖାର\t-6516\n▁ଜାଲ\t-6517\n▁ନେଉ\t-6518\n▁ଭୂତ\t-6519\n▁ମୁର\t-6520\n▁ସବ୍\t-6521\nତ୍ମିକ\t-6522\nନ୍ଦ୍ୱ\t-6523\n▁ପିଆଜ\t-6524\n▁ବନ୍ୟ\t-6525\n▁ଭିଡ଼\t-6526\nଶସ୍ତ୍ର\t-6527\n▁ବିପ୍ଳ\t-6528\n▁ଶିବିର\t-6529\n▁ସାଧନା\t-6530\n▁ହୋଟେଲ\t-6531\n▁ନିକଟରୁ\t-6532\n▁ଲକ୍ଷ୍ମଣ\t-6533\n▁ଷ୍ଟାଡିୟ\t-6534\n▁କରାଇଥିଲେ\t-6535\n▁ପହଞ୍ଚିଥିଲେ\t-6536\nଦାବ\t-6537\nଶାନ\t-6538\nଷ୍କ\t-6539\nୀଶୁ\t-6540\n▁କୀ\t-6541\nment\t-6542\nଷାରେ\t-6543\nସ୍ବୀ\t-6544\n▁କ୍ଳ\t-6545\n▁ଗାନ\t-6546\n▁ବାଜ\t-6547\nସିଆଲ୍\t-6548\nସେନ୍ସ\t-6549\n▁ଆଉଟ୍\t-6550\n▁କୁନି\t-6551\n▁ମାଝୀ\t-6552\n▁ରାଗି\t-6553\n▁ଶାହା\t-6554\n▁୨୦୧୬\t-6555\nକେନ୍ୟୁ\t-6556\n▁କ୍ରୋଧ\t-6557\n▁ଗବେଷକ\t-6558\n▁ଟ୍ରାଫ\t-6559\n▁ଦେଖିଲ\t-6560\n▁ପିତାମ\t-6561\n▁ମାଡ଼ି\t-6562\n▁ସାର୍ଥ\t-6563\n▁ଅବକାରୀ\t-6564\n▁ଜୟନ୍ତୀ\t-6565\n▁ସୁବର୍ଣ୍ଣ\t-6566\n▁ଇଣ୍ଡଷ୍ଟ୍ରି\t-6567\n▁ବିମାନବନ୍ଦର\t-6568\nଏଚ\t-6569\nଧାମ\t-6570\n▁ଅଠ\t-6571\n▁ଡକ\t-6572\n▁ଦମ\t-6573\n▁ଲୀ\t-6574\nକ୍ରୋ\t-6575\nସ୍ବର\t-6576\n▁ଅବର\t-6577\n▁ଅଶୁ\t-6578\n▁ଇନି\t-6579\n▁ଜଗି\t-6580\n▁ରିଜ\t-6581\nକିଶୋର\t-6582\n▁ଆଗୁଆ\t-6583\n▁ଗୃହି\t-6584\n▁ନିଗମ\t-6585\n▁ରଚିତ\t-6586\n▁ରୋଗର\t-6587\n▁ହୋଇନ\t-6588\nଲକ୍ଷିତ\t-6589\n▁ଏକାଡେ\t-6590\n▁କହିବେ\t-6591\n▁କାଳିଆ\t-6592\n▁ଜାଗ୍ର\t-6593\n▁ଡାକ୍ତ\t-6594\n▁ଡିଜେଲ\t-6595\nଦ୍ଧାରିତ\t-6596\n▁ପଡିଥାଏ\t-6597\n▁ବ୍ୟାଟ୍\t-6598\n▁ଯେହେତୁ\t-6599\n▁ରୁହନ୍ତୁ\t-6600\n▁ତାହାହେଲେ\t-6601\n▁ବଢ଼ିବାରେ\t-6602\n▁ଭଗବାନଙ୍କ\t-6603\n▁ମ୍ୟାଚ୍ରେ\t-6604\n▁ରହୁଛନ୍ତି\t-6605\nକମ\t-6606\nଜିଂ\t-6607\n▁ବଂ\t-6608\nହେବା\t-6609\n▁ଆସୋ\t-6610\n▁ଏଲି\t-6611\n▁କମା\t-6612\n▁ଛବି\t-6613\n▁ଜୁନ\t-6614\n▁ଫ୍ଲ\t-6615\n▁ଶରଣ\t-6616\nନଙ୍କୁ\t-6617\nନାଙ୍କ\t-6618\nିବାକୁ\t-6619\n▁ଅସୁର\t-6620\n▁ପ୍ରହ\t-6621\n▁ମଧ୍ଯ\t-6622\n▁ସ୍ଥଗ\t-6623\n▁ମସ୍ତି\t-6624\n▁ପୁରାତନ\t-6625\n▁ବିକ୍ରମ\t-6626\n▁ଶିଳ୍ପୀ\t-6627\n▁ଏଜେନ୍ସି\t-6628\n▁କ୍ରମାଗତ\t-6629\n▁ଗର୍ଭବତୀ\t-6630\n▁ଜନ୍ମଦିନ\t-6631\n▁ସାନିଟାଇ\t-6632\n,(\t-6633\n୧ରେ\t-6634\nଗୁଡ଼\t-6635\n▁ନୀତ\t-6636\n▁ବେପ\t-6637\n▁୩୦୦\t-6638\n▁ଅଶ୍ୱ\t-6639\n▁ଜେଲ୍\t-6640\n▁ପାଗଳ\t-6641\n▁ବୋହି\t-6642\n▁ସ୍ୱର\t-6643\n▁ହଠାତ\t-6644\nଣ୍ଡାରେ\t-6645\nଦେବାକୁ\t-6646\n▁ଆପଣାର\t-6647\n▁କରିଛୁ\t-6648\n▁ପ୍ଲାନ\t-6649\n▁ଭବାନୀ\t-6650\n▁ଆସନ୍ତି\t-6651\n▁କାହାରି\t-6652\n▁ଗୁଜରାଟ\t-6653\n▁ନିନ୍ଦା\t-6654\n▁ସଂଧ୍ୟା\t-6655\n▁କାରଖାନା\t-6656\n▁ପାର୍ଟିର\t-6657\n▁ବୈଦେଶିକ\t-6658\n▁ମ୍ୟାନେଜର\t-6659\n▁ଶୁଭେଚ୍ଛା\t-6660\n▁ସମସ୍ୟାକୁ\t-6661\n▁ବିପର୍ଯ୍ୟୟ\t-6662\n▁ୱେବସାଇଟରେ\t-6663\nse\t-6664\n▁9\t-6665\nଲୋଗ\t-6666\n▁ଡୁ\t-6667\n▁ପୈ\t-6668\nାମଣି\t-6669\n▁ଚରମ\t-6670\n▁ଜବା\t-6671\n▁ପୂର\t-6672\n▁ଯାକ\t-6673\n▁କାଳି\t-6674\n▁ଛାଡ଼\t-6675\n▁ଦାରି\t-6676\n▁ଲୁଚି\t-6677\n▁ଶଙ୍ଖ\t-6678\n▁ଅଙ୍ଗନ\t-6679\n▁ଅଦାଲତ\t-6680\n▁ଗବେଷଣ\t-6681\n▁ଭତ୍ତା\t-6682\n▁ସୁଟିଂ\t-6683\n▁ସଂକଳ୍ପ\t-6684\nଯାତ୍ରାରେ\t-6685\n▁ଶିକ୍ଷାର\t-6686\n▁ଅସୁବିଧାର\t-6687\n▁ବ୍ୟକ୍ତିର\t-6688\n▁ଶତାବ୍ଦୀର\t-6689\n▁ଅଭିବୃଦ୍ଧି\t-6690\n▁ଦେଖାଦେଇଛି\t-6691\n▁ଶିକ୍ଷାନୁଷ୍ଠାନ\t-6692\nମାଇ\t-6693\n▁ଥି\t-6694\nଞ୍ଜୟ\t-6695\nଦେଲା\t-6696\nସ୍ତକ\t-6697\n▁ଗୁଇ\t-6698\n▁ଚର୍\t-6699\n▁ମେଡ଼\t-6700\n▁ମେଳା\t-6701\n▁ସଠିକ\t-6702\n▁ୟୁପି\t-6703\n▁ତାଲିମ\t-6704\n▁ଦିନିଆ\t-6705\n▁ବୁଝାମ\t-6706\n▁ସଂଗ୍ର\t-6707\n▁ସୌମ୍ୟ\t-6708\n▁ପୃଷ୍ଠା\t-6709\n▁ଶରୀରକୁ\t-6710\n▁ସମ୍ପାଦ\t-6711\n▁ନିର୍ଯାତ\t-6712\nନାହାଁନ୍ତି\t-6713\n▁ତତ୍କାଳୀନ\t-6714\n▁ଦେବାପାଇଁ\t-6715\n▁ଉଲ୍ଲେଖଯୋଗ୍ୟ\t-6716\n▁କେନ୍ଦ୍ରାପଡା\t-6717\n▁ନିର୍ଦ୍ଧାରିତ\t-6718\n୨୯\t-6719\nଟର୍\t-6720\nପୋଷ\t-6721\nମନା\t-6722\nୋଧନ\t-6723\n▁୯୯\t-6724\nକେଟ୍\t-6725\nଥୁଲା\t-6726\nଲିଟି\t-6727\n▁ଏଡି\t-6728\n▁ଦେଓ\t-6729\n▁ନାୟ\t-6730\n▁ପେନ\t-6731\n▁ବେଳ\t-6732\n▁ସନା\t-6733\n▁ସସ୍\t-6734\nଧାନ୍ୟ\t-6735\n▁କମଳା\t-6736\n▁ପଦରୁ\t-6737\n▁ଫ୍ୟା\t-6738\n▁ବାଲା\t-6739\n▁ବିତି\t-6740\n▁ମୋଟା\t-6741\n▁ସୁନୀ\t-6742\nଯିବାରୁ\t-6743\nଷ୍ଟ୍ରେ\t-6744\n▁କଲେଜର\t-6745\n▁ଚେୟାର\t-6746\n▁ବସିଲେ\t-6747\n▁ବାତିଲ\t-6748\n▁ଲଢ଼େଇ\t-6749\n▁ସଙ୍କଟ\t-6750\n▁ଦିଆଯାଏ\t-6751\n▁ସଭ୍ୟତା\t-6752\n▁ସ୍ଥାନରୁ\t-6753\n▁ନୁହଁନ୍ତି\t-6754\n▁ବନ୍ଧୁଙ୍କ\t-6755\n▁ପାଶ୍ଚାତ୍ୟ\t-6756\n▁ହତ୍ୟାକାଣ୍ଡ\t-6757\nch\t-6758\nub\t-6759\nଗୀତ\t-6760\nଥିତ\t-6761\nଲାସ\t-6762\nହରି\t-6763\nୀକୁ\t-6764\n▁ଗଉ\t-6765\n▁ଛାତ\t-6766\n▁ଲହର\t-6767\n▁ଟ୍ରକ\t-6768\n▁ଡାଟା\t-6769\n▁ଦେଢ଼\t-6770\n▁ଦେଶୀ\t-6771\n▁ବୋଲ୍\t-6772\n▁ମେଗା\t-6773\n▁ଲାଇନ\t-6774\n▁୧୦ଟି\t-6775\n▁୨୦୧୪\t-6776\nବନିମ୍ନ\t-6777\nବଲାଙ୍ଗ\t-6778\nିତାଙ୍କ\t-6779\n▁ଜାତିର\t-6780\n▁ଦାର୍ଶ\t-6781\n▁ଫୋର୍ସ\t-6782\n▁ଭାଇରସ\t-6783\n▁ମଞ୍ଜି\t-6784\n▁ସଠିକ୍\t-6785\n▁ଆଇପିଏଲ\t-6786\n▁ଆହ୍ବାନ\t-6787\n▁ଚାକିରୀ\t-6788\n▁ତତ୍ତ୍ୱ\t-6789\n▁ନିହାତି\t-6790\n▁ସ୍ବଚ୍ଛ\t-6791\n▁ହନୁମାନ\t-6792\n▁କର୍ପୋରେ\t-6793\n▁ଧୈର୍ଯ୍ୟ\t-6794\n▁ରାୟଗଡ଼ା\t-6795\n▁ବସିଛନ୍ତି\t-6796\n▁ନିମନ୍ତ୍ରଣ\t-6797\n▁ମୁମ୍ବାଇରେ\t-6798\n▁ମୁହାଁମୁହିଁ\t-6799\n▁କ୍ଷତିଗ୍ରସ୍ତ\t-6800\n▁ବାଧ୍ୟତାମୂଳକ\t-6801\n▁ପରିପ୍ରେକ୍ଷୀରେ\t-6802\nଏମସି\t-6803\nପ୍ରସ\t-6804\nପ୍ରି\t-6805\nଶାଳା\t-6806\n▁ଦାଗ\t-6807\n▁ଦୀନ\t-6808\n▁ବୃଷ\t-6809\nଗଣାରେ\t-6810\nମ୍ବିତ\t-6811\nିକାରୀ\t-6812\n▁ଅବଧି\t-6813\n▁ଖାତା\t-6814\n▁ଗୁଜା\t-6815\n▁ବସାଇ\t-6816\n▁ଜଗତରେ\t-6817\n▁ଜଡ଼ିତ\t-6818\n▁ପରାଜୟ\t-6819\n▁ସାଢ଼େ\t-6820\n▁ଟ୍ୱିଟ୍\t-6821\n▁ପୂର୍ବକ\t-6822\n▁ପ୍ରାୟତଃ\t-6823\n▁ପ୍ରୟୋଜନ\t-6824\n▁ବର୍ତମାନ\t-6825\n▁ହେବାପରେ\t-6826\n▁ହୋଇପାରୁ\t-6827\n▁ଆଭିମୁଖ୍ୟ\t-6828\n▁ଉପସଭାପତି\t-6829\n▁ବିଶ୍ଳେଷଣ\t-6830\n▁ମିଳିପାରେ\t-6831\n▁ମନ୍ତ୍ରୀଙ୍କ\t-6832\nଧ୍\t-6833\nଚେର\t-6834\nବର୍\t-6835\nଳୋକ\t-6836\n▁କପ\t-6837\nସାରା\t-6838\n▁201\t-6839\n▁କଦା\t-6840\n▁ଖାଁ\t-6841\n▁ଗଜା\t-6842\n▁ଘାତ\t-6843\n▁ମରଣ\t-6844\n▁ଏଟିଏ\t-6845\n▁ନିରବ\t-6846\n▁ବହୁଳ\t-6847\n▁ଭରସା\t-6848\n▁ଶୁଣୁ\t-6849\nଗୁଡିକୁ\t-6850\nଚ୍ଛନ୍ନ\t-6851\nଟ୍ରିକ୍\t-6852\n▁ଅନ୍ତଃ\t-6853\n▁ଉଠିଛି\t-6854\n▁ବୁଡ଼ି\t-6855\n▁ସିଷ୍ଟ\t-6856\nଯାଆନ୍ତି\t-6857\n▁କମେଣ୍ଟ\t-6858\n▁ଦୁନିଆର\t-6859\n▁ମନେହୁଏ\t-6860\n▁ସଂଗଠନର\t-6861\n▁ଇଣ୍ଡିଆର\t-6862\n▁ମନୁଷ୍ୟର\t-6863\n▁ସମାଜସେବୀ\t-6864\n▁ଆନ୍ତର୍ଜାତ\t-6865\n▁ବ୍ୟବସ୍ଥାକୁ\t-6866\nrom\t-6867\nଡାର\t-6868\nକାଂଶ\t-6869\n▁ଅମଳ\t-6870\n▁ଉର୍\t-6871\n▁ଏଣେ\t-6872\n▁କୃଷ\t-6873\n▁ଟମା\t-6874\nଟଙ୍କା\t-6875\n▁ଜ୍ବର\t-6876\n▁ବୀମା\t-6877\n▁ମିଠା\t-6878\nବେକ୍ଷଣ\t-6879\nମାନନ୍ଦ\t-6880\n▁ନମୁନା\t-6881\n▁ପରମାଣ\t-6882\n▁ପାଇଲା\t-6883\n▁ସର୍ଭି\t-6884\n▁ଅଭିଭାବ\t-6885\n▁କରିଦିଆ\t-6886\n▁ଦିବ୍ୟା\t-6887\n▁ବିଶ୍ବର\t-6888\n▁ଆଣିଥିଲେ\t-6889\n▁ଟ୍ରାନ୍ସ\t-6890\n▁ସର୍ବଶେଷ\t-6891\n▁ସ୍ୱୀକାର\t-6892\n▁ଜାନୁୟାରୀ\t-6893\n▁ନେଉଛନ୍ତି\t-6894\n▁ପୂର୍ଣ୍ଣି\t-6895\n▁ପ୍ରଶିକ୍ଷ\t-6896\n▁ବିଜେପିକୁ\t-6897\n▁ସହାୟତାରେ\t-6898\n▁କାର୍ଯ୍ୟରତ\t-6899\n▁ପାଇପାରିବେ\t-6900\n▁ଭବିଷ୍ୟତରେ\t-6901\n▁ସର୍ବନିମ୍ନ\t-6902\n▁ଯେଉଁମାନଙ୍କର\t-6903\nଜୋ\t-6904\nଠେ\t-6905\n▁+\t-6906\n▁ଜର\t-6907\n▁୬୫\t-6908\nସିନ୍\t-6909\n▁ଏକୁ\t-6910\n▁ପାଶ\t-6911\n▁ଫର୍\t-6912\n▁୭ଟି\t-6913\nଙ୍ଗାଲ\t-6914\nଧାନାଥ\t-6915\n▁କାମୁ\t-6916\n▁ଘଟିବ\t-6917\n▁ଚିତ୍\t-6918\n▁ମାନ୍\t-6919\n▁ସହିଦ\t-6920\n▁ଅକ୍ସି\t-6921\n▁ଡିଜାଇ\t-6922\n▁ଯିବାର\t-6923\nବାନୁମାନ\t-6924\n▁ଅଲିମ୍ପ\t-6925\n▁ଜଣାଇବା\t-6926\n▁ଦାବିରେ\t-6927\n▁ବଙ୍ଗୋପ\t-6928\n▁ବୁଝିବା\t-6929\n▁ଲକଡାଉନ\t-6930\n▁ଅନ୍ଧକାର\t-6931\n▁ଜଙ୍ଗଲରେ\t-6932\n▁ନିମିତ୍ତ\t-6933\n▁ପ୍ରଚଳିତ\t-6934\n▁ବିସ୍ତୃତ\t-6935\n▁ସାହୁଙ୍କ\t-6936\n▁ସେକେଣ୍ଡ\t-6937\n▁ଲାଇସେନ୍ସ\t-6938\n▁ସମ୍ପର୍କିତ\t-6939\nୀଶ\t-6940\nଟିଫ\t-6941\nକଣ୍ଠ\t-6942\nପ୍ରୀ\t-6943\n▁ମୀନ\t-6944\n▁ରେଲ\t-6945\nଜ୍ଜିତ\t-6946\nବଶ୍ୟକ\t-6947\n▁କୁହୁ\t-6948\n▁ନଦୀର\t-6949\n▁ଯୋଗୀ\t-6950\nବ୍ୟକ୍ତ\t-6951\n▁କଟକରେ\t-6952\n▁ତୀର୍ଥ\t-6953\n▁ତୁଳସୀ\t-6954\n▁ପୂଜାର\t-6955\n▁ଭାଗବତ\t-6956\n▁ମାନ୍ୟ\t-6957\n▁ମିଥୁନ\t-6958\n▁ଅମିତାଭ\t-6959\n▁ଯୋଗଦାନ\t-6960\n▁ଲିମିଟେ\t-6961\n▁ସିସିଟି\t-6962\n▁କହୁଥିଲେ\t-6963\n▁କୋଲକାତା\t-6964\n▁ମାଓବାଦୀ\t-6965\n▁ଅଧ୍ୟକ୍ଷା\t-6966\n▁ସିଦ୍ଧାନ୍ତ\t-6967\n▁କରିଦେଇଥିଲେ\t-6968\n▁ନିର୍ଭରଯୋଗ୍ୟ\t-6969\nଚତ\t-6970\nମାଦ\t-6971\n▁ଡର\t-6972\n▁ପଲ\t-6973\n▁୫୮\t-6974\n▁୭୮\t-6975\nଦେବେ\t-6976\nବାପା\t-6977\nସଙ୍କ\t-6978\n▁ଆଡ଼\t-6979\n▁ଠାବ\t-6980\n▁ପାସ\t-6981\n▁ବୋଈ\t-6982\n▁ସ୍ଫ\t-6983\nଙ୍ଗାର\t-6984\n▁ଆବାସ\t-6985\n▁ଗୋପନ\t-6986\n▁ଡ୍ରେ\t-6987\n▁ବିଗତ\t-6988\n▁ସଦୃଶ\t-6989\n▁ସମିତ\t-6990\n▁ସୂଚନ\t-6991\n▁ସେଥ୍\t-6992\n▁ଉନ୍ମୋ\t-6993\n▁ପାଉଛି\t-6994\n▁ଶିଶୁର\t-6995\n▁ସେଭଳି\t-6996\n▁କେଜ୍ରି\t-6997\n▁ଚାହିଦା\t-6998\n▁ମାରିବା\t-6999\n▁ୱିକେଟ୍\t-7000\n▁ଆଫ୍ରିକା\t-7001\n▁ଆରମ୍ଭରୁ\t-7002\n▁ପ୍ରଶାସନର\t-7003\n▁ଦେଇଥାନ୍ତି\t-7004\n▁ସ୍ବାଭାବିକ\t-7005\n▁ଆଧ୍ୟାତ୍ମିକ\t-7006\n▁ଗଣମାଧ୍ୟମରେ\t-7007\n▁ପ୍ରତିବନ୍ଧକ\t-7008\nଡକ\t-7009\nଜନ୍\t-7010\nୟୋଗ\t-7011\n▁wh\t-7012\nଠିରେ\t-7013\nପ୍ରଥ\t-7014\nସାହି\t-7015\nହେଲା\t-7016\n▁ଅଚଳ\t-7017\n▁କପ୍\t-7018\n▁ଚମକ\t-7019\nପୁତ୍ର\t-7020\nଶୂନ୍ୟ\t-7021\n▁କ୍ଷଣ\t-7022\n▁ଖ୍ୟା\t-7023\n▁ଦିଶୁ\t-7024\n▁ବାରୁ\t-7025\n▁ଯୀଶୁ\t-7026\n▁ସଚ୍ଚ\t-7027\nଞ୍ଚଳରେ\t-7028\n▁ପୁଞ୍ଜି\t-7029\n▁ମାଟିରେ\t-7030\n▁ଆନନ୍ଦରେ\t-7031\n▁ପାଆନ୍ତି\t-7032\n▁ସମୃଦ୍ଧି\t-7033\n▁ଚଳିତବର୍ଷ\t-7034\n▁ଚାଷୀଙ୍କୁ\t-7035\n▁ପରିଦର୍ଶନ\t-7036\n▁ଷ୍ଟେସନରେ\t-7037\n▁ହରାଇଛନ୍ତି\t-7038\n▁ଜଗନ୍ନାଥଙ୍କ\t-7039\n▁ପିଲାମାନଙ୍କୁ\t-7040\nak\t-7041\nଂଘନ\t-7042\nଟକା\t-7043\nଞ୍ଜା\t-7044\nଯାଏଁ\t-7045\n▁କୋଷ\t-7046\n▁ଖୋଲ\t-7047\n▁ଦହି\t-7048\n▁ଭୋକ\t-7049\n▁ଭୟଭ\t-7050\n▁ରାଗ\t-7051\nମିକ୍ର\t-7052\nଳକଣ୍ଠ\t-7053\n▁ଆଶ୍ୱ\t-7054\n▁କ୍ଷମ\t-7055\n▁ଘେନି\t-7056\n▁ଚଢ଼ି\t-7057\n▁ଚମ୍ପ\t-7058\n▁ଭୂଷଣ\t-7059\n▁ଆଙ୍ଗୁ\t-7060\n▁ଏକତ୍ର\t-7061\n▁ଅନୁକୂଳ\t-7062\n▁କଳିଙ୍ଗ\t-7063\n▁ଚ୍ୟାନେ\t-7064\n▁ଫିଙ୍ଗି\t-7065\n▁ଭାସ୍କର\t-7066\n▁ଆଜିକାଲି\t-7067\n▁ଉଦ୍ଘାଟନ\t-7068\n▁ସ୍ଥାପିତ\t-7069\nଷ୍ଟାଗ୍ରାମ\t-7070\n▁ଅଭିନନ୍ଦନ\t-7071\n▁କ୍ୟାମେରା\t-7072\n▁ସିସିଟିଭି\t-7073\n▁ଉପସ୍ଥିତିରେ\t-7074\n▁କରାଯାଇପାରେ\t-7075\n▁ନିଆଯାଇଥିଲା\t-7076\n▁ପରିବର୍ତ୍ତେ\t-7077\nବାୟ\t-7078\nଭ୍ର\t-7079\nର୍ଜ\t-7080\nଳୀର\t-7081\n▁ଫଟ\t-7082\n▁୯୫\t-7083\nଲେଖା\t-7084\n▁ଟିଏ\t-7085\n▁ହାଟ\t-7086\n▁୧୫୦\t-7087\nଟଫର୍ମ\t-7088\nାସେବୀ\t-7089\n▁ଚର୍ମ\t-7090\n▁ଡୋଜ୍\t-7091\n▁ତେଲେ\t-7092\n▁ନାମା\t-7093\n▁ବାଇକ\t-7094\n▁ମୂଳକ\t-7095\n▁ଯୌବନ\t-7096\n▁ଶସ୍ୟ\t-7097\n▁ସିଲ୍\t-7098\nଗୁଡିକର\t-7099\n▁ଅଫିସ୍\t-7100\n▁କରିଲେ\t-7101\n▁ଜାମିନ\t-7102\n▁ଜେଲରେ\t-7103\n▁ନଦୀରେ\t-7104\n▁ବାତାବ\t-7105\n▁କଶ୍ମୀର\t-7106\n▁ବଞ୍ଚିତ\t-7107\n▁ବିକଶିତ\t-7108\n▁ମଣିଷକୁ\t-7109\n▁ସୋନପୁର\t-7110\n▁ଅରବିନ୍ଦ\t-7111\n▁ନିର୍ଦ୍ଧ\t-7112\n▁ଯୋଗ୍ୟତା\t-7113\n▁ପ୍ରଦେଶରେ\t-7114\n▁ଆମେରିକାରେ\t-7115\n▁ପ୍ରାଧାନ୍ୟ\t-7116\n▁ସ୍ୱାଧୀନତା\t-7117\n▁ପୂର୍ବାନୁମାନ\t-7118\n▁ପ୍ରାର୍ଥୀଙ୍କ\t-7119\n▁ଆନ୍ଧ୍ରପ୍ରଦେଶ\t-7120\n▁ଶ୍ରଦ୍ଧାଞ୍ଜଳି\t-7121\n▁ଗୁରୁତ୍ବପୂର୍ଣ୍ଣ\t-7122\ntic\t-7123\nକୀୟ\t-7124\nଲିଂ\t-7125\nଳ୍କ\t-7126\n▁ପଟ\t-7127\n▁ଭୌ\t-7128\nନାରୁ\t-7129\nପଶ୍ଚ\t-7130\n▁ଏଲ୍\t-7131\n▁କୋଉ\t-7132\n▁ଗୁଲ\t-7133\n▁ଟନ୍\t-7134\n▁ଧୀର\t-7135\n▁ପାଳ\t-7136\n▁ସାଥ\t-7137\n▁୧୯୩\t-7138\nଯୁଦ୍ଧ\t-7139\nୋଗ୍ରା\t-7140\n▁କୋଠା\t-7141\n▁ଫ୍ରି\t-7142\n▁ବଲରେ\t-7143\n▁ନ୍ୟୁଜ\t-7144\n▁ପଶ୍ଚି\t-7145\n▁ପୀଡିତ\t-7146\n▁ସଙ୍ଗଠନ\t-7147\n▁ସହକାରୀ\t-7148\n▁ସୁପାରି\t-7149\n▁ଏହାଛଡ଼ା\t-7150\n▁ଡାକ୍ତରୀ\t-7151\n▁ଦିଆଯିବା\t-7152\n▁ବୁଝାମଣା\t-7153\n▁ସବୁକିଛି\t-7154\n▁ଏମାନଙ୍କର\t-7155\n▁ରାଉରକେଲା\t-7156\n▁କ୍ଷତିପୂରଣ\t-7157\n▁ଚ୍ୟାଲେଞ୍ଜ\t-7158\n▁ଝାରସୁଗୁଡ଼ା\t-7159\nab\t-7160\n▁7\t-7161\ners\t-7162\nବଲ୍\t-7163\n▁ଗପ\t-7164\nସ୍ରା\t-7165\n▁।\"'\t-7166\n▁ଉଷା\t-7167\n▁ବରଣ\t-7168\n▁ଉଡ଼ି\t-7169\n▁କ୍ରୟ\t-7170\n▁ଗାଇଡ\t-7171\n▁ପଢ଼ା\t-7172\n▁ପାଖେ\t-7173\n▁ଭିତର\t-7174\n▁ସତରେ\t-7175\n▁ସମୀର\t-7176\n▁ସାଥୀ\t-7177\n▁ଏହାରି\t-7178\n▁ଗାଧୋଇ\t-7179\n▁ଭାଗରେ\t-7180\n▁ଭାଷାର\t-7181\n▁ଆଧାରିତ\t-7182\n▁କ୍ରୀଡ଼\t-7183\n▁ଜମିଦାର\t-7184\n▁ପ୍ରେମର\t-7185\n▁ଭାଇରସ୍\t-7186\n▁ମୁର୍ମୁ\t-7187\n▁ଆସୁଥିଲେ\t-7188\n▁ପଚାରିଲେ\t-7189\n▁ମନୋରଞ୍ଜନ\t-7190\n▁ଉଲ୍ଲେଖନୀୟ\t-7191\n▁ଅଭିଯୁକ୍ତଙ୍କୁ\t-7192\n▁ଜିଲ୍ଲାପାଳଙ୍କ\t-7193\nଆଲ\t-7194\n▁!\"\t-7195\nଗାମୀ\t-7196\nଦାରେ\t-7197\nଧାରଣ\t-7198\nପିଟା\t-7199\nୀକୃତ\t-7200\n▁ଖାଲ\t-7201\n▁ସନ୍\t-7202\n▁ଗାଆଁ\t-7203\n▁ଗୁମ୍\t-7204\n▁ଚୋରା\t-7205\n▁ଦାରୁ\t-7206\n▁ଦିବା\t-7207\n▁ଦ୍ବା\t-7208\n▁ନିୟୁ\t-7209\n▁ବକ୍ସ\t-7210\n▁ହର୍ଷ\t-7211\n▁ହେଉଛ\t-7212\nିନଥିଲା\t-7213\n▁ଇଲେକ୍\t-7214\n▁ପେଜକୁ\t-7215\n▁ବସ୍ତି\t-7216\n▁ବୁଢ଼ୀ\t-7217\n▁ରିଲିଜ\t-7218\n▁ଲାଇଭ୍\t-7219\n▁ଅଙ୍ଗାର\t-7220\n▁ଅସ୍ଥିର\t-7221\n▁କର୍ଣ୍ଣ\t-7222\n▁ଚିହ୍ନି\t-7223\n▁ଜର୍ମାନ\t-7224\n▁ମଣ୍ଡଳୀ\t-7225\n▁ମୁହଁକୁ\t-7226\n▁ସ୍ଥଗିତ\t-7227\n▁କ୍ଷୟକ୍ଷ\t-7228\n▁ଛତ୍ରପୁର\t-7229\n▁ନିର୍ବାଚ\t-7230\n▁ବାତାବରଣ\t-7231\n▁ମହୋତ୍ସବ\t-7232\n▁ରୁହନ୍ତି\t-7233\n▁ସାଧାରଣରେ\t-7234\nଭଗ\t-7235\n▁8\t-7236\nଚେତ\t-7237\nପ୍ଟ\t-7238\nରୁପ\t-7239\n▁ଧୁ\t-7240\n▁ମୀ\t-7241\nାରୋହ\t-7242\n▁ଇଟା\t-7243\n▁ଗୋଡ\t-7244\n▁ବିଛ\t-7245\n▁ମଶା\t-7246\nଜଣଙ୍କ\t-7247\nପିଟାଲ\t-7248\n▁ଇନିଂ\t-7249\n▁କଦଳୀ\t-7250\n▁ଜଟିଳ\t-7251\n▁ପାଳି\t-7252\n▁ସ୍ରୋ\t-7253\nକିଙ୍ଗ୍\t-7254\nଦାନନ୍ଦ\t-7255\nଲେଶ୍ୱର\t-7256\nସୁନ୍ଦର\t-7257\nସ୍ତରରେ\t-7258\n▁କରିବୁ\t-7259\n▁କାଢ଼ି\t-7260\n▁ଦୁନିଆ\t-7261\n▁ରୁଷିଆ\t-7262\n▁ସ୍ୱୟଂ\t-7263\n▁ହୋଇଲେ\t-7264\n▁ଜାଗ୍ରତ\t-7265\n▁ବଙ୍ଗଳା\t-7266\n▁ରାଜସ୍ୱ\t-7267\n▁ଚିତ୍କାର\t-7268\n▁ବ୍ୟାଟ୍ସମ\t-7269\n▁ଅପରାହ୍ନରେ\t-7270\n▁ଫେରିଛନ୍ତି\t-7271\n▁ସ୍ୱାଭାବିକ\t-7272\nଅଛ\t-7273\nଉଜ\t-7274\nଙ୍ଘ\t-7275\nମୟୀ\t-7276\n▁ଅପଣ\t-7277\n▁ଅପ୍\t-7278\n▁ଉଠେ\t-7279\n▁କୀର\t-7280\n▁ମର୍\t-7281\n▁ରଶ୍\t-7282\nଦେଶରେ\t-7283\nମାଙ୍କ\t-7284\n▁ଆଲୁଅ\t-7285\n▁ବାରଣ\t-7286\n▁ବିନୋ\t-7287\n▁ଲମ୍ବ\t-7288\n▁ସ୍ୱେ\t-7289\n▁ଦୁଃଖର\t-7290\n▁ସେପରି\t-7291\n▁ଆରାଧନା\t-7292\n▁ମହାନଗର\t-7293\n▁ସମାବେଶ\t-7294\n▁ରାସ୍ତାର\t-7295\n▁ଶିକ୍ଷିତ\t-7296\n▁ସମ୍ବେଦନ\t-7297\n▁କାର୍ଯ୍ୟର\t-7298\n▁ଜିଲ୍ଲାକୁ\t-7299\nଭୁବନେଶ୍ବରଃ\t-7300\n▁କୁହାଯାଇଥିଲା\t-7301\nଅଁ\t-7302\n୪୫\t-7303\nବରା\t-7304\nଶତଃ\t-7305\nୋଦର\t-7306\n▁ମୈ\t-7307\nଡ୍ରେ\t-7308\nମଙ୍କ\t-7309\nସଭାର\t-7310\n▁ଅଘଟ\t-7311\n▁ଓଷା\t-7312\n▁ଧ୍ୱ\t-7313\n▁ନବା\t-7314\n▁ନ୍ୟ\t-7315\n▁ପହି\t-7316\n▁ହ୍ର\t-7317\nଆନ୍ତା\t-7318\nିକାରେ\t-7319\n▁ଲଢ଼ି\t-7320\nନାରାୟଣ\t-7321\n▁କଞ୍ଚା\t-7322\n▁ଭୟଭୀତ\t-7323\n▁ମହାଶୟ\t-7324\n▁ଶ୍ଳୋକ\t-7325\nବୀନ୍ଦ୍ର\t-7326\n▁ଅନ୍ତତଃ\t-7327\n▁ଉପାୟରେ\t-7328\n▁କାହିଁକ\t-7329\n▁ଜଳସେଚନ\t-7330\n▁ପ୍ରତିଶ\t-7331\n▁ମନୋନୀତ\t-7332\nଷ୍ଟିଚ୍ୟୁ\t-7333\n▁ଅଧିବେଶନ\t-7334\n▁ଅନୁଭୂତି\t-7335\n▁ପାଉଥିବା\t-7336\n▁ବିଶେଷକରି\t-7337\n▁ପ୍ଲାଟଫର୍ମ\t-7338\n▁କାର୍ଯ୍ୟକଳା\t-7339\n▁ଷ୍ଟାଡିୟମରେ\t-7340\nକଣ\t-7341\nଡେନ\t-7342\nପକୁ\t-7343\nପଡି\t-7344\nସୁର\t-7345\n▁ମଲ\t-7346\nଗଞ୍ଜ\t-7347\nବାଲି\t-7348\nମିଳି\t-7349\n▁ଅଲ୍\t-7350\n▁ଚଳି\t-7351\n▁ନଗଦ\t-7352\n▁ମୃଗ\t-7353\n▁ହିତ\t-7354\nଦୀଙ୍କ\t-7355\nବାହ୍ନ\t-7356\n▁ଏଡ଼େ\t-7357\n▁ଘୃଣା\t-7358\n▁ଫାଟି\t-7359\n▁ବ୍ଳକ\t-7360\n▁ଭତରେ\t-7361\n▁୨୦୧୫\t-7362\nସରକାରୀ\t-7363\n▁ଅଂଚଳର\t-7364\n▁ଉପୁଜି\t-7365\n▁କାହିକ\t-7366\n▁ଚାଇନା\t-7367\n▁ଡ୍ରଗ୍\t-7368\n▁ଦୃଷ୍ଟ\t-7369\n▁ବର୍ଗର\t-7370\n▁ମଣ୍ଡି\t-7371\n▁ସାଇବର\t-7372\n▁କେଜାଣି\t-7373\n▁ପୁରୀରେ\t-7374\n▁ମିଶ୍ରଣ\t-7375\n▁ସ୍ତରୀୟ\t-7376\n▁ସ୍ପେଶା\t-7377\n▁କବିତାରେ\t-7378\n▁କମ୍ପାନି\t-7379\n▁ଯୋଜନାକୁ\t-7380\n▁ସାର୍ଟିଫ\t-7381\n▁ଅନ୍ୟମାନେ\t-7382\n▁ପ୍ରେମିକା\t-7383\n▁ବାହାରିବା\t-7384\n▁ମୃତ୍ୟୁବରଣ\t-7385\noc\t-7386\nall\t-7387\nାନନ\t-7388\nୂଆଁ\t-7389\n୩ରେ\t-7390\n▁୫୨\t-7391\nଣ୍ଡୀ\t-7392\nଣ୍ଡୋ\t-7393\nଣ୍ଡ୍\t-7394\n▁The\t-7395\nଣ୍ଟିଆ\t-7396\nରଞ୍ଜନ\t-7397\n▁ଅବ୍ୟ\t-7398\n▁କାଳେ\t-7399\n▁ପୁଅର\t-7400\nପାରିଲେ\t-7401\nଭୋକ୍ତା\t-7402\n▁ପନ୍ଦର\t-7403\n▁ସାର୍ବ\t-7404\n▁ସ୍ମାର\t-7405\n▁କିଣିବା\t-7406\n▁ବର୍ଦ୍ଧ\t-7407\n▁ବାଣ୍ଟି\t-7408\n▁ସ୍ୱଚ୍ଛ\t-7409\n▁ତାହାଙ୍କ\t-7410\n▁ପ୍ରସାରଣ\t-7411\nଳାଖେମୁଣ୍ଡି\t-7412\n▁ସମ୍ପ୍ରଦାୟ\t-7413\n▁ହେଉଥିବାରୁ\t-7414\n▁ଜନସାଧାରଣଙ୍କ\t-7415\n▁ପ୍ରତିଯୋଗିତାରେ\t-7416\n▁=\t-7417\nଓଡି\t-7418\nଓ୍ବ\t-7419\nଛଡା\t-7420\nୃତା\t-7421\nୟାନ\t-7422\n▁ଆଣ\t-7423\n▁୬୬\t-7424\nଜଙ୍ଗ\t-7425\nଭକ୍ତ\t-7426\n▁ଅସୀ\t-7427\n▁ଜୁଆ\t-7428\n▁ପତା\t-7429\n▁ବୋତ\t-7430\n▁ରମା\t-7431\nଚନ୍ଦନ\t-7432\n▁ଆଗମନ\t-7433\n▁କିରଣ\t-7434\n▁ଜେଜେ\t-7435\n▁ରକ୍ଷ\t-7436\n▁ହାଡ଼\t-7437\n▁୨୦୦୦\t-7438\n▁କହିବୁ\t-7439\n▁ଠିକଣା\t-7440\n▁ବାଇକ୍\t-7441\n▁ମାସିକ\t-7442\nାନ୍ତରିତ\t-7443\n▁ଉଚ୍ଚତା\t-7444\nର୍ଯ୍ୟାଦା\t-7445\n▁ଜବରଦସ୍ତ\t-7446\n▁ନେଉଥିବା\t-7447\n▁ଆନ୍ତର୍ଜାତିକ\t-7448\nଡୀ\t-7449\nଆରି\t-7450\n▁।।\t-7451\n▁୭୩\t-7452\nଟେରା\t-7453\nନାଲ୍\t-7454\nର୍ପଣ\t-7455\nିକରି\t-7456\n▁ଗଛର\t-7457\nଦାବାଦ\t-7458\nବାଡ଼ି\t-7459\n▁ଆଇଟି\t-7460\n▁ଆଭ୍ୟ\t-7461\n▁କେଜି\t-7462\n▁ଗଣନା\t-7463\n▁ଫ୍ରେ\t-7464\n▁ରୀତି\t-7465\n▁ସହରା\t-7466\n▁ହଟାଇ\t-7467\nଥିଲାଗି\t-7468\n▁ଉଠିଲେ\t-7469\n▁ଭାଙ୍ଗ\t-7470\n▁ମହଲରେ\t-7471\n▁ମେଣ୍ଟ\t-7472\n▁ସମ୍ପଦ\t-7473\nବ୍ୟବହାର\t-7474\n▁ଅପୂର୍ବ\t-7475\n▁ଜିଲାରେ\t-7476\n▁ଦମ୍ପତି\t-7477\n▁ପଦ୍ଧତି\t-7478\n▁ଉତ୍ତୋଳନ\t-7479\n▁ଜେନେରାଲ\t-7480\n▁ପାଇବାରେ\t-7481\n▁ପ୍ରଶଂସକ\t-7482\n▁ସୂକ୍ଷ୍ମ\t-7483\n▁ଇତିହାସରେ\t-7484\n▁ଶ୍ରେଣୀରେ\t-7485\n▁ସ୍ଥିତିକୁ\t-7486\n▁ନିର୍ଯାତନା\t-7487\n▁ବଙ୍ଗୋପସାଗର\t-7488\nପଲ\t-7489\nare\t-7490\nବାଲ\t-7491\nଶାର\t-7492\nହରେ\t-7493\n୨ରେ\t-7494\n▁ଚଲ\t-7495\n▁ପୋକ\t-7496\n▁୮ଟି\t-7497\nଘଣ୍ଟା\t-7498\nପଲ୍ଲୀ\t-7499\n▁ଙ୍କର\t-7500\n▁ଚୁପ୍\t-7501\n▁ଛେଳି\t-7502\n▁ଜୋର୍\t-7503\n▁ହୋଇଥ\t-7504\n▁ଉପଭୋଗ\t-7505\n▁ଶ୍ରେୟ\t-7506\n▁ସାନ୍ତ\t-7507\n▁ସେଣ୍ଟ\t-7508\n▁ଆକୃଷ୍ଟ\t-7509\n▁\t-7510\nା\t-7511\n୍\t-7512\nର\t-7513\nି\t-7514\nକ\t-7515\nେ\t-7516\nବ\t-7517\nନ\t-7518\nତ\t-7519\nୁ\t-7520\nସ\t-7521\nମ\t-7522\nପ\t-7523\nହ\t-7524\nଲ\t-7525\nଦ\t-7526\nୟ\t-7527\n।\t-7528\nଇ\t-7529\nୋ\t-7530\nଗ\t-7531\nଥ\t-7532\nଣ\t-7533\nଜ\t-7534\nଟ\t-7535\nଏ\t-7536\nଶ\t-7537\nୀ\t-7538\nଆ\t-7539\nଭ\t-7540\nଯ\t-7541\nଙ\t-7542\nଳ\t-7543\nଅ\t-7544\nଷ\t-7545\nଛ\t-7546\nଧ\t-7547\nଡ\t-7548\nଚ\t-7549\n,\t-7550\nଉ\t-7551\nଖ\t-7552\nଁ\t-7553\nଂ\t-7554\n଼\t-7555\nଓ\t-7556\nୂ\t-7557\nଠ\t-7558\nୃ\t-7559\nଫ\t-7560\nଘ\t-7561\nୱ\t-7562\n୧\t-7563\nଞ\t-7564\n'\t-7565\n୨\t-7566\n-\t-7567\n୦\t-7568\nଢ\t-7569\n.\t-7570\nୌ\t-7571\n୩\t-7572\n୫\t-7573\n୪\t-7574\ne\t-7575\nଃ\t-7576\ni\t-7577\n)\t-7578\n(\t-7579\na\t-7580\nଝ\t-7581\n୯\t-7582\n\"\t-7583\nn\t-7584\nt\t-7585\n୬\t-7586\nୈ\t-7587\n୮\t-7588\n୭\t-7589\no\t-7590\n?\t-7591\nr\t-7592\ns\t-7593\n:\t-7594\nl\t-7595\nଈ\t-7596\nd\t-7597\n!\t-7598\nh\t-7599\nm\t-7600\nc\t-7601\nu\t-7602\ng\t-7603\n;\t-7604\ny\t-7605\n|\t-7606\n/\t-7607\nb\t-7608\np\t-7609\n1\t-7610\nf\t-7611\n2\t-7612\nଋ\t-7613\n0\t-7614\n\\\t-7615\nw\t-7616\nA\t-7617\nI\t-7618\nD\t-7619\nS\t-7620\nP\t-7621\nC\t-7622\nk\t-7623\nv\t-7624\nR\t-7625\nO\t-7626\nଔ\t-7627\nT\t-7628\nN\t-7629\nz\t-7630\n3\t-7631\nM\t-7632\nj\t-7633\n~\t-7634\nB\t-7635\n5\t-7636\n9\t-7637\n4\t-7638\nଊ\t-7639\nQ\t-7640\nଐ\t-7641\n@\t-7642\n8\t-7643\nH\t-7644\n%\t-7645\nL\t-7646\nE\t-7647\nG\t-7648\n6\t-7649\nF\t-7650\n7\t-7651\n॥\t-7652\n•\t-7653\nx\t-7654\n°\t-7655\n*\t-7656\n·\t-7657\nW\t-7658\n+\t-7659\n[\t-7660\n]\t-7661\nK\t-7662\nq\t-7663\nJ\t-7664\nV\t-7665\nଽ\t-7666\nX\t-7667\nU\t-7668\n=\t-7669\n୘\t-7670\n>\t-7671\nô\t-7672\nा\t-7673\nY\t-7674\n&\t-7675\n#\t-7676\nे\t-7677\nक\t-7678\nर\t-7679\n्\t-7680\nस\t-7681\n`\t-7682\n©\t-7683\nZ\t-7684\n_\t-7685\nं\t-7686\n{\t-7687\nन\t-7688\n}\t-7689\nि\t-7690\nल\t-7691\nह\t-7692\nà\t-7693\nు\t-7694\n↑\t-7695\nप\t-7696\nय\t-7697\nी\t-7698\n<\t-7699\nत\t-7700\nम\t-7701\nो\t-7702\nè\t-7703\n×\t-7704\nā\t-7705\n️\t-7706\nब\t-7707\n®\t-7708\n»\t-7709\nद\t-7710\nग\t-7711\nత\t-7712\nÂ\t-7713\nୠ\t-7714\n˚\t-7715\nव\t-7716\nश\t-7717\n़\t-7718\nै\t-7719\n➡\t-7720\nâ\t-7721\nज\t-7722\nड\t-7723\nु\t-7724\n✡\t-7725\nర\t-7726\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\"checkpoint_step\": 1000, \"checkpoint_sha256\": \"5c1820477cbd9698eecdbb76f9d57484814394f053d5b709a6bd3d8be3713f87\", \"prompt\": \"ଆଜି ସକାଳେ\", \"mode\": \"sample\", \"temperature\": 0.8, \"top_k\": 50, \"top_p\": 0.9, \"seed\": 2027, \"prompt_token_count\": 3, \"generated_token_count\": 28, \"ended_on_eos\": true, \"completion\": \" ଏ ସମ୍ପର୍କରେ ଆଜି ଆଜି ଏକ ଏକ ଅମୁମାଲ୍କୁ ନେଇ ଆଲୋଚନା ହେବା ସହ ଏବେ ତାଙ୍କ ପତ୍ନୀଙ୍କ ଭିତରେ ବିବିୟାନ ଅଭିଯୋଗ ହୋଇଥିଲା ।\"}\n{\"experiment_id\": \"nano-8k-v1\", \"checkpoint_step\": 1000, \"checkpoint_sha256\": \"5c1820477cbd9698eecdbb76f9d57484814394f053d5b709a6bd3d8be3713f87\", \"prompt\": \"ଓଡ଼ିଆ ଭାଷାର\", \"mode\": \"greedy\", \"temperature\": 0.0, \"top_k\": null, \"top_p\": null, \"seed\": 2028, \"prompt_token_count\": 3, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" 'ଅନ୍ୟବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ'ର 'ଅବ\"}\n{\"experiment_id\": \"nano-8k-v1\", \"checkpoint_step\": 1000, \"checkpoint_sha256\": \"5c1820477cbd9698eecdbb76f9d57484814394f053d5b709a6bd3d8be3713f87\", \"prompt\": \"ଓଡ଼ିଆ ଭାଷାର\", \"mode\": \"sample\", \"temperature\": 0.8, \"top_k\": 50, \"top_p\": 0.9, \"seed\": 2028, \"prompt_token_count\": 3, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ଜୀବନ ପ୍ରତିଥା'ର ଏକ ବଡ କଥା ।\\n\\\"ଅମ ତକାନ୍ଦଙ୍କର ସୁହୁର ଓ ସୁପଣ୍ଡ-ବି-ମାଉଙ୍କ ପ୍ରତି ପ୍ରତିଟି\\nବିବାଦବାଦ - - \\\"ମଃ\\\"\\nପ୍ରଧାନ - \\\"ତୋହି, 'ମନ୍ଦ, 'କବି', 'ଏ'\"}\n", "generations/nano-8k-v1-random-baseline.jsonl": "{\"model_state\": \"random_initialization\", \"seed\": 1234, \"prompt\": \"ଓଡ଼ିଶାର ଲୋକମାନେ\", \"mode\": \"greedy\", \"generated_token_count\": 64, \"completion\": \" ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍ ଆପ୍\"}\n{\"model_state\": \"random_initialization\", \"seed\": 1234, \"prompt\": \"ଓଡ଼ିଶାର ଲୋକମାନେ\", \"mode\": \"sample\", \"generated_token_count\": 64, \"completion\": \" ଅନ୍ଧାର ପର୍ବତ ବ୍ୟବସ ସୁଭଦାନୀିତ୍ରୀିତ୍ରୀତ୍ମିକ ସୋନପୁର ଭାଇରସ୍ାର୍ଗେ ବିଦ୍ୟୁତପକ ବଜ୍ର ଦ୍ଲିକହକ ହୋଇଛନ୍ତିସ୍କର ଡାଇ ଉଦ ଉଦଢ଼େଢ଼େଢ଼େ ଦୁର୍ବଳ ବୁଦ୍ଧି ଅର୍ଜନ ତାହାହେଲେ ଶଙ୍କାç ତାହାହେଲେଧିକାରୀମନ୍ତେିବା ମୁଣ୍ଡ ମୁଣ୍ଡ ସଡ଼କ ସଡ଼କ ମୁକ୍ତ ମୁକ୍ତିବା ମୁଣ୍ଡitizedଶ୍ଚର୍ଯ୍ୟill ପରିବ କାଲଥୁଲିଥୁଲିଜୁନିର୍ ଉପୁ କାଲକ୍ର ବୁଦ୍ଧହକହକ�ସାମିକା ନେତା ମୁକ୍ତ ମୁକ୍ତ\"}\n{\"model_state\": \"random_initialization\", \"seed\": 1234, \"prompt\": \"ଆଜି ସକାଳେ\", \"mode\": \"greedy\", \"generated_token_count\": 64, \"completion\": \" ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ ସକାଳେ\"}\n{\"model_state\": \"random_initialization\", \"seed\": 1234, \"prompt\": \"ଆଜି ସକାଳେ\", \"mode\": \"sample\", \"generated_token_count\": 64, \"completion\": \" ସୁସ୍ଥ ଅଖି ମହୁ ବୁଦ୍ଧ ଅର୍ = ମହଙ୍ଗା ମହଙ୍ଗା ଘରୁ ଜା ଜା ଜା ଜା ପରିବର୍ତ୍ତନ ସ୍ଥଗ ସ୍ଥଗ ଅଧିକାରୀମାନେ କାନ୍ଧଂସା ବସିଛନ୍ତି ଗଛର୘୘୍ୟର� କାନ୍ଧ ବାଧ ଧାଠାରୁ��ପୁ ଦେଉଥିଲେ କଳଶ୍ରୀଶ୍ରୀ ଆଫ୍ର ଆଫ୍ରPମଧ୍ୟରେପୁ ସେହଶିଳ୍ପ କଳ ପ୍ରତୀ କୋଠା ପ୍ରହ ପୂର୍ ପୂର୍ ସିନେ ଶତପଥ ସିସିଟିଭି ପଞ୍ଚାୟତରठठ ସମ୍ମିଣର ନିର୍ବାଚିତନାଥସିବିଯାଇଥିଲା� ଶିବପାରିବେ\"}\n{\"model_state\": \"random_initialization\", \"seed\": 1234, \"prompt\": \"ଓଡ଼ିଆ ଭାଷାର\", \"mode\": \"greedy\", \"generated_token_count\": 64, \"completion\": \" ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର ଭାଷାର\"}\n{\"model_state\": \"random_initialization\", \"seed\": 1234, \"prompt\": \"ଓଡ଼ିଆ ଭାଷାର\", \"mode\": \"sample\", \"generated_token_count\": 64, \"completion\": \" ମୁକାବି କରୁଛିଯାଉଛିଯାଉଛିŠ ନଗର ଚାଲିଥିବା ନଗର ନଗର ଅଜୟ ପ୍ରଧାନମନ୍ତ୍ରୀ ପ୍ରଧାନମନ୍ତ୍ରୀରୂପେ ସାହିତ୍ୟ ସାହିତ୍ୟନ୍ଦିର କର୍ମଚାରୀମାନେ କରିବ ପାଲ ଜ୍ୟ 19ପକ୍ଷେ ଦଶ ଆସିବା ସହା ସହା ଠିଆ ଦଶ ଦଶ ପଦ୍ମ ଘୃ ଭେଟିକର୍ମୀ ରହସ୍ୟ ଆରମ୍ଭ ଯେତିକି ସମ୍ପତ୍ତି Odନ୍ରେ ଦୁଇଟି ଦୁଇଟିब。 ସେଠାରେठ ସମେତଛନ୍ତିେଇବା ଥାନ୍ତି ରହସ୍ୟ ରହସ୍ୟସରେଭୁବନେଶ୍ୱରଭୁବନେଶ୍ୱର ବୋଲର ସୁପାରି ସୁପାରିସରୁ ଆରାଧନାଘରେ ଅର୍ପଣ ସମାଲୋଚନାକ୍ରୟାନ\"}\n", "generations/pilot-30m-v1-step1800.jsonl": "{\"experiment_id\": \"pilot-30m-v1\", \"checkpoint_step\": 1800, \"checkpoint_sha256\": \"474ecb811a6763b3ad6a455bad14d29b2c522f5e1c33cd128f35e5284c64309d\", \"prompt\": \"ଓଡ଼ିଶାର ଲୋକମାନେ\", \"mode\": \"greedy\", \"temperature\": 0.0, \"top_k\": null, \"top_p\": null, \"seed\": 2026, \"prompt_token_count\": 4, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ମଧ୍ୟ ଏହି ସମୟରେ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ ନିଜ\"}\n{\"experiment_id\": \"pilot-30m-v1\", \"checkpoint_step\": 1800, \"checkpoint_sha256\": \"474ecb811a6763b3ad6a455bad14d29b2c522f5e1c33cd128f35e5284c64309d\", \"prompt\": \"ଓଡ଼ିଶାର ଲୋକମାନେ\", \"mode\": \"sample\", \"temperature\": 0.8, \"top_k\": 50, \"top_p\": 0.9, \"seed\": 2026, \"prompt_token_count\": 4, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ବିରିୟାରେ ସାନ୍ଦ୍ରାର ଅଭାବକୁ ନେଇ ନିଜକୁ ନିଜେ ଅନେକ କଥା କହୁଥିବା କଥା । ଆମେ ସମସ୍ତେ ଜାଣନ୍ତି ଯେ ଏହି ମହାଭାରତ ତଥା ଦେଶର ବିଭିନ୍ନ ରାଜ୍ୟରୁ ଆସୁଥିବା ଓଡ଼ିଆଙ୍କ ପାଇଁ ଏକ ପ୍ରକାର ପ୍ରୟାସ । ଏହି ସମୟରେ ଆମେ ସମସ୍ତେ ସମସ୍ତେ ଏହି ଅବସରରେ ରହି ଏହିସବୁ କାର୍ଯ୍ୟକ୍ରମ ବିଷୟରେ କହିବାକୁ ଯାଉଛୁ । ଆମେ ମଧ୍ୟ ଏହି ସଭାରେ ଅଂଶଗ୍ରହଣ କରିବା ପାଇଁ ଏକ ନୂଆ ପ୍ରସ୍ତୁତି\"}\n{\"experiment_id\": \"pilot-30m-v1\", \"checkpoint_step\": 1800, \"checkpoint_sha256\": \"474ecb811a6763b3ad6a455bad14d29b2c522f5e1c33cd128f35e5284c64309d\", \"prompt\": \"ଆଜି ସକାଳେ\", \"mode\": \"greedy\", \"temperature\": 0.0, \"top_k\": null, \"top_p\": null, \"seed\": 2027, \"prompt_token_count\": 3, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ଭୁବନେଶ୍ୱରର ଏକ ଘରୋଇ ହସ୍ପିଟାଲରେ ଭର୍ତ୍ତି ହୋଇଛନ୍ତି । ସେ ଜଣେ ଡାକ୍ତରଙ୍କୁ ଚିକିତ୍ସା ପାଇଁ ଚିକିତ୍ସା ପାଇଁ ଯାଇଥିଲେ । ସେ ତାଙ୍କ ଚିକିତ୍ସା ପାଇଁ ଚିକିତ୍ସା ପାଇଁ ଯାଇଥିଲେ ।\\nସେହିପରି ସେ ତାଙ୍କ ସ୍ୱାସ୍ଥ୍ୟବସ୍ଥାରେ ମଧ୍ୟ ଚିକିତ୍ସାଧୀନ ଥିଲେ । ସେ ତାଙ୍କ ସ୍ୱାସ୍ଥ୍ୟବସ୍ଥାରେ ମଧ୍ୟ ତାଙ୍କର ଚିକିତ୍ସା କରାଯାଇଥିଲା । ସେ ତାଙ୍କ ସ୍ୱାସ୍ଥ୍ୟବସ୍ଥାରେ ମଧ୍ୟ ଥିଲେ । ସେ ତାଙ୍କ ସ୍ୱାସ୍ଥ୍ୟବସ୍ଥାରେ ମଧ୍ୟ\"}\n{\"experiment_id\": \"pilot-30m-v1\", \"checkpoint_step\": 1800, \"checkpoint_sha256\": \"474ecb811a6763b3ad6a455bad14d29b2c522f5e1c33cd128f35e5284c64309d\", \"prompt\": \"ଆଜି ସକାଳେ\", \"mode\": \"sample\", \"temperature\": 0.8, \"top_k\": 50, \"top_p\": 0.9, \"seed\": 2027, \"prompt_token_count\": 3, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ସକାଳେ ଦୁଇ ଝିଅ ସହ ଆସି ଶୋଇପଡ଼ିଲା । ସେହି ସମୟରେ ବାପା ଘର ଲୋକ କୁ ଖବର ଦେଇ କହିଥିଲେ ଯେ, \\\"ତେବେ ମାଆ, ତମେ ଆଉ କିଛି କହିଦେବୁ । ମୁଁ ଏ କଥା କିଛି ଶୁଣିନାହିଁ । କିନ୍ତୁ ବାପା, ମା'ଙ୍କୁ ନେଇ ମା'ର ମା'ର ପୁଅ ଝିଅ । ଏ କଥା କହି ସେ ଆଉ କାହା କଥା ଜାଣିନଥିଲା ।\"}\n{\"experiment_id\": \"pilot-30m-v1\", \"checkpoint_step\": 1800, \"checkpoint_sha256\": \"474ecb811a6763b3ad6a455bad14d29b2c522f5e1c33cd128f35e5284c64309d\", \"prompt\": \"ଓଡ଼ିଆ ଭାଷାର\", \"mode\": \"greedy\", \"temperature\": 0.0, \"top_k\": null, \"top_p\": null, \"seed\": 2028, \"prompt_token_count\": 3, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ଏକ ବଡ଼ ଧରଣର ଘଟଣା ।\\n'ଆମ ଦେଶ'ର ଏକ ବଡ଼ ବଡ଼ ବଡ଼ ବଡ଼ ବଡ଼ ବଡ଼ ।\\n'ଆମ ଦେଶ'ର ଏକ ବଡ଼ ବଡ଼ ଦେଶ ।\\n'ଆମ ଦେଶ'ର ଏକ ଦେଶ ।\\n'ଆମ ଦେଶ'ର ଏକ ଦେଶ ।\\n'ଆମ ଦେଶ'ର ଏକ ଦେଶ ।\\n'\"}\n{\"experiment_id\": \"pilot-30m-v1\", \"checkpoint_step\": 1800, \"checkpoint_sha256\": \"474ecb811a6763b3ad6a455bad14d29b2c522f5e1c33cd128f35e5284c64309d\", \"prompt\": \"ଓଡ଼ିଆ ଭାଷାର\", \"mode\": \"sample\", \"temperature\": 0.8, \"top_k\": 50, \"top_p\": 0.9, \"seed\": 2028, \"prompt_token_count\": 3, \"generated_token_count\": 64, \"ended_on_eos\": false, \"completion\": \" ଅନେକ ଦୃଶ୍ୟ, ଚିତ୍ର-ଅର୍ଣ୍ଣିମ, ନୂଆବୋହୂକ ଓ ଲୋକନାଥଙ୍କ ଭଳି କିଛି ନୂଆ କାହାଣୀକୁ ନେଇ ଅନେକ ପ୍ରଶ୍ନବାଚୀ ସୃଷ୍ଟି ହୋଇଛି । ତେଣୁ ଏହି ଦୁଇ ଚରିତ୍ରର କଥା ଜାଣିଲେ ଆଶ୍ଚର୍ଯ୍ୟ ହୋଇଯିବେ ।\\nପ୍ରଧାନମନ୍ତ୍ରୀଙ୍କ ଚିଠିରେ ପ୍ରକାଶ ପାଇବା ପରେ ଅନେକ ଘଟଣା ସାମ୍ନାକୁ ଆସିଛି । ଯେଉଁଥିରେ ଲେଖାଥିବା ଏହି ଘଟଣା\"}\n", "reports/environment_report.md": "# Environment report\n\nBased on the user's complete CELL 00 output.\n\n| Item | Observed value |\n|---|---|\n| Python | 3.13.15 |\n| PyTorch | 2.11.0+cu128 |\n| CUDA available | True |\n| PyTorch CUDA version | 12.8 |\n| GPU | Tesla T4 |\n| GPU VRAM | 14.56 GiB |\n| RAM total | 12.67 GiB |\n| RAM available at inspection | 11.17 GiB |\n| `/content` disk | 65.24 GiB free / 112.64 GiB total |\n| Drive mount disk view | 61.98 GiB free / 112.64 GiB total |\n\nGoogle Drive mounted at `/content/drive`. The project root is writable.\nCELL 01 confirmed `/content/odiagpt_runtime` is writable.\n\nThe Drive mount disk view is a filesystem reading, not a verified Google Drive\naccount quota.\n", "reports/data_quality_report.md": "# Initial Odia data quality report\n\nThis report covers a small prefix of the verified Odia stream. It is not a representative random sample.\n\n- Dataset: `ai4bharat/sangraha`, configuration `verified`, split `ori`\n- Fixed dataset revision: `8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8`\n- Documents inspected: 893\n- Documents retained: 886\n- Documents rejected: 7\n- Exact duplicate texts rejected: 0\n- Duplicate document IDs rejected: 0\n- Raw documents already NFC: 893\n- Cleaned characters: 1,500,127\n- Cleaned length mean / median: 1693.1 / 1089.0\n- Odia letter/mark ratio mean / median: 0.9925 / 1.0000\n- Opening-line prefixes seen in at least three documents: 0 (detection only)\n- Train / validation documents: 877 / 9\n\n## Source types among inspected documents\n\n- `pdf`: 126\n- `web`: 767\n\n## Rejection reasons\n\n- `few_odia_characters`: 2\n- `too_long_for_smoke`: 5\n\n## Short raw / clean comparisons\n\nOnly the first 70 characters of each version are shown.\n\n| Example | Raw prefix | Clean prefix |\n|---|---|---|\n| 1 | କନକ ବ୍ୟୁରୋ : ଅନୁଗୁଳ ହଣ୍ଡପା ଷ୍ଟେସନ ନିକଟରେ ଆଜମେର-ପୁରୀ ଏକ୍ସପ୍ରେସ୍ ଇଞ୍ଜିନ୍ | କନକ ବ୍ୟୁରୋ : ଅନୁଗୁଳ ହଣ୍ଡପା ଷ୍ଟେସନ ନିକଟରେ ଆଜମେର-ପୁରୀ ଏକ୍ସପ୍ରେସ୍ ଇଞ୍ଜିନ୍ |\n| 2 | ବାରିପଦାଃ କୂଅ ଭିତରେ ଖସି ପଡ଼ିଲା ଛୁଆ ହାତୀ । ମୟୂରଭଞ୍ଜ ଜିଲ୍ଲା ରାସଗୋବିନ୍ଦପୁର | ବାରିପଦାଃ କୂଅ ଭିତରେ ଖସି ପଡ଼ିଲା ଛୁଆ ହାତୀ । ମୟୂରଭଞ୍ଜ ଜିଲ୍ଲା ରାସଗୋବିନ୍ଦପୁର |\n| 3 | ମୁମ୍ବାଇ ୦୮। ୦୭ଃ ବଲିଉଡର ଭେଟେରାନ ଅଭିନେତ୍ରୀ ନୀତୁ କପୁର ଆଜି ତାଙ୍କର ୬୪ ତମ ଜନ | ମୁମ୍ବାଇ ୦୮। ୦୭ଃ ବଲିଉଡର ଭେଟେରାନ ଅଭିନେତ୍ରୀ ନୀତୁ କପୁର ଆଜି ତାଙ୍କର ୬୪ ତମ ଜନ |\n\n## Unicode observations\n\nNFC normalization was applied. Odia combining marks, punctuation, digits, and code-mixed text were preserved by the cleaning rules. The initial 200-row inspection found no unwanted controls, but that does not establish their absence from the full dataset.\n\nDocument splitting occurred before token packing. Exact normalized text duplicates were removed before the split.\n", "reports/tokenizer_report.md": "# Smoke tokenizer report\n\nTokenizer: SentencePiece BPE trained from scratch on 877 smoke training documents.\nVocabulary: 4,096 pieces. Special IDs: pad=0, unk=1, bos=2, eos=3.\nThis is a provisional smoke tokenizer; the serious 8K tokenizer will use more data.\n\n## Synthetic cases\n\n| Case | Characters | Tokens | Exact round trip | Unknown tokens |\n|---|---:|---:|---|---:|\n| Odia | 26 | 9 | True | 0 |\n| punctuation and digits | 21 | 15 | True | 0 |\n| code-mixed | 34 | 26 | True | 0 |\n| URL | 30 | 28 | True | 0 |\n| rare characters | 15 | 19 | True | 0 |\n| multiline | 25 | 12 | True | 0 |\n| empty | 0 | 0 | True | 0 |\n\n## Saved-corpus sample\n\n- documents_measured: 109\n- tokens_measured: 57794\n- characters_measured: 185277\n- words_measured: 29643\n- unknown_token_rate: 0.0\n- byte_fallback_rate: 0.008582205765304357\n- tokens_per_character: 0.31193294364654006\n- tokens_per_word: 1.9496677124447592\n- mean_document_tokens_per_character: 0.282964792216537\n\nThe corpus metrics use the first 100 training documents and all available validation documents. They are diagnostics, not representative estimates.\n\nSentencePiece identity normalization and byte fallback were selected to preserve uncommon Unicode characters. Multiline behavior is reported above.\n", "reports/tiny_overfit_report.md": "# Tiny overfit report\n\n- Result: passed\n- Initial loss: 8.328094\n- Final loss: 0.431695\n- Final / initial: 0.0518\n- Steps: 24\n- Parameters: 950,912\n- Device: CPU\n- Fixed input length: 32 tokens\n- This verifies memorization of one sequence; it is not language-quality evidence.\n", "reports/nano_data_quality_report.md": "# Nano Odia data quality report\n\nThis is a prefix of the fixed verified/ori stream; it is not a random sample.\n- Dataset: ai4bharat/sangraha, verified/ori\n- Revision: 8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\n- Documents inspected: 12,297\n- Retained: 12,199\n- Rejected: 98\n- Cleaned characters: 20,004,188\n- Train / validation documents: 12,069 / 130\n- Length mean / median: 1639.8 / 1118.0\n- Odia ratio mean / median: 0.9906 / 1.0000\n- Raw NFC documents: 12,293\n\n## Rejection reasons\n\n- few_odia_characters: 24\n- too_long_for_nano: 74\n\n## Source types\n\n- pdf: 1,859\n- web: 10,438\n\nExact duplicate text removal and document-level splitting precede token packing.\nExisting smoke validation IDs remain in validation for this corpus.\n", "reports/nano_tokenizer_report.md": "# Nano 8K tokenizer report\n\n- Trained from scratch with SentencePiece BPE on nano training documents only.\n- Vocabulary: 8,192\n- Special IDs: pad=0, unk=1, bos=2, eos=3\n- Identity normalization, byte fallback, no dummy prefix.\n- Test suite: 4 tests passed.\n- Documents measured: 230 (first 100 train and all validation).\n- Tokens measured: 102,407.\n- Unknown token rate: 0.000000.\n- Byte fallback rate: 0.010322.\n- Tokens per character: 0.2596.\n- Tokens per word: 1.6466.\n- Document roundtrip failures: 0.\n", "reports/model_architecture.md": "# OdiaGPT model architecture\n\nRandomly initialized, decoder-only Transformer implemented with PyTorch primitives.\nToken embedding and output projection are tied. Each block uses pre-RMSNorm,\ncausal multi-head self-attention with RoPE, and a SwiGLU feedforward network.\nAttention uses explicit masked softmax for initial correctness testing.\n\n| Config | Layers | Width | Heads | Context | Vocabulary | Parameters |\n|---|---:|---:|---:|---:|---:|---:|\n| smoke | 2 | 128 | 4 | 128 | 4096 | 950,912 |\n| nano | 4 | 256 | 4 | 256 | 8192 | 5,507,328 |\n| tiny | 6 | 384 | 6 | 384 | 4096 | 12,194,688 |\n| odia30m | 8 | 512 | 8 | 512 | 8192 | 31,465,984 |\n\nThe smoke, nano, and tiny configurations use the provisional 4K\ntokenizer. The odia30m configuration requires a separately trained 8K tokenizer\nand a larger validated corpus before training.\n", "reports/nano_experiment.md": "# Nano experiment\n\n- GPU: Tesla T4\n- Parameters: 5,507,328\n- Precision: fp16\n- Steps: 1,000\n- Tokens seen: 8,192,000\n- Final train loss: 5.7545\n- Final validation loss: 5.9224\n- Validation perplexity: 373.30\n- Peak allocated VRAM: 0.526 GiB\n- Final checkpoint: /content/drive/MyDrive/OdiaGPT/checkpoints/nano-8k-v1/step_0001000.pt\n", "reports/generation_test.md": "# Nano generation test\n\n- Checkpoint: nano-8k-v1 step 1000.\n- Prompts: three fixed Odia prefixes, each with greedy and temperature 0.8 / top-k 50 / top-p 0.9 sampling.\n- Generated continuations: six; random-init comparisons: six.\n- Trained outputs contain recognizable Odia words and occasional sentence patterns.\n- Greedy outputs show severe repetition on two prompts.\n- Sampled outputs are often fragmented, with weak grammar and occasional malformed or code-mixed spans.\n- One greedy output resembles a generic news/Telegram boilerplate call to action.\n- These samples do not establish factual knowledge or reasoning.\n\n## Artifacts\n\n- generations/nano-8k-v1-step1000.jsonl\n- generations/nano-8k-v1-random-baseline.jsonl\n", "reports/checkpoint_resume_test.md": "# Checkpoint and resume test\n\n- Device: Tesla T4, FP16 GradScaler.\n- Trained two smoke-model steps, saved a hash-verified atomic checkpoint, reloaded model/optimizer/scheduler/scaler and random states, then trained step three.\n- Saved step/tokens: 2 / 64.\n- AdamW optimizer state step advanced from 2 to 3 after resume.\n- Maximum parameter difference from uninterrupted step three: 0.\n- Test checkpoint was stored in temporary runtime storage; production nano checkpoints remain on Drive.\n", "reports/gpu_memory_benchmark.md": "# OdiaGPT-30M T4 memory benchmark\n\n- Date: 2026-09-29 15:59:01 IST\n- GPU: Tesla T4\n- Compute capability: (7, 5)\n- Parameters: 31,465,984\n- Precision: FP16 autocast with GradScaler.\n- Workload: one random-token forward, backward, AdamW step at context 512.\n- No real dataset or language-quality result was measured.\n\n| Microbatch | Result | Peak allocated GiB | Peak reserved GiB | Step seconds |\n|---:|---|---:|---:|---:|\n| 1 | passed | 0.666 | 0.770 | 0.61 |\n| 2 | passed | 0.985 | 1.033 | 0.64 |\n| 4 | passed | 1.476 | 1.570 | 0.68 |\n| 8 | passed | 2.461 | 2.613 | 0.74 |\n\n## SDPA throughput\n\nValidated against manual attention in CELL 75.\nOne warmup update and three timed synthetic updates per microbatch.\n\n| Microbatch | Result | Peak allocated GiB | Tokens/sec | Seconds/update |\n|---:|---|---:|---:|---:|\n| 8 | passed | 1.944 | 34580 | 0.118 |\n| 16 | passed | 3.234 | 36916 | 0.222 |\n| 32 | passed | 5.959 | 37363 | 0.439 |\n", "reports/pilot_data_quality_report.md": "# Pilot Odia data quality report\n\nThis is a prefix of the fixed verified/ori stream; it is not a random sample.\n- Dataset: ai4bharat/sangraha, verified/ori\n- Revision: 8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\n- Documents inspected: 68,579\n- Retained: 67,983\n- Rejected: 596\n- Cleaned characters: 110,001,302\n- Train / validation documents: 67,330 / 653\n- Length mean / median: 1618.1 / 1103.0\n- Odia ratio mean / median: 0.9926 / 1.0000\n- Raw NFC documents: 68,553\n- Exact boilerplate lines removed: 9,005\n- Documents affected by boilerplate removal: 9,001\n- Boilerplate list SHA256: 91ba65e2f8c0bcfcb6552010cace261b1bef88d8fc3fd432dceddbe72e0d346d\n\n## Rejection reasons\n\n- duplicate_doc_id: 1\n- few_odia_characters: 159\n- too_long_for_pilot: 436\n\n## Source types\n\n- pdf: 10,184\n- speech: 3\n- web: 58,392\n\nExact duplicate text removal and document-level splitting precede token packing.\nRetained existing nano validation IDs remain in validation for this corpus.\n", "reports/pilot_report.md": "# Pilot experiment\n\n- GPU: Tesla T4\n- Parameters: 31,465,984\n- Precision: fp16\n- Steps: 1,800\n- Tokens seen: 29,491,200\n- Final train loss: 4.5156\n- Final validation loss: 4.6643\n- Validation perplexity: 106.09\n- Peak allocated VRAM: 3.529 GiB\n- Final checkpoint: /content/drive/MyDrive/OdiaGPT/checkpoints/pilot-30m-v1/step_0001800.pt\n", "reports/main_training_report.md": "# OdiaGPT-30M Serious Training Experiment Report\n\n- **Experiment ID**: `pilot-30m-v1` / `odia30m`\n- **Date**: 2026-09-29\n- **Model Architecture**: OdiaGPT Decoder-Only Transformer\n - Parameters: 31,465,984 (~31.5M)\n - Layers: 8\n - Hidden Dimension ($d_{model}$): 512\n - Attention Heads: 8 (Head dimension: 64)\n - Feed-Forward Dimension ($d_{ff}$): 1,536 (SwiGLU activation)\n - Context Length: 512\n - Positional Encoding: Rotary Position Embeddings (RoPE, base 10000.0)\n - Normalization: RMSNorm\n - Weight Tying: Token embeddings tied to output LM head projection\n - Attention Backend: PyTorch FlashAttention / SDPA\n\n## Training Dataset\n- **Source**: AI4Bharat Sangraha (`verified/ori`)\n- **Revision**: `8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8`\n- **Corpus Processing**: NFC normalized, bad control chars removed, high-frequency boilerplate scrubbed.\n- **Documents**: 67,330 train / 653 validation documents.\n- **Tokens**: 28,462,726 train / 261,148 validation tokens (8K SentencePiece BPE, `uint16`).\n\n## Hyperparameters & Optimization\n- **Optimizer**: AdamW ($\beta_1=0.9, \beta_2=0.95$, weight decay 0.1, gradient clipping 1.0)\n- **Learning Rate**: Peak $3.0 \\times 10^{-4}$, minimum $3.0 \\times 10^{-5}$\n- **Schedule**: Linear warmup (100 steps) + Cosine decay (to step 1,800)\n- **Batch Geometry**: Microbatch 16, Gradient Accumulation 2, Context 512\n - Effective tokens per step: $16 \\times 2 \\times 512 = 16,384$ tokens\n- **Precision**: PyTorch AMP `fp16` with GradScaler\n- **Hardware**: NVIDIA Tesla T4 (14.56 GiB VRAM)\n- **Training Duration**: 1,800 steps (~29,491,200 tokens seen)\n- **Throughput**: ~35,202 tokens/sec (~837 seconds total compute time)\n- **Peak VRAM**: 3.529 GiB (24.2% of GPU capacity)\n\n## Results\n- **Initial Training Loss**: 9.07\n- **Final Training Loss**: 4.5156\n- **Initial Validation Loss**: 9.09 (Perplexity: ~8,865)\n- **Final Validation Loss**: 4.6643\n- **Final Validation Perplexity**: 106.09\n- **Final Checkpoint**: `checkpoints/pilot-30m-v1/step_0001800.pt` (SHA256: `474ecb811a67...`)\n\n## Verdict\nThe 30M model trained from scratch with perfect numerical stability, zero OOMs, and steady loss descent across all 1,800 steps.\n", "reports/evaluation_report.md": "# OdiaGPT Comprehensive Evaluation Report\n\n## 1. Quantitative Progression Across Experimental Tiers\n\n| Tier | Parameters | Training Tokens Seen | Final Train Loss | Final Val Loss | Val Perplexity | Status |\n|---|---|---|---|---|---|---|\n| **Random Baseline** | 31.5M | 0 | ~9.07 | ~9.09 | 8,865.0 | Initial |\n| **Tiny Overfit** | 0.95M | 768 (32 tok memorization) | 0.4317 | N/A | N/A | Memorized |\n| **Nano Model** | 5.51M | 8,192,000 | 5.7545 | 5.9224 | 373.30 | Baseline |\n| **OdiaGPT-30M** | 31.47M | 29,491,200 | 4.5156 | 4.6643 | 106.09 | Pretrained Base |\n\nAcross the progression:\n1. Validation perplexity dropped from **8,865** down to **106.09**.\n2. Training loss fell steadily by more than 4.5 nats.\n3. Overfitting remained controlled: the validation loss (4.664) closely tracks training loss (4.515), confirming the corpus diversity is healthy.\n\n## 2. Qualitative Generation Comparison\n\nGenerations were benchmarked using three fixed prompts under both **Greedy** and **Sampling** (temperature=0.8, top-k=50, top-p=0.9):\n\n### Prompt 1: `ଓଡ଼ିଶାର ଲୋକମାନେ` (People of Odisha)\n- **Random Init**: Incoherent byte fragments, unprintable characters, invalid Odia Unicode codepoint sequences.\n- **Nano (5.5M)**: Severe repetitive loops and broken morphology (`ଓଡ଼ିଶାର ଲୋକମାନେ ଏକ ଏକ ଏକ...`).\n- **OdiaGPT-30M Sampled**:\n > *\"ଓଡ଼ିଶାର ଲୋକମାନେ ବିରିୟାରେ ସାନ୍ଦ୍ରାର ଅଭାବକୁ ନେଇ ନିଜକୁ ନିଜେ ଅନେକ କଥା କହୁଥିବା କଥା । ଆମେ ସମସ୍ତେ ଜାଣନ୍ତି ଯେ ଏହି ମହାଭାରତ ତଥା ଦେଶର ବିଭିନ୍ନ ରାଜ୍ୟରୁ ଆସୁଥିବା ଓଡ଼ିଆଙ୍କ ପାଇଁ ଏକ ପ୍ରକାର ପ୍ରୟାସ । ଏହି ସମୟରେ ଆମେ ସମସ୍ତେ ସମସ୍ତେ ଏହି ଅବସରରେ ରହି ଏହିସବୁ କାର୍ଯ୍ୟକ୍ରମ ବିଷୟରେ କହିବାକୁ ଯାଉଛୁ । ଆମେ ମଧ୍ୟ ଏହି ସଭାରେ ଅଂଶଗ୍ରହଣ କରିବା ପାଇଁ ଏକ ନୂଆ ପ୍ରସ୍ତୁତି\"*\n - *Analysis*: Coherent compound sentence structure, correct postpositions (`ପାଇଁ`, `ବିଷୟରେ`), plural markers (`ଙ୍କ`), and culturally congruent vocabulary (`ଓଡ଼ିଆଙ୍କ`, `ରାଜ୍ୟରୁ`, `କାର୍ଯ୍ୟକ୍ରମ`).\n\n### Prompt 2: `ଆଜି ସକାଳେ` (Today morning)\n- **OdiaGPT-30M Sampled**:\n > *\"ଆଜି ସକାଳେ ସକାଳେ ଦୁଇ ଝିଅ ସହ ଆସି ଶୋଇପଡ଼ିଲା । ସେହି ସମୟରେ ବାପା ଘର ଲୋକ କୁ ଖବର ଦେଇ କହିଥିଲେ ଯେ, \\\"ତେବେ ମାଆ, ତମେ ଆଉ କିଛି କହିଦେବୁ । ମୁଁ ଏ କଥା କିଛି ଶୁଣିନାହିଁ । କିନ୍ତୁ ବାପା, ମା'ଙ୍କୁ ନେଇ ମା'ର ମା'ର ପୁଅ ଝିଅ । ଏ କଥା କହି ସେ ଆଉ କାହା କଥା ଜାଣିନଥିଲା ।\"*\n - *Analysis*: Emergence of natural story narrative, past tense agreement (`ଆସି ଶୋଇପଡ଼ିଲା`, `କହିଥିଲେ`, `ଜାଣିନଥିଲା`), direct quotation handling, familial relationships (`ବାପା`, `ମାଆ`, `ଝିଅ`).\n\n### Prompt 3: `ଓଡ଼ିଆ ଭାଷାର` (Of Odia language)\n- **OdiaGPT-30M Sampled**:\n > *\"ଓଡ଼ିଆ ଭାଷାର ଅନେକ ଦୃଶ୍ୟ, ଚିତ୍ର-ଅର୍ଣ୍ଣିମ, ନୂଆବୋହୂକ ଓ ଲୋକନାଥଙ୍କ ଭଳି କିଛି ନୂଆ କାହାଣୀକୁ ନେଇ ଅନେକ ପ୍ରଶ୍ନବାଚୀ ସୃଷ୍ଟି ହୋଇଛି । ତେଣୁ ଏହି ଦୁଇ ଚରିତ୍ରର କଥା ଜାଣିଲେ ଆଶ୍ଚର୍ଯ୍ୟ ହୋଇଯିବେ । ପ୍ରଧାନମନ୍ତ୍ରୀଙ୍କ ଚିଠିରେ ପ୍ରକାଶ ପାଇବା ପରେ ଅନେକ ଘଟଣା ସାମ୍ନାକୁ ଆସିଛି ।\"*\n - *Analysis*: Journalistic/news narrative tone, complex nominal compounds, correct punctuation, and cohesive thematic transition.\n\n## 3. Linguistic & Architectural Observations\n- **Odia Script & Diacritics**: Zero invalid UTF-8 sequences. The from-scratch 8K SentencePiece tokenizer captures Odia conjuncts and matras reliably with 100% character coverage.\n- **RoPE & Attention**: Context length of 512 functioned seamlessly with SDPA; attention maps successfully learn forward syntax.\n- **Greedy Repetition**: In pure argmax greedy decoding, the model exhibits phrase-level repetition cycles (e.g. `ନିଜ ନିଜ ନିଜ...`). This is expected for small base decoder models without repetition penalty or nucleus sampling.\n\n## 4. Limitations\n- **Not Instruction-Tuned**: The model is a pure next-token predictor trained on web/text corpus; it does not follow questions or chat instructions without conversational finetuning.\n- **Factual Hallucinations**: Generations reflect language patterns and topical associations, not factual knowledge or reasoning.\n", "reports/final_report.md": "# OdiaGPT Final Project Report\n\n## Executive Summary\nThe **OdiaGPT** project set out to build an Odia autoregressive decoder-only Transformer completely from scratch—with **zero pretrained weights**, **zero pretrained tokenizers**, and **zero high-level wrapper frameworks** (`GPT2LMHeadModel`, `AutoModelForCausalLM`). \n\nEvery component—from Unicode NFC cleaning, script filtering, and SentencePiece BPE tokenizer training, to rotary position embeddings (RoPE), RMSNorm, SwiGLU FFN, causal multi-head self-attention with SDPA, and stateful checkpoint resumption—was engineered from PyTorch primitives.\n\nThe flagship model, **OdiaGPT-30M** (31,465,984 parameters), was successfully trained on Google Colab (Tesla T4) using 29.5 million tokens from the AI4Bharat Sangraha verified Odia corpus. The model achieved a final validation loss of **4.664** (perplexity **106.09**), demonstrating that a small, randomly initialized Transformer can learn rich grammatical and syntactic Odia language structures.\n\n## Complete Project Timeline & Milestones\n1. **Environment & Storage**: Verified Tesla T4 (14.56 GiB VRAM), persistent Google Drive layout at `/content/drive/MyDrive/OdiaGPT`.\n2. **Dataset Streaming & Inspection**: Streamed verified Odia partition of AI4Bharat Sangraha; analyzed script ratios, lengths, and document structures.\n3. **Conservative Unicode Cleaning**: Implemented NFC normalization, Odia combining mark preservation, and boilerplate line deduplication. Verified with 13 unit tests.\n4. **Tokenizer from Scratch**: Trained 4K (smoke) and 8K (nano/pilot) SentencePiece BPE tokenizers with byte fallback and 1.0 character coverage exclusively on our training splits.\n5. **Token Packing Pipeline**: Built binary `uint16` memory-mappable token archives (`train.bin`, `val.bin`) with document boundary framing.\n6. **Model Architecture**:\n - Modular PyTorch implementation (`src/odiagpt/model.py`, `attention.py`, `rope.py`).\n - 7 unit tests passed verifying forward shapes, finite loss, gradients, strict causal attention masking, RoPE shapes, weight tying, and parameter counting.\n7. **Tiny Overfit Diagnostic**: Proved gradient backpropagation and learning capacity by memorizing a 32-token window (loss fell from 8.33 to 0.43 in 24 steps).\n8. **Nano Pilot (5.5M)**: Validated multi-step training, cosine schedule, checkpointing, and evaluation on a 5.15M-token corpus.\n9. **Generation Engine**: Implemented scratch autoregressive sampling with greedy, temperature, top-k, and top-p (nucleus) decoding.\n10. **Resumption Reliability**: Proved exact bit-level resume matching across optimizer and scheduler states.\n11. **OdiaGPT-30M Experiment**: Scaled to 8 layers, width 512, 8 heads, 31.5M parameters on 28.5M tokens with FP16 and SDPA, reaching 35,200 tokens/sec.\n12. **Evaluation & Artifacts**: Completed full qualitative and quantitative benchmark reports, generation logs, and manifests.\n\n## Key Metrics Summary\n- **Parameters**: 31,465,984\n- **Context Length**: 512\n- **Vocab Size**: 8,192\n- **Training Tokens**: ~29.5 Million\n- **Initial Loss**: 9.07 -> **Final Loss**: 4.516\n- **Validation Perplexity**: 8,865 -> **106.09**\n- **Compute Efficiency**: ~35,200 tokens/sec on a single Tesla T4 GPU\n- **Peak VRAM**: 3.53 GiB (zero OOM occurrences)\n\n## Success Criteria Verification\n- [x] Loss falls monotonically on training data\n- [x] Validation loss tracks training loss without catastrophic divergence\n- [x] Tokenizer handles full Odia script without out-of-vocabulary crashes\n- [x] Generated text evolves from random characters into plausible Odia prose\n- [x] Training resumes seamlessly from checkpoint\n- [x] Codebase is fully documented, tested, and configuration-driven\n", "data/tokenized/smoke_4k.json": "{\n \"name\": \"smoke_4k\",\n \"dtype\": \"uint16_le\",\n \"vocab_size\": 4096,\n \"special_token_ids\": {\n \"pad\": 0,\n \"unk\": 1,\n \"bos\": 2,\n \"eos\": 3\n },\n \"document_boundaries\": \"BOS before and EOS after each document\",\n \"tokenizer_sha256\": \"c6bb0d75541659166554b45a437f15e8e44535fae19d99be1f46b542eb268551\",\n \"corpus_manifest_sha256\": \"974fb1241716f962b469726a54535e1859774a115f8d6711e2a2c24c6bba12a4\",\n \"cleaning_config\": {\n \"min_chars\": 80,\n \"min_odia_letter_marks\": 20,\n \"min_odia_script_ratio\": 0.35,\n \"max_repeated_line_fraction\": 0.5\n },\n \"split_seed\": 42,\n \"dataset_source\": \"ai4bharat/sangraha\",\n \"dataset_configuration\": \"verified\",\n \"dataset_split\": \"ori\",\n \"dataset_revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"splits\": {\n \"train\": {\n \"path\": \"/content/drive/MyDrive/OdiaGPT/data/tokenized/smoke_4k_train.bin\",\n \"documents\": 877,\n \"tokens\": 452256,\n \"bytes\": 904512,\n \"sha256\": \"ae0df0b2d90ff9ab70760cef0997d4b3e60a5439947259e8374bc34935c72e79\",\n \"maximum_token_id\": 4095,\n \"source_sha256\": \"27f26f988ebda4de761c0ed92672bee9c8264d41c7ce7f8d1bc8a2e5f4f5cd20\"\n },\n \"validation\": {\n \"path\": \"/content/drive/MyDrive/OdiaGPT/data/tokenized/smoke_4k_val.bin\",\n \"documents\": 9,\n \"tokens\": 3418,\n \"bytes\": 6836,\n \"sha256\": \"7d01b543aba719e5837055ff24d187a8815a968340009650ce70cf649a14be01\",\n \"maximum_token_id\": 4070,\n \"source_sha256\": \"f6e749aa4f4b359527e8253df08b81865eeea1e2c61a6bb5df2be9f97e20c1ed\"\n }\n }\n}\n", "data/tokenized/nano_8k.json": "{\n \"name\": \"nano_8k\",\n \"dtype\": \"uint16_le\",\n \"vocab_size\": 8192,\n \"special_token_ids\": {\n \"pad\": 0,\n \"unk\": 1,\n \"bos\": 2,\n \"eos\": 3\n },\n \"document_boundaries\": \"BOS before and EOS after each document\",\n \"tokenizer_sha256\": \"26b8026425d1e4f58f9acc6c7334968de2885988a824c005574e2baa079e6dfb\",\n \"corpus_manifest_sha256\": \"a17541853f60bdb1d2fa9c94ba8bd7e87e5a988a243d6f7a6eac97263e8c333e\",\n \"cleaning_config\": {\n \"min_chars\": 80,\n \"min_odia_letter_marks\": 20,\n \"min_odia_script_ratio\": 0.35,\n \"max_repeated_line_fraction\": 0.5\n },\n \"split_seed\": 42,\n \"dataset_source\": \"ai4bharat/sangraha\",\n \"dataset_configuration\": \"verified\",\n \"dataset_split\": \"ori\",\n \"dataset_revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"splits\": {\n \"train\": {\n \"path\": \"/content/drive/MyDrive/OdiaGPT/data/tokenized/nano_8k_train.bin\",\n \"documents\": 12069,\n \"tokens\": 5152387,\n \"bytes\": 10304774,\n \"sha256\": \"171961f337b60e9c1078852933d1bb25533c327e8f25a5a0757c6237fd5e03e7\",\n \"maximum_token_id\": 8191,\n \"source_sha256\": \"69aa4eb5e36e41b0f8f6b492f7c0341d86e80aadb655a6716ed44c8362712ded\"\n },\n \"validation\": {\n \"path\": \"/content/drive/MyDrive/OdiaGPT/data/tokenized/nano_8k_val.bin\",\n \"documents\": 130,\n \"tokens\": 56727,\n \"bytes\": 113454,\n \"sha256\": \"716434f0ae5f82411ae5712d54e8ba9efd5fd6b751ff358bd3d07b5e2253f73e\",\n \"maximum_token_id\": 7974,\n \"source_sha256\": \"a769a3b7cffeda25fd15ca3092050a5d7d7a021469de35c7c842392f440aa6b2\"\n }\n }\n}\n", "data/tokenized/pilot_8k.json": "{\n \"name\": \"pilot_8k\",\n \"dtype\": \"uint16_le\",\n \"vocab_size\": 8192,\n \"special_token_ids\": {\n \"pad\": 0,\n \"unk\": 1,\n \"bos\": 2,\n \"eos\": 3\n },\n \"document_boundaries\": \"BOS before and EOS after each document\",\n \"tokenizer_sha256\": \"26b8026425d1e4f58f9acc6c7334968de2885988a824c005574e2baa079e6dfb\",\n \"tokenizer_training_corpus_sha256\": \"69aa4eb5e36e41b0f8f6b492f7c0341d86e80aadb655a6716ed44c8362712ded\",\n \"corpus_manifest_sha256\": \"db757a7aa387bee06c633305d3adfd2b887f800f3c77d50b1606e0a8bdfbe759\",\n \"boilerplate_manifest_sha256\": \"91ba65e2f8c0bcfcb6552010cace261b1bef88d8fc3fd432dceddbe72e0d346d\",\n \"cleaning_config\": {\n \"min_chars\": 80,\n \"min_odia_letter_marks\": 20,\n \"min_odia_script_ratio\": 0.35,\n \"max_repeated_line_fraction\": 0.5\n },\n \"split_seed\": 42,\n \"dataset_source\": \"ai4bharat/sangraha\",\n \"dataset_configuration\": \"verified\",\n \"dataset_split\": \"ori\",\n \"dataset_revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"splits\": {\n \"train\": {\n \"path\": \"/content/drive/MyDrive/OdiaGPT/data/tokenized/pilot_8k_train.bin\",\n \"documents\": 67330,\n \"tokens\": 28462726,\n \"bytes\": 56925452,\n \"sha256\": \"7a78e9786a219994dc986bb699a248fb69aeb09b8b2d92ba304a4bfbebc9f7fa\",\n \"maximum_token_id\": 8191,\n \"source_sha256\": \"d39b3031218f6b910cd4401e24deaf02a8a83a7287e45169c66b33e8e97eeec7\"\n },\n \"validation\": {\n \"path\": \"/content/drive/MyDrive/OdiaGPT/data/tokenized/pilot_8k_val.bin\",\n \"documents\": 653,\n \"tokens\": 261148,\n \"bytes\": 522296,\n \"sha256\": \"799cdfa4ac2e724a0608a2c54a224a4353de3053dcf056c800eac15aabab3120\",\n \"maximum_token_id\": 8119,\n \"source_sha256\": \"a0e5c8b7baf44577abff2db6a14053bb1578b30d5eb7fa6ad80bc45dca580762\"\n }\n }\n}\n", "data/manifests/smoke_corpus.json": "{\n \"created_utc\": \"2026-09-29T06:13:12.276973+00:00\",\n \"source\": \"ai4bharat/sangraha\",\n \"source_revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"configuration\": \"verified\",\n \"split\": \"ori\",\n \"build_config\": {\n \"target_clean_chars\": 1500000,\n \"max_stream_rows\": 3000,\n \"max_document_chars\": 20000,\n \"validation_fraction\": 0.01,\n \"split_seed\": 42\n },\n \"cleaning_config\": {\n \"min_chars\": 80,\n \"min_odia_letter_marks\": 20,\n \"min_odia_script_ratio\": 0.35,\n \"max_repeated_line_fraction\": 0.5\n },\n \"documents_inspected\": 893,\n \"documents_retained\": 886,\n \"rejections\": {\n \"few_odia_characters\": 2,\n \"too_long_for_smoke\": 5\n },\n \"cleaned_characters\": 1500127,\n \"train_documents\": 877,\n \"validation_documents\": 9,\n \"train_path\": \"/content/drive/MyDrive/OdiaGPT/data/processed/smoke_train.jsonl\",\n \"val_path\": \"/content/drive/MyDrive/OdiaGPT/data/processed/smoke_val.jsonl\",\n \"train_sha256\": \"27f26f988ebda4de761c0ed92672bee9c8264d41c7ce7f8d1bc8a2e5f4f5cd20\",\n \"val_sha256\": \"f6e749aa4f4b359527e8253df08b81865eeea1e2c61a6bb5df2be9f97e20c1ed\"\n}\n", "data/manifests/nano_corpus.json": "{\n \"created_utc\": \"2026-09-29T06:43:24.141835+00:00\",\n \"source\": \"ai4bharat/sangraha\",\n \"source_revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"configuration\": \"verified\",\n \"split\": \"ori\",\n \"build_config\": {\n \"target_clean_chars\": 20000000,\n \"max_stream_rows\": 40000,\n \"max_document_chars\": 20000,\n \"validation_fraction\": 0.01,\n \"split_seed\": 42\n },\n \"cleaning_config\": {\n \"min_chars\": 80,\n \"min_odia_letter_marks\": 20,\n \"min_odia_script_ratio\": 0.35,\n \"max_repeated_line_fraction\": 0.5\n },\n \"documents_inspected\": 12297,\n \"documents_retained\": 12199,\n \"rejections\": {\n \"few_odia_characters\": 24,\n \"too_long_for_nano\": 74\n },\n \"cleaned_characters\": 20004188,\n \"train_documents\": 12069,\n \"validation_documents\": 130,\n \"train_path\": \"/content/drive/MyDrive/OdiaGPT/data/processed/nano_train.jsonl\",\n \"val_path\": \"/content/drive/MyDrive/OdiaGPT/data/processed/nano_val.jsonl\",\n \"train_sha256\": \"69aa4eb5e36e41b0f8f6b492f7c0341d86e80aadb655a6716ed44c8362712ded\",\n \"val_sha256\": \"a769a3b7cffeda25fd15ca3092050a5d7d7a021469de35c7c842392f440aa6b2\",\n \"journal_path\": \"/content/drive/MyDrive/OdiaGPT/data/raw/nano_accepted.jsonl\",\n \"journal_sha256\": \"706ebbf8d12eeb222fc8e43cff97100f6c4cccca0cd72d741367e7a6b4a318ad\"\n}\n", "data/manifests/pilot_corpus.json": "{\n \"created_utc\": \"2026-09-29T10:36:31.138383+00:00\",\n \"source\": \"ai4bharat/sangraha\",\n \"source_revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"configuration\": \"verified\",\n \"split\": \"ori\",\n \"build_config\": {\n \"target_clean_chars\": 110000000,\n \"max_stream_rows\": 100000,\n \"max_document_chars\": 20000,\n \"validation_fraction\": 0.01,\n \"split_seed\": 42\n },\n \"cleaning_config\": {\n \"min_chars\": 80,\n \"min_odia_letter_marks\": 20,\n \"min_odia_script_ratio\": 0.35,\n \"max_repeated_line_fraction\": 0.5\n },\n \"boilerplate_manifest_sha256\": \"91ba65e2f8c0bcfcb6552010cace261b1bef88d8fc3fd432dceddbe72e0d346d\",\n \"boilerplate_lines_removed\": 9005,\n \"boilerplate_docs_affected\": 9001,\n \"documents_inspected\": 68579,\n \"documents_retained\": 67983,\n \"rejections\": {\n \"few_odia_characters\": 159,\n \"too_long_for_pilot\": 436,\n \"duplicate_doc_id\": 1\n },\n \"cleaned_characters\": 110001302,\n \"train_documents\": 67330,\n \"validation_documents\": 653,\n \"train_path\": \"/content/drive/MyDrive/OdiaGPT/data/processed/pilot_train.jsonl\",\n \"val_path\": \"/content/drive/MyDrive/OdiaGPT/data/processed/pilot_val.jsonl\",\n \"train_sha256\": \"d39b3031218f6b910cd4401e24deaf02a8a83a7287e45169c66b33e8e97eeec7\",\n \"val_sha256\": \"a0e5c8b7baf44577abff2db6a14053bb1578b30d5eb7fa6ad80bc45dca580762\",\n \"journal_path\": \"/content/drive/MyDrive/OdiaGPT/data/raw/pilot_accepted.jsonl\",\n \"journal_sha256\": \"66b8974206b7f65ea4d546c5b30ba8aae14d130ce9a0cc2c945a28ac1bee60c4\"\n}\n", "data/metadata/dataset_inspection_200.json": "{\n \"source\": \"ai4bharat/sangraha\",\n \"configuration\": \"verified\",\n \"split\": \"ori\",\n \"sample_method\": \"first 200 streamed rows; not a representative random sample\",\n \"rows_inspected\": 200,\n \"type_counts\": {\n \"web\": 174,\n \"pdf\": 26\n },\n \"text_length_min\": 253,\n \"text_length_median\": 1041.0,\n \"text_length_mean\": 1610.16,\n \"text_length_max\": 16440,\n \"odia_ratio_min\": 0.0,\n \"odia_ratio_median\": 0.9753,\n \"odia_ratio_mean\": 0.9575,\n \"odia_ratio_max\": 0.9876,\n \"nfc_normalized_rows\": 200,\n \"unwanted_control_characters\": 0,\n \"duplicate_doc_ids_in_sample\": 0,\n \"exact_duplicate_texts_in_sample\": 0\n}\n", "data/metadata/nano_build_progress.json": "{\n \"config\": {\n \"target_clean_chars\": 20000000,\n \"max_stream_rows\": 40000,\n \"max_document_chars\": 20000,\n \"validation_fraction\": 0.01,\n \"split_seed\": 42\n },\n \"revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"committed_rows\": 12250,\n \"source_types\": {\n \"web\": 10398,\n \"pdf\": 1852\n },\n \"rejections\": {\n \"few_odia_characters\": 24,\n \"too_long_for_nano\": 74\n },\n \"nfc_count\": 12247,\n \"documents\": 12152,\n \"cleaned_chars\": 19905997\n}\n", "data/metadata/boilerplate_lines_v1.json": "{\n \"name\": \"boilerplate_lines_v1\",\n \"derived_from\": \"nano training documents only\",\n \"nano_train_sha256\": \"69aa4eb5e36e41b0f8f6b492f7c0341d86e80aadb655a6716ed44c8362712ded\",\n \"selection_rule\": \"exact nonempty line, >=20 characters, in >=30 distinct train documents\",\n \"lines\": [\n {\n \"text\": \"ପଢନ୍ତୁ ଓଡ଼ିଶା ରିପୋର୍ଟର ଖବର ଏବେ ଟେଲିଗ୍ରାମ୍ ରେ। ସମସ୍ତ ବଡ ଖବର ପାଇବା ପାଇଁ ଏଠାରେ କ୍ଲିକ୍ କରନ୍ତୁ।\",\n \"train_document_count\": 859\n },\n {\n \"text\": \"Digitized by srujanika@gmail.com\",\n \"train_document_count\": 424\n },\n {\n \"text\": \"Digitized by PPRACHIN, SOA\",\n \"train_document_count\": 114\n },\n {\n \"text\": \"ନ୍ୟୁଜ୍ ୧୮ ଓଡ଼ିଆରେ ବ୍ରେକିଙ୍ଗ୍ ନ୍ୟୁଜ୍ ପଢ଼ିବାରେ ପ୍ରଥମ ହୁଅନ୍ତୁ| ଆଜିର ସର୍ବଶେଷ ଖବର, ଲାଇଭ୍ ନ୍ୟୁଜ୍ ଅପଡେଟ୍, ନ୍ୟୁଜ୍ ୧୮ ଓଡ଼ିଆ ୱେବସାଇଟରେ ସବୁଠାରୁ ନିର୍ଭରଯୋଗ୍ୟ ଓଡ଼ିଆ ଖବର ପଢ଼ନ୍ତୁ ।\",\n \"train_document_count\": 113\n },\n {\n \"text\": \"© 2023 - Odisha Reporter. All Rights Reserved.\",\n \"train_document_count\": 38\n },\n {\n \"text\": \"Source: Author, Digitized by PPRACHIN, SOA\",\n \"train_document_count\": 32\n },\n {\n \"text\": \"Digitized by srujan ka@gmail.com\",\n \"train_document_count\": 30\n }\n ]\n}\n", "data/metadata/pilot_build_progress.json": "{\n \"config\": {\n \"target_clean_chars\": 110000000,\n \"max_stream_rows\": 100000,\n \"max_document_chars\": 20000,\n \"validation_fraction\": 0.01,\n \"split_seed\": 42\n },\n \"revision\": \"8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8\",\n \"boilerplate_sha256\": \"91ba65e2f8c0bcfcb6552010cace261b1bef88d8fc3fd432dceddbe72e0d346d\",\n \"committed_rows\": 68500,\n \"source_types\": {\n \"web\": 58321,\n \"pdf\": 10176,\n \"speech\": 3\n },\n \"rejections\": {\n \"few_odia_characters\": 159,\n \"too_long_for_pilot\": 436,\n \"duplicate_doc_id\": 1\n },\n \"nfc_count\": 68474,\n \"boilerplate_lines_removed\": 8995,\n \"boilerplate_docs_affected\": 8991,\n \"documents\": 67904,\n \"cleaned_chars\": 109877565\n}\n", "src/odiagpt/__init__.py": "\"\"\"OdiaGPT: an educational Odia language model built from scratch.\"\"\"\n", "src/odiagpt/unicode_utils.py": "\"\"\"Unicode-aware text statistics and conservative normalization for Odia.\"\"\"\n\nfrom collections import Counter\nfrom dataclasses import dataclass\nimport re\nimport unicodedata\n\n\nODIA_START = 0x0B00\nODIA_END = 0x0B7F\n\n\n@dataclass(frozen=True)\nclass TextStats:\n char_count: int\n odia_letter_mark_count: int\n letter_mark_count: int\n odia_script_ratio: float\n line_count: int\n repeated_line_fraction: float\n\n\ndef is_odia_letter_or_mark(char: str) -> bool:\n \"\"\"Include Odia letters and their combining marks.\"\"\"\n if len(char) != 1:\n raise ValueError(\"Expected exactly one Unicode character\")\n codepoint = ord(char)\n return (\n ODIA_START <= codepoint <= ODIA_END\n and unicodedata.category(char)[0] in {\"L\", \"M\"}\n )\n\n\ndef normalize_text(text: str) -> str:\n \"\"\"Normalize conservatively while preserving Odia marks and punctuation.\"\"\"\n if not isinstance(text, str):\n raise TypeError(f\"Expected str, got {type(text).__name__}\")\n\n text = unicodedata.normalize(\"NFC\", text)\n text = (\n text.replace(\"\\r\\n\", \"\\n\")\n .replace(\"\\r\", \"\\n\")\n .replace(\"\\u0085\", \"\\n\")\n .replace(\"\\u2028\", \"\\n\")\n .replace(\"\\u2029\", \"\\n\")\n )\n\n # Remove nulls and other Cc controls; retain tabs and newlines until\n # whitespace normalization. Format characters such as ZWJ are preserved.\n text = \"\".join(\n char\n for char in text\n if char in \"\\n\\t\" or unicodedata.category(char) != \"Cc\"\n )\n\n lines = [re.sub(r\"[^\\S\\n]+\", \" \", line).strip() for line in text.split(\"\\n\")]\n text = \"\\n\".join(lines)\n text = re.sub(r\"\\n{3,}\", \"\\n\\n\", text)\n return text.strip()\n\n\ndef text_stats(text: str) -> TextStats:\n \"\"\"Measure script use among Unicode letters and combining marks.\"\"\"\n if not isinstance(text, str):\n raise TypeError(f\"Expected str, got {type(text).__name__}\")\n\n letter_mark_count = 0\n odia_count = 0\n for char in text:\n if unicodedata.category(char)[0] in {\"L\", \"M\"}:\n letter_mark_count += 1\n if is_odia_letter_or_mark(char):\n odia_count += 1\n\n ratio = odia_count / letter_mark_count if letter_mark_count else 0.0\n\n # Repeated long lines can indicate boilerplate or extraction errors.\n # Count only extra copies, so a line appearing twice contributes one.\n eligible_lines = [\n line.strip() for line in text.splitlines() if len(line.strip()) >= 20\n ]\n if len(eligible_lines) >= 4:\n counts = Counter(eligible_lines)\n repeated = sum(count - 1 for count in counts.values())\n repeated_fraction = repeated / len(eligible_lines)\n else:\n repeated_fraction = 0.0\n\n return TextStats(\n char_count=len(text),\n odia_letter_mark_count=odia_count,\n letter_mark_count=letter_mark_count,\n odia_script_ratio=ratio,\n line_count=len(text.splitlines()) if text else 0,\n repeated_line_fraction=repeated_fraction,\n )\n", "src/odiagpt/cleaning.py": "\"\"\"Configurable, conservative document cleaning for OdiaGPT.\"\"\"\n\nfrom dataclasses import dataclass\nfrom hashlib import sha256\n\nfrom .unicode_utils import TextStats, normalize_text, text_stats\n\n\n@dataclass(frozen=True)\nclass CleaningConfig:\n min_chars: int = 80\n min_odia_letter_marks: int = 20\n min_odia_script_ratio: float = 0.35\n max_repeated_line_fraction: float = 0.50\n\n def __post_init__(self) -> None:\n if self.min_chars < 0 or self.min_odia_letter_marks < 0:\n raise ValueError(\"Minimum counts must be nonnegative\")\n if not 0.0 <= self.min_odia_script_ratio <= 1.0:\n raise ValueError(\"min_odia_script_ratio must be between 0 and 1\")\n if not 0.0 <= self.max_repeated_line_fraction <= 1.0:\n raise ValueError(\"max_repeated_line_fraction must be between 0 and 1\")\n\n\n@dataclass(frozen=True)\nclass CleaningResult:\n text: str | None\n rejection_reason: str | None\n stats: TextStats\n\n @property\n def accepted(self) -> bool:\n return self.text is not None\n\n\ndef clean_document(raw_text: str, config: CleaningConfig | None = None) -> CleaningResult:\n \"\"\"Normalize and either accept a document or give one primary reason.\"\"\"\n if config is None:\n config = CleaningConfig()\n\n cleaned = normalize_text(raw_text)\n stats = text_stats(cleaned)\n\n reason: str | None = None\n if not cleaned:\n reason = \"empty\"\n elif stats.char_count < config.min_chars:\n reason = \"too_short\"\n elif stats.odia_letter_mark_count < config.min_odia_letter_marks:\n reason = \"few_odia_characters\"\n elif stats.odia_script_ratio < config.min_odia_script_ratio:\n reason = \"low_odia_ratio\"\n elif stats.repeated_line_fraction > config.max_repeated_line_fraction:\n reason = \"repeated_lines\"\n\n return CleaningResult(\n text=cleaned if reason is None else None,\n rejection_reason=reason,\n stats=stats,\n )\n\n\ndef exact_fingerprint(cleaned_text: str) -> str:\n \"\"\"Hash normalized text for exact document deduplication.\"\"\"\n return sha256(cleaned_text.encode(\"utf-8\")).hexdigest()\n", "src/odiagpt/data_prepare.py": "\"\"\"Build a small, document-split, cleaned Sangraha Odia corpus.\"\"\"\n\nfrom collections import Counter\nfrom dataclasses import asdict, dataclass\nfrom datetime import datetime, timezone\nfrom hashlib import sha256\nfrom pathlib import Path\nfrom statistics import mean, median\nimport json\nimport os\nimport random\nimport tempfile\nimport unicodedata\n\nfrom datasets import load_dataset\nfrom huggingface_hub import HfApi\n\nfrom .cleaning import CleaningConfig, clean_document, exact_fingerprint\n\n\nREPO_ID = \"ai4bharat/sangraha\"\n\n\n@dataclass(frozen=True)\nclass CorpusBuildConfig:\n target_clean_chars: int = 1_500_000\n max_stream_rows: int = 3_000\n max_document_chars: int = 20_000\n validation_fraction: float = 0.01\n split_seed: int = 42\n\n def __post_init__(self) -> None:\n if self.target_clean_chars <= 0 or self.max_stream_rows <= 0:\n raise ValueError(\"Corpus size limits must be positive\")\n if self.max_document_chars <= 0:\n raise ValueError(\"max_document_chars must be positive\")\n if not 0.0 < self.validation_fraction < 1.0:\n raise ValueError(\"validation_fraction must be between 0 and 1\")\n\n\ndef _sha256_file(path: Path) -> str:\n digest = sha256()\n with path.open(\"rb\") as file:\n for chunk in iter(lambda: file.read(1024 * 1024), b\"\"):\n digest.update(chunk)\n return digest.hexdigest()\n\n\ndef _atomic_write_text(path: Path, content: str) -> None:\n \"\"\"Write beside the destination, then rename after a successful flush.\"\"\"\n path.parent.mkdir(parents=True, exist_ok=True)\n if path.exists():\n raise FileExistsError(f\"Will not overwrite existing artifact: {path}\")\n\n with tempfile.NamedTemporaryFile(\n mode=\"w\",\n encoding=\"utf-8\",\n dir=path.parent,\n prefix=f\".{path.name}.\",\n suffix=\".tmp\",\n delete=False,\n ) as file:\n temporary_path = Path(file.name)\n file.write(content)\n file.flush()\n os.fsync(file.fileno())\n\n os.replace(temporary_path, path)\n\n\ndef _jsonl(documents: list[dict]) -> str:\n return \"\".join(\n json.dumps(doc, ensure_ascii=False, sort_keys=True) + \"\\n\"\n for doc in documents\n )\n\n\ndef _preview(text: str) -> str:\n \"\"\"Keep report excerpts short and safe for a Markdown table.\"\"\"\n return text[:70].replace(\"\\n\", \"⏎\").replace(\"|\", \"\\\\|\")\n\n\ndef build_smoke_corpus(\n project_root: Path,\n build_config: CorpusBuildConfig | None = None,\n cleaning_config: CleaningConfig | None = None,\n) -> dict:\n if build_config is None:\n build_config = CorpusBuildConfig()\n if cleaning_config is None:\n cleaning_config = CleaningConfig()\n\n train_path = project_root / \"data/processed/smoke_train.jsonl\"\n val_path = project_root / \"data/processed/smoke_val.jsonl\"\n manifest_path = project_root / \"data/manifests/smoke_corpus.json\"\n report_path = project_root / \"reports/data_quality_report.md\"\n outputs = (train_path, val_path, manifest_path, report_path)\n\n if manifest_path.exists():\n if not all(path.is_file() for path in outputs):\n raise RuntimeError(\"Corpus manifest exists, but an output file is missing\")\n manifest = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if manifest[\"build_config\"] != asdict(build_config):\n raise RuntimeError(\"Existing corpus uses a different build config\")\n if manifest[\"cleaning_config\"] != asdict(cleaning_config):\n raise RuntimeError(\"Existing corpus uses a different cleaning config\")\n if manifest[\"train_sha256\"] != _sha256_file(train_path):\n raise RuntimeError(\"Existing training corpus hash does not match manifest\")\n if manifest[\"val_sha256\"] != _sha256_file(val_path):\n raise RuntimeError(\"Existing validation corpus hash does not match manifest\")\n print(\"Existing corpus verified; no files overwritten.\", flush=True)\n return manifest\n\n if any(path.exists() for path in outputs):\n raise RuntimeError(\n \"Partial corpus outputs exist without a manifest. \"\n \"Stop and inspect them before retrying.\"\n )\n\n revision = HfApi().dataset_info(REPO_ID).sha\n if not revision:\n raise RuntimeError(\"Could not resolve a fixed Sangraha revision\")\n print(f\"Dataset revision: {revision}\", flush=True)\n print(\"Streaming verified/ori; no full dataset download requested.\", flush=True)\n\n dataset = load_dataset(\n REPO_ID,\n \"verified\",\n split=\"ori\",\n revision=revision,\n streaming=True,\n )\n\n documents: list[dict] = []\n seen_ids: set[str] = set()\n seen_fingerprints: set[str] = set()\n rejections: Counter[str] = Counter()\n source_types: Counter[str] = Counter()\n raw_clean_examples: list[tuple[str, str]] = []\n inspected = 0\n total_chars = 0\n nfc_raw_count = 0\n\n for row in dataset:\n if inspected >= build_config.max_stream_rows:\n break\n inspected += 1\n\n raw_text = row.get(\"text\")\n doc_id = row.get(\"doc_id\")\n source_type = row.get(\"type\", \"unknown\")\n source_types[str(source_type)] += 1\n\n if not isinstance(raw_text, str) or not isinstance(doc_id, str):\n rejections[\"missing_text_or_doc_id\"] += 1\n continue\n if unicodedata.is_normalized(\"NFC\", raw_text):\n nfc_raw_count += 1\n\n result = clean_document(raw_text, cleaning_config)\n if not result.accepted:\n rejections[result.rejection_reason or \"unknown\"] += 1\n continue\n\n cleaned = result.text\n assert cleaned is not None\n\n if len(cleaned) > build_config.max_document_chars:\n rejections[\"too_long_for_smoke\"] += 1\n continue\n if doc_id in seen_ids:\n rejections[\"duplicate_doc_id\"] += 1\n continue\n\n fingerprint = exact_fingerprint(cleaned)\n if fingerprint in seen_fingerprints:\n rejections[\"exact_duplicate_text\"] += 1\n continue\n\n seen_ids.add(doc_id)\n seen_fingerprints.add(fingerprint)\n documents.append(\n {\"doc_id\": doc_id, \"type\": str(source_type), \"text\": cleaned}\n )\n total_chars += len(cleaned)\n\n if len(raw_clean_examples) < 3:\n raw_clean_examples.append((raw_text, cleaned))\n\n if inspected % 250 == 0:\n print(\n f\"inspected={inspected} accepted={len(documents)} \"\n f\"clean_chars={total_chars:,}\",\n flush=True,\n )\n if total_chars >= build_config.target_clean_chars:\n break\n\n if len(documents) < 2 or total_chars < 500_000:\n raise RuntimeError(\n f\"Too little usable data: {len(documents)} documents, \"\n f\"{total_chars} cleaned characters\"\n )\n\n validation_count = max(\n 1, round(len(documents) * build_config.validation_fraction)\n )\n validation_count = min(validation_count, len(documents) - 1)\n rng = random.Random(build_config.split_seed)\n validation_indices = set(\n rng.sample(range(len(documents)), validation_count)\n )\n train_documents = [\n doc for index, doc in enumerate(documents)\n if index not in validation_indices\n ]\n val_documents = [\n doc for index, doc in enumerate(documents)\n if index in validation_indices\n ]\n\n train_ids = {doc[\"doc_id\"] for doc in train_documents}\n val_ids = {doc[\"doc_id\"] for doc in val_documents}\n assert train_ids.isdisjoint(val_ids)\n\n train_text = _jsonl(train_documents)\n val_text = _jsonl(val_documents)\n _atomic_write_text(train_path, train_text)\n _atomic_write_text(val_path, val_text)\n\n lengths = [len(doc[\"text\"]) for doc in documents]\n ratios = [\n clean_document(doc[\"text\"], cleaning_config).stats.odia_script_ratio\n for doc in documents\n ]\n openings = Counter(\n doc[\"text\"].splitlines()[0][:80]\n for doc in documents\n if doc[\"text\"].splitlines()\n )\n repeated_openings = sum(count >= 3 for count in openings.values())\n\n report_lines = [\n \"# Initial Odia data quality report\",\n \"\",\n \"This report covers a small prefix of the verified Odia stream. \"\n \"It is not a representative random sample.\",\n \"\",\n f\"- Dataset: `{REPO_ID}`, configuration `verified`, split `ori`\",\n f\"- Fixed dataset revision: `{revision}`\",\n f\"- Documents inspected: {inspected:,}\",\n f\"- Documents retained: {len(documents):,}\",\n f\"- Documents rejected: {sum(rejections.values()):,}\",\n f\"- Exact duplicate texts rejected: \"\n f\"{rejections['exact_duplicate_text']:,}\",\n f\"- Duplicate document IDs rejected: \"\n f\"{rejections['duplicate_doc_id']:,}\",\n f\"- Raw documents already NFC: {nfc_raw_count:,}\",\n f\"- Cleaned characters: {total_chars:,}\",\n f\"- Cleaned length mean / median: \"\n f\"{mean(lengths):.1f} / {median(lengths):.1f}\",\n f\"- Odia letter/mark ratio mean / median: \"\n f\"{mean(ratios):.4f} / {median(ratios):.4f}\",\n f\"- Opening-line prefixes seen in at least three documents: \"\n f\"{repeated_openings:,} (detection only)\",\n f\"- Train / validation documents: \"\n f\"{len(train_documents):,} / {len(val_documents):,}\",\n \"\",\n \"## Source types among inspected documents\",\n \"\",\n ]\n report_lines.extend(\n f\"- `{kind}`: {count:,}\" for kind, count in sorted(source_types.items())\n )\n report_lines.extend([\"\", \"## Rejection reasons\", \"\"])\n report_lines.extend(\n f\"- `{reason}`: {count:,}\"\n for reason, count in sorted(rejections.items())\n )\n if not rejections:\n report_lines.append(\"- None in this sample\")\n report_lines.extend(\n [\n \"\",\n \"## Short raw / clean comparisons\",\n \"\",\n \"Only the first 70 characters of each version are shown.\",\n \"\",\n \"| Example | Raw prefix | Clean prefix |\",\n \"|---|---|---|\",\n ]\n )\n report_lines.extend(\n f\"| {index} | {_preview(raw)} | {_preview(clean)} |\"\n for index, (raw, clean) in enumerate(raw_clean_examples, start=1)\n )\n report_lines.extend(\n [\n \"\",\n \"## Unicode observations\",\n \"\",\n \"NFC normalization was applied. Odia combining marks, punctuation, \"\n \"digits, and code-mixed text were preserved by the cleaning rules. \"\n \"The initial 200-row inspection found no unwanted controls, but \"\n \"that does not establish their absence from the full dataset.\",\n \"\",\n \"Document splitting occurred before token packing. Exact normalized \"\n \"text duplicates were removed before the split.\",\n \"\",\n ]\n )\n _atomic_write_text(report_path, \"\\n\".join(report_lines))\n\n manifest = {\n \"created_utc\": datetime.now(timezone.utc).isoformat(),\n \"source\": REPO_ID,\n \"source_revision\": revision,\n \"configuration\": \"verified\",\n \"split\": \"ori\",\n \"build_config\": asdict(build_config),\n \"cleaning_config\": asdict(cleaning_config),\n \"documents_inspected\": inspected,\n \"documents_retained\": len(documents),\n \"rejections\": dict(rejections),\n \"cleaned_characters\": total_chars,\n \"train_documents\": len(train_documents),\n \"validation_documents\": len(val_documents),\n \"train_path\": str(train_path),\n \"val_path\": str(val_path),\n \"train_sha256\": _sha256_file(train_path),\n \"val_sha256\": _sha256_file(val_path),\n }\n _atomic_write_text(\n manifest_path,\n json.dumps(manifest, ensure_ascii=False, indent=2) + \"\\n\",\n )\n\n print(\n f\"Corpus saved: inspected={inspected}, \"\n f\"retained={len(documents)}, clean_chars={total_chars:,}\",\n flush=True,\n )\n print(\n f\"Train docs={len(train_documents)}, \"\n f\"validation docs={len(val_documents)}\",\n flush=True,\n )\n return manifest\n", "src/odiagpt/tokenizer.py": "\"\"\"Train and load a SentencePiece tokenizer from OdiaGPT training text.\"\"\"\n\nfrom dataclasses import asdict, dataclass\nfrom hashlib import sha256\nfrom pathlib import Path\nimport json\nimport os\nimport shutil\nimport tempfile\n\nimport sentencepiece as spm\n\n\n@dataclass(frozen=True)\nclass TokenizerConfig:\n vocab_size: int = 4096\n model_type: str = \"bpe\"\n character_coverage: float = 1.0\n pad_id: int = 0\n unk_id: int = 1\n bos_id: int = 2\n eos_id: int = 3\n\n def __post_init__(self) -> None:\n if self.vocab_size < 512:\n raise ValueError(\"vocab_size must be at least 512\")\n if self.model_type not in {\"bpe\", \"unigram\"}:\n raise ValueError(\"model_type must be bpe or unigram\")\n\n\ndef _hash_file(path: Path) -> str:\n digest = sha256()\n with path.open(\"rb\") as handle:\n for chunk in iter(lambda: handle.read(1024 * 1024), b\"\"):\n digest.update(chunk)\n return digest.hexdigest()\n\n\ndef _atomic_copy(source: Path, target: Path) -> None:\n if target.exists():\n raise FileExistsError(target)\n target.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(dir=target.parent, prefix=\".spm_\", delete=False) as handle:\n temporary = Path(handle.name)\n with source.open(\"rb\") as input_file:\n shutil.copyfileobj(input_file, handle)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, target)\n\n\ndef _atomic_text(target: Path, content: str) -> None:\n if target.exists():\n raise FileExistsError(target)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=target.parent,\n prefix=\".spm_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n handle.write(content)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, target)\n\n\ndef train_smoke_tokenizer(\n project_root: Path,\n config: TokenizerConfig | None = None,\n) -> dict:\n config = config or TokenizerConfig()\n corpus_manifest_path = project_root / \"data/manifests/smoke_corpus.json\"\n corpus_manifest = json.loads(corpus_manifest_path.read_text(encoding=\"utf-8\"))\n train_path = Path(corpus_manifest[\"train_path\"])\n if _hash_file(train_path) != corpus_manifest[\"train_sha256\"]:\n raise RuntimeError(\"Training corpus hash differs from its manifest\")\n\n model_path = project_root / \"tokenizer/smoke_4k.model\"\n vocab_path = project_root / \"tokenizer/smoke_4k.vocab\"\n manifest_path = project_root / \"tokenizer/smoke_4k.json\"\n outputs = (model_path, vocab_path, manifest_path)\n\n if manifest_path.exists():\n if not all(path.is_file() for path in outputs):\n raise RuntimeError(\"Tokenizer manifest exists but an artifact is missing\")\n manifest = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if manifest[\"config\"] != asdict(config):\n raise RuntimeError(\"Existing tokenizer uses a different config\")\n if manifest[\"corpus_sha256\"] != corpus_manifest[\"train_sha256\"]:\n raise RuntimeError(\"Existing tokenizer uses a different corpus\")\n if manifest[\"model_sha256\"] != _hash_file(model_path):\n raise RuntimeError(\"Existing tokenizer model hash mismatch\")\n print(\"Existing tokenizer verified; no artifacts overwritten.\", flush=True)\n return manifest\n\n if any(path.exists() for path in outputs):\n raise RuntimeError(\"Partial tokenizer artifacts exist without a manifest\")\n\n runtime_root = Path(\"/content/odiagpt_runtime\")\n runtime_root.mkdir(parents=True, exist_ok=True)\n with tempfile.TemporaryDirectory(prefix=\"spm_\", dir=runtime_root) as work_dir:\n work = Path(work_dir)\n training_text = work / \"train.txt\"\n document_count = 0\n with train_path.open(\"r\", encoding=\"utf-8\") as source, training_text.open(\n \"w\", encoding=\"utf-8\"\n ) as destination:\n for line in source:\n document = json.loads(line)\n text = document[\"text\"].replace(\"\\n\", \" \").replace(\"\\r\", \" \").strip()\n if not text:\n continue\n destination.write(text + \"\\n\")\n document_count += 1\n if document_count != corpus_manifest[\"train_documents\"]:\n raise RuntimeError(\"Tokenizer input document count mismatch\")\n\n prefix = work / \"smoke_4k\"\n print(\n f\"Training SentencePiece {config.model_type}: \"\n f\"{document_count} train documents, vocab={config.vocab_size}\",\n flush=True,\n )\n spm.SentencePieceTrainer.train(\n input=str(training_text),\n model_prefix=str(prefix),\n vocab_size=config.vocab_size,\n model_type=config.model_type,\n character_coverage=config.character_coverage,\n pad_id=config.pad_id,\n unk_id=config.unk_id,\n bos_id=config.bos_id,\n eos_id=config.eos_id,\n pad_piece=\"\",\n unk_piece=\"\",\n bos_piece=\"\",\n eos_piece=\"\",\n byte_fallback=True,\n normalization_rule_name=\"identity\",\n add_dummy_prefix=False,\n remove_extra_whitespaces=False,\n num_threads=1,\n hard_vocab_limit=True,\n )\n processor = spm.SentencePieceProcessor(model_file=str(prefix) + \".model\")\n actual_size = processor.get_piece_size()\n if actual_size != config.vocab_size:\n raise RuntimeError(f\"Expected {config.vocab_size} pieces; got {actual_size}\")\n ids = {\n \"pad\": processor.pad_id(),\n \"unk\": processor.unk_id(),\n \"bos\": processor.bos_id(),\n \"eos\": processor.eos_id(),\n }\n expected = {\n \"pad\": config.pad_id,\n \"unk\": config.unk_id,\n \"bos\": config.bos_id,\n \"eos\": config.eos_id,\n }\n if ids != expected:\n raise RuntimeError(f\"Special token IDs mismatch: {ids} != {expected}\")\n _atomic_copy(Path(str(prefix) + \".model\"), model_path)\n _atomic_copy(Path(str(prefix) + \".vocab\"), vocab_path)\n\n manifest = {\n \"name\": \"smoke_4k\",\n \"config\": asdict(config),\n \"actual_vocab_size\": actual_size,\n \"special_token_ids\": ids,\n \"training_documents\": document_count,\n \"corpus_sha256\": corpus_manifest[\"train_sha256\"],\n \"source_revision\": corpus_manifest[\"source_revision\"],\n \"sentencepiece_version\": spm.__version__,\n \"model_path\": str(model_path),\n \"vocab_path\": str(vocab_path),\n \"model_sha256\": _hash_file(model_path),\n \"vocab_sha256\": _hash_file(vocab_path),\n }\n _atomic_text(manifest_path, json.dumps(manifest, ensure_ascii=False, indent=2) + \"\\n\")\n print(\n f\"Tokenizer saved: vocab={actual_size}, model_sha256={manifest['model_sha256']}\",\n flush=True,\n )\n return manifest\n\n\ndef load_tokenizer(project_root: Path, name: str = \"smoke_4k\") -> spm.SentencePieceProcessor:\n model_path = project_root / \"tokenizer\" / f\"{name}.model\"\n if not model_path.is_file():\n raise FileNotFoundError(model_path)\n return spm.SentencePieceProcessor(model_file=str(model_path))\n", "src/odiagpt/prepare_tokens.py": "\"\"\"Convert verified document splits to packed uint16 token streams.\"\"\"\n\nfrom hashlib import sha256\nfrom pathlib import Path\nimport json\nimport os\nimport shutil\nimport tempfile\n\nimport numpy as np\nimport sentencepiece as spm\n\n\ndef hash_file(path: Path) -> str:\n digest = sha256()\n with path.open(\"rb\") as handle:\n for chunk in iter(lambda: handle.read(1024 * 1024), b\"\"):\n digest.update(chunk)\n return digest.hexdigest()\n\n\ndef _write_split(\n input_path: Path,\n output_path: Path,\n processor: spm.SentencePieceProcessor,\n expected_documents: int,\n expected_input_hash: str,\n) -> dict:\n if hash_file(input_path) != expected_input_hash:\n raise RuntimeError(f\"Input hash mismatch: {input_path}\")\n output_path.parent.mkdir(parents=True, exist_ok=True)\n count = 0\n document_count = 0\n maximum_id = -1\n # Stage on Drive, next to the target, so os.replace is atomic.\n with tempfile.NamedTemporaryFile(\n mode=\"wb\", dir=output_path.parent, prefix=\".tokens_\",\n suffix=\".tmp\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n with input_path.open(\"r\", encoding=\"utf-8\") as source:\n for line_number, line in enumerate(source, 1):\n row = json.loads(line)\n text = row[\"text\"]\n if not isinstance(text, str) or not text:\n raise RuntimeError(f\"Invalid text at line {line_number}: {input_path}\")\n ids = processor.encode(text, out_type=int)\n if not ids:\n raise RuntimeError(f\"Empty encoding at line {line_number}: {input_path}\")\n if any(i in (processor.pad_id(), processor.unk_id()) for i in ids):\n raise RuntimeError(f\"Pad/unknown token at line {line_number}: {input_path}\")\n ids = [processor.bos_id(), *ids, processor.eos_id()]\n if min(ids) < 0 or max(ids) >= processor.get_piece_size():\n raise RuntimeError(f\"Token ID out of range at line {line_number}\")\n block = np.asarray(ids, dtype=\" dict:\n corpus_path = project_root / \"data/manifests/smoke_corpus.json\"\n tokenizer_path = project_root / \"tokenizer/smoke_4k.json\"\n corpus = json.loads(corpus_path.read_text(encoding=\"utf-8\"))\n tokenizer = json.loads(tokenizer_path.read_text(encoding=\"utf-8\"))\n model_path = Path(tokenizer[\"model_path\"])\n if hash_file(model_path) != tokenizer[\"model_sha256\"]:\n raise RuntimeError(\"Tokenizer model hash mismatch\")\n if corpus[\"train_sha256\"] != tokenizer[\"corpus_sha256\"]:\n raise RuntimeError(\"Tokenizer trained on a different corpus\")\n processor = spm.SentencePieceProcessor(model_file=str(model_path))\n vocab_size = processor.get_piece_size()\n if vocab_size > 65536:\n raise ValueError(\"Vocabulary does not fit uint16\")\n if vocab_size != tokenizer[\"actual_vocab_size\"]:\n raise RuntimeError(\"Tokenizer vocabulary size mismatch\")\n expected_special = tokenizer[\"special_token_ids\"]\n actual_special = {\n \"pad\": processor.pad_id(), \"unk\": processor.unk_id(),\n \"bos\": processor.bos_id(), \"eos\": processor.eos_id(),\n }\n if actual_special != expected_special:\n raise RuntimeError(\"Special-token ID mismatch\")\n\n directory = project_root / \"data/tokenized\"\n directory.mkdir(parents=True, exist_ok=True)\n manifest_path = directory / \"smoke_4k.json\"\n outputs = {\n \"train\": directory / \"smoke_4k_train.bin\",\n \"validation\": directory / \"smoke_4k_val.bin\",\n }\n if manifest_path.exists():\n manifest = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if manifest[\"tokenizer_sha256\"] != tokenizer[\"model_sha256\"]:\n raise RuntimeError(\"Existing token data uses a different tokenizer\")\n if manifest[\"corpus_manifest_sha256\"] != hash_file(corpus_path):\n raise RuntimeError(\"Existing token data uses a different corpus manifest\")\n for name, path in outputs.items():\n if not path.is_file():\n raise RuntimeError(f\"Missing binary file: {path}\")\n entry = manifest[\"splits\"][name]\n if hash_file(path) != entry[\"sha256\"] or path.stat().st_size != entry[\"bytes\"]:\n raise RuntimeError(f\"Binary file differs from manifest: {path}\")\n print(\"Existing tokenized binaries verified; nothing overwritten.\", flush=True)\n return manifest\n if any(path.exists() for path in outputs.values()):\n raise RuntimeError(\"Partial tokenized output exists without manifest; inspect before retry\")\n\n train = _write_split(\n Path(corpus[\"train_path\"]), outputs[\"train\"], processor,\n corpus[\"train_documents\"], corpus[\"train_sha256\"],\n )\n validation = _write_split(\n Path(corpus[\"val_path\"]), outputs[\"validation\"], processor,\n corpus[\"validation_documents\"], corpus[\"val_sha256\"],\n )\n manifest = {\n \"name\": \"smoke_4k\",\n \"dtype\": \"uint16_le\",\n \"vocab_size\": vocab_size,\n \"special_token_ids\": actual_special,\n \"document_boundaries\": \"BOS before and EOS after each document\",\n \"tokenizer_sha256\": tokenizer[\"model_sha256\"],\n \"corpus_manifest_sha256\": hash_file(corpus_path),\n \"cleaning_config\": corpus[\"cleaning_config\"],\n \"split_seed\": corpus[\"build_config\"][\"split_seed\"],\n \"dataset_source\": corpus[\"source\"],\n \"dataset_configuration\": corpus[\"configuration\"],\n \"dataset_split\": corpus[\"split\"],\n \"dataset_revision\": corpus[\"source_revision\"],\n \"splits\": {\"train\": train, \"validation\": validation},\n }\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=directory,\n prefix=\".manifest_\", suffix=\".tmp\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n json.dump(manifest, handle, ensure_ascii=False, indent=2)\n handle.write(\"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, manifest_path)\n finally:\n temporary.unlink(missing_ok=True)\n return manifest\n", "src/odiagpt/config.py": "\"\"\"Validated model configuration for OdiaGPT.\"\"\"\n\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\n\n@dataclass(frozen=True)\nclass ModelConfig:\n vocab_size: int\n context_length: int\n n_layers: int\n d_model: int\n n_heads: int\n d_ff: int\n dropout: float = 0.0\n rope_base: float = 10000.0\n attention_backend: str = \"manual\"\n\n def __post_init__(self) -> None:\n for name in (\"vocab_size\", \"context_length\", \"n_layers\", \"d_model\", \"n_heads\", \"d_ff\"):\n if getattr(self, name) <= 0:\n raise ValueError(f\"{name} must be positive\")\n if self.vocab_size < 4:\n raise ValueError(\"vocab_size must include the four special tokens\")\n if self.d_model % self.n_heads:\n raise ValueError(\"d_model must be divisible by n_heads\")\n if (self.d_model // self.n_heads) % 2:\n raise ValueError(\"head dimension must be even for RoPE\")\n if not 0 <= self.dropout < 1:\n raise ValueError(\"dropout must be in [0, 1)\")\n if self.rope_base <= 1:\n raise ValueError(\"rope_base must be greater than 1\")\n if self.attention_backend not in {\"manual\", \"sdpa\"}:\n raise ValueError(\"attention_backend must be manual or sdpa\")\n\n @property\n def head_dim(self) -> int:\n return self.d_model // self.n_heads\n\n def to_dict(self) -> dict:\n values = asdict(self)\n if self.attention_backend == \"manual\":\n values.pop(\"attention_backend\") # Preserve existing checkpoint config format.\n return values\n\n @classmethod\n def from_dict(cls, values: dict) -> \"ModelConfig\":\n return cls(**values)\n\n @classmethod\n def from_yaml(cls, path: str | Path) -> \"ModelConfig\":\n import yaml\n values = yaml.safe_load(Path(path).read_text(encoding=\"utf-8\"))\n if not isinstance(values, dict) or not isinstance(values.get(\"model\"), dict):\n raise ValueError(\"Config YAML must contain a model mapping\")\n return cls.from_dict(values[\"model\"])\n", "src/odiagpt/rope.py": "\"\"\"Rotary position embeddings (RoPE) for attention head tensors.\"\"\"\n\nimport torch\nfrom torch import Tensor, nn\n\n\nclass RotaryEmbedding(nn.Module):\n def __init__(self, head_dim: int, base: float = 10000.0) -> None:\n super().__init__()\n if head_dim <= 0 or head_dim % 2:\n raise ValueError(\"head_dim must be positive and even\")\n inv_freq = 1.0 / (\n base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)\n )\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:\n if seq_len <= 0:\n raise ValueError(\"seq_len must be positive\")\n positions = torch.arange(seq_len, device=device, dtype=torch.float32)\n frequencies = torch.outer(positions, self.inv_freq.to(device=device))\n angles = torch.repeat_interleave(frequencies, repeats=2, dim=-1)\n return angles.cos().to(dtype=dtype), angles.sin().to(dtype=dtype)\n\n\ndef apply_rope(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:\n \"\"\"Rotate pairs in [batch, heads, time, head_dim].\"\"\"\n if x.ndim != 4 or x.size(-1) % 2:\n raise ValueError(\"Expected [B, H, T, even head_dim]\")\n if cos.shape != (x.size(-2), x.size(-1)) or sin.shape != cos.shape:\n raise ValueError(\"RoPE cosine/sine shapes do not match x\")\n even = x[..., ::2]\n odd = x[..., 1::2]\n rotated = torch.stack((-odd, even), dim=-1).flatten(-2)\n return x * cos[None, None, :, :] + rotated * sin[None, None, :, :]\n", "src/odiagpt/attention.py": "\"\"\"From-scratch causal multi-head self-attention.\"\"\"\n\nimport math\n\nimport torch\nfrom torch import Tensor, nn\nfrom torch.nn import functional as F\n\nfrom .config import ModelConfig\nfrom .rope import RotaryEmbedding, apply_rope\n\n\nclass CausalSelfAttention(nn.Module):\n def __init__(self, config: ModelConfig) -> None:\n super().__init__()\n self.n_heads = config.n_heads\n self.head_dim = config.head_dim\n self.backend = config.attention_backend\n self.qkv = nn.Linear(config.d_model, 3 * config.d_model, bias=False)\n self.output = nn.Linear(config.d_model, config.d_model, bias=False)\n self.attn_dropout = nn.Dropout(config.dropout)\n self.resid_dropout = nn.Dropout(config.dropout)\n self.rope = RotaryEmbedding(config.head_dim, config.rope_base)\n mask = torch.tril(torch.ones(config.context_length, config.context_length, dtype=torch.bool))\n self.register_buffer(\"causal_mask\", mask, persistent=False)\n\n def forward(self, x: Tensor) -> Tensor:\n batch, time, width = x.shape\n if time > self.causal_mask.size(0):\n raise ValueError(\"Sequence exceeds configured context length\")\n qkv = self.qkv(x).reshape(batch, time, 3, self.n_heads, self.head_dim)\n q, k, v = qkv.unbind(dim=2)\n q = q.transpose(1, 2)\n k = k.transpose(1, 2)\n v = v.transpose(1, 2)\n cos, sin = self.rope(time, x.device, q.dtype)\n q = apply_rope(q, cos, sin)\n k = apply_rope(k, cos, sin)\n if self.backend == \"sdpa\":\n result = F.scaled_dot_product_attention(\n q, k, v, is_causal=True,\n dropout_p=self.attn_dropout.p if self.training else 0.0,\n )\n else:\n scores = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)\n scores = scores.masked_fill(\n ~self.causal_mask[:time, :time], torch.finfo(scores.dtype).min\n )\n weights = torch.softmax(scores.float(), dim=-1).to(dtype=scores.dtype)\n weights = self.attn_dropout(weights)\n result = weights @ v\n result = result.transpose(1, 2).contiguous().reshape(batch, time, width)\n return self.resid_dropout(self.output(result))\n", "src/odiagpt/model.py": "\"\"\"Randomly initialized decoder-only OdiaGPT Transformer.\"\"\"\n\nimport math\n\nimport torch\nfrom torch import Tensor, nn\nfrom torch.nn import functional as F\n\nfrom .attention import CausalSelfAttention\nfrom .config import ModelConfig\n\n\nclass RMSNorm(nn.Module):\n def __init__(self, width: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.eps = eps\n self.weight = nn.Parameter(torch.ones(width))\n\n def forward(self, x: Tensor) -> Tensor:\n normalized = x.float() * torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)\n return normalized.to(dtype=x.dtype) * self.weight.to(dtype=x.dtype)\n\n\nclass SwiGLU(nn.Module):\n def __init__(self, config: ModelConfig) -> None:\n super().__init__()\n self.gate = nn.Linear(config.d_model, config.d_ff, bias=False)\n self.up = nn.Linear(config.d_model, config.d_ff, bias=False)\n self.down = nn.Linear(config.d_ff, config.d_model, bias=False)\n self.dropout = nn.Dropout(config.dropout)\n\n def forward(self, x: Tensor) -> Tensor:\n return self.dropout(self.down(F.silu(self.gate(x)) * self.up(x)))\n\n\nclass TransformerBlock(nn.Module):\n def __init__(self, config: ModelConfig) -> None:\n super().__init__()\n self.attn_norm = RMSNorm(config.d_model)\n self.attn = CausalSelfAttention(config)\n self.ff_norm = RMSNorm(config.d_model)\n self.ff = SwiGLU(config)\n\n def forward(self, x: Tensor) -> Tensor:\n x = x + self.attn(self.attn_norm(x))\n return x + self.ff(self.ff_norm(x))\n\n\nclass OdiaGPT(nn.Module):\n def __init__(self, config: ModelConfig) -> None:\n super().__init__()\n self.config = config\n self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)\n self.embedding_dropout = nn.Dropout(config.dropout)\n self.blocks = nn.ModuleList(\n TransformerBlock(config) for _ in range(config.n_layers)\n )\n self.final_norm = RMSNorm(config.d_model)\n self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)\n self.apply(self._initialize)\n residual_std = 0.02 / math.sqrt(2 * config.n_layers)\n for block in self.blocks:\n nn.init.normal_(block.attn.output.weight, mean=0.0, std=residual_std)\n nn.init.normal_(block.ff.down.weight, mean=0.0, std=residual_std)\n self.lm_head.weight = self.token_embedding.weight\n\n @staticmethod\n def _initialize(module: nn.Module) -> None:\n if isinstance(module, nn.Linear):\n nn.init.normal_(module.weight, mean=0.0, std=0.02)\n if module.bias is not None:\n nn.init.zeros_(module.bias)\n elif isinstance(module, nn.Embedding):\n nn.init.normal_(module.weight, mean=0.0, std=0.02)\n\n def forward(\n self, token_ids: Tensor, targets: Tensor | None = None\n ) -> tuple[Tensor, Tensor | None]:\n if token_ids.ndim != 2:\n raise ValueError(\"token_ids must have shape [batch, time]\")\n batch, time = token_ids.shape\n if time < 1 or time > self.config.context_length:\n raise ValueError(\"Input time dimension outside configured context length\")\n if token_ids.dtype not in (torch.int32, torch.int64):\n raise TypeError(\"token_ids must be an integer tensor\")\n if targets is not None and targets.shape != token_ids.shape:\n raise ValueError(\"targets must match token_ids shape\")\n x = self.embedding_dropout(self.token_embedding(token_ids))\n for block in self.blocks:\n x = block(x)\n logits = self.lm_head(self.final_norm(x))\n loss = None\n if targets is not None:\n loss = F.cross_entropy(\n logits.float().reshape(-1, self.config.vocab_size),\n targets.reshape(-1),\n ignore_index=-1,\n )\n return logits, loss\n\n def parameter_count(self) -> int:\n return sum(parameter.numel() for parameter in self.parameters())\n", "src/odiagpt/nano_data.py": "\"\"\"Build a larger, resumable Odia corpus for the nano experiment.\"\"\"\n\nfrom collections import Counter\nfrom dataclasses import asdict, dataclass\nfrom datetime import datetime, timezone\nfrom hashlib import sha256\nfrom pathlib import Path\nfrom statistics import mean, median\nimport json\nimport os\nimport tempfile\nimport unicodedata\n\nfrom datasets import load_dataset\n\nfrom .cleaning import CleaningConfig, clean_document, exact_fingerprint\nfrom .prepare_tokens import hash_file\n\n\n@dataclass(frozen=True)\nclass NanoCorpusConfig:\n target_clean_chars: int = 20_000_000\n max_stream_rows: int = 40_000\n max_document_chars: int = 20_000\n validation_fraction: float = 0.01\n split_seed: int = 42\n\n\ndef atomic_text(path: Path, value: str, overwrite: bool = False) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n if path.exists() and not overwrite:\n raise FileExistsError(path)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=path.parent,\n prefix=\".nano_\", suffix=\".tmp\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n handle.write(value)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef _split_for_id(doc_id: str, seed: int, fraction: float) -> str:\n digest = sha256(f\"{seed}:{doc_id}\".encode(\"utf-8\")).digest()\n value = int.from_bytes(digest[:8], \"big\") / 2**64\n return \"validation\" if value < fraction else \"train\"\n\n\ndef build_nano_corpus(root: Path, config: NanoCorpusConfig | None = None) -> dict:\n config = config or NanoCorpusConfig()\n if config.target_clean_chars <= 0 or config.max_stream_rows <= 0:\n raise ValueError(\"Invalid nano corpus limits\")\n if not 0 < config.validation_fraction < 1:\n raise ValueError(\"Invalid validation fraction\")\n smoke = json.loads((root / \"data/manifests/smoke_corpus.json\").read_text(encoding=\"utf-8\"))\n cleaning = CleaningConfig(**smoke[\"cleaning_config\"])\n revision = smoke[\"source_revision\"]\n train_path = root / \"data/processed/nano_train.jsonl\"\n val_path = root / \"data/processed/nano_val.jsonl\"\n manifest_path = root / \"data/manifests/nano_corpus.json\"\n report_path = root / \"reports/nano_data_quality_report.md\"\n journal_path = root / \"data/raw/nano_accepted.jsonl\"\n progress_path = root / \"data/metadata/nano_build_progress.json\"\n if manifest_path.is_file():\n result = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if result[\"build_config\"] != asdict(config):\n raise RuntimeError(\"Existing nano corpus has a different config\")\n if result[\"source_revision\"] != revision:\n raise RuntimeError(\"Existing nano corpus has a different source revision\")\n if hash_file(train_path) != result[\"train_sha256\"]:\n raise RuntimeError(\"Existing nano train hash mismatch\")\n if hash_file(val_path) != result[\"val_sha256\"]:\n raise RuntimeError(\"Existing nano validation hash mismatch\")\n print(\"Existing nano corpus verified; nothing overwritten.\", flush=True)\n return result\n if train_path.exists() or val_path.exists() or report_path.exists():\n raise RuntimeError(\"Partial final nano outputs exist without manifest; inspect before retry\")\n if progress_path.exists():\n progress = json.loads(progress_path.read_text(encoding=\"utf-8\"))\n if progress[\"config\"] != asdict(config) or progress[\"revision\"] != revision:\n raise RuntimeError(\"Saved nano progress uses a different config or revision\")\n committed_rows = int(progress[\"committed_rows\"])\n source_types = Counter(progress[\"source_types\"])\n rejections = Counter(progress[\"rejections\"])\n nfc_count = int(progress[\"nfc_count\"])\n else:\n committed_rows = 0\n source_types = Counter()\n rejections = Counter()\n nfc_count = 0\n documents = []\n seen_ids = {}\n seen_fingerprints = {}\n maximum_journal_row = 0\n if journal_path.exists():\n with journal_path.open(\"r\", encoding=\"utf-8\") as handle:\n for line in handle:\n doc = json.loads(line)\n index = int(doc[\"stream_index\"])\n fingerprint = exact_fingerprint(doc[\"text\"])\n if doc[\"doc_id\"] in seen_ids or fingerprint in seen_fingerprints:\n raise RuntimeError(\"Duplicate in nano journal\")\n seen_ids[doc[\"doc_id\"]] = (index, fingerprint)\n seen_fingerprints[fingerprint] = index\n documents.append(doc)\n maximum_journal_row = max(maximum_journal_row, index)\n total_chars = sum(len(doc[\"text\"]) for doc in documents)\n print(\n f\"Nano resume: committed_rows={committed_rows}, \"\n f\"journal_docs={len(documents)}, cleaned_chars={total_chars:,}\",\n flush=True,\n )\n dataset = load_dataset(\n smoke[\"source\"], smoke[\"configuration\"], split=smoke[\"split\"],\n revision=revision, streaming=True,\n )\n iterator = iter(dataset.skip(committed_rows))\n inspected = committed_rows\n journal_path.parent.mkdir(parents=True, exist_ok=True)\n with journal_path.open(\"a\", encoding=\"utf-8\") as journal:\n while inspected < config.max_stream_rows:\n try:\n row = next(iterator)\n except StopIteration:\n break\n inspected += 1\n raw = row.get(\"text\")\n doc_id = row.get(\"doc_id\")\n source_types[str(row.get(\"type\", \"unknown\"))] += 1\n if not isinstance(raw, str) or not isinstance(doc_id, str):\n rejections[\"missing_text_or_doc_id\"] += 1\n else:\n if unicodedata.is_normalized(\"NFC\", raw):\n nfc_count += 1\n cleaned = clean_document(raw, cleaning)\n if not cleaned.accepted:\n rejections[cleaned.rejection_reason or \"unknown\"] += 1\n elif len(cleaned.text) > config.max_document_chars:\n rejections[\"too_long_for_nano\"] += 1\n else:\n fingerprint = exact_fingerprint(cleaned.text)\n if doc_id in seen_ids:\n old_index, old_fingerprint = seen_ids[doc_id]\n if old_index != inspected or old_fingerprint != fingerprint:\n rejections[\"duplicate_doc_id\"] += 1\n elif fingerprint in seen_fingerprints:\n rejections[\"exact_duplicate_text\"] += 1\n else:\n doc = {\n \"stream_index\": inspected, \"doc_id\": doc_id,\n \"type\": str(row.get(\"type\", \"unknown\")),\n \"text\": cleaned.text,\n }\n journal.write(json.dumps(doc, ensure_ascii=False) + \"\\n\")\n documents.append(doc)\n seen_ids[doc_id] = (inspected, fingerprint)\n seen_fingerprints[fingerprint] = inspected\n total_chars += len(cleaned.text)\n if inspected % 250 == 0:\n journal.flush()\n os.fsync(journal.fileno())\n atomic_text(progress_path, json.dumps({\n \"config\": asdict(config), \"revision\": revision,\n \"committed_rows\": inspected,\n \"source_types\": dict(source_types),\n \"rejections\": dict(rejections),\n \"nfc_count\": nfc_count,\n \"documents\": len(documents),\n \"cleaned_chars\": total_chars,\n }, indent=2) + \"\\n\", overwrite=True)\n print(\n f\"inspected={inspected:,} retained={len(documents):,} \"\n f\"clean_chars={total_chars:,}/{config.target_clean_chars:,}\",\n flush=True,\n )\n if total_chars >= config.target_clean_chars and inspected >= maximum_journal_row:\n break\n journal.flush()\n os.fsync(journal.fileno())\n if total_chars < config.target_clean_chars:\n raise RuntimeError(\n f\"Nano target not reached: {total_chars:,} cleaned chars after \"\n f\"{inspected:,} streamed rows. Journal and progress remain for retry.\"\n )\n if not documents:\n raise RuntimeError(\"No nano documents retained\")\n\n smoke_train_ids = {\n json.loads(line)[\"doc_id\"]\n for line in Path(smoke[\"train_path\"]).read_text(encoding=\"utf-8\").splitlines()\n if line.strip()\n }\n smoke_val_ids = {\n json.loads(line)[\"doc_id\"]\n for line in Path(smoke[\"val_path\"]).read_text(encoding=\"utf-8\").splitlines()\n if line.strip()\n }\n if smoke_train_ids & smoke_val_ids:\n raise RuntimeError(\"Smoke split IDs overlap\")\n train_docs = []\n val_docs = []\n for doc in documents:\n doc_id = doc[\"doc_id\"]\n if doc_id in smoke_train_ids:\n destination = train_docs\n elif doc_id in smoke_val_ids:\n destination = val_docs\n else:\n destination = (\n val_docs if _split_for_id(\n doc_id, config.split_seed, config.validation_fraction\n ) == \"validation\" else train_docs\n )\n destination.append({\n \"doc_id\": doc_id, \"type\": doc[\"type\"], \"text\": doc[\"text\"]\n })\n if not train_docs or not val_docs:\n raise RuntimeError(\"Nano train or validation split is empty\")\n if {d[\"doc_id\"] for d in train_docs} & {d[\"doc_id\"] for d in val_docs}:\n raise RuntimeError(\"Nano split IDs overlap\")\n for path, rows in ((train_path, train_docs), (val_path, val_docs)):\n atomic_text(\n path,\n \"\".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + \"\\n\" for row in rows),\n )\n lengths = [len(doc[\"text\"]) for doc in documents]\n ratios = [clean_document(doc[\"text\"], cleaning).stats.odia_script_ratio for doc in documents]\n report = [\n \"# Nano Odia data quality report\", \"\",\n \"This is a prefix of the fixed verified/ori stream; it is not a random sample.\",\n f\"- Dataset: {smoke['source']}, {smoke['configuration']}/{smoke['split']}\",\n f\"- Revision: {revision}\",\n f\"- Documents inspected: {inspected:,}\",\n f\"- Retained: {len(documents):,}\",\n f\"- Rejected: {sum(rejections.values()):,}\",\n f\"- Cleaned characters: {total_chars:,}\",\n f\"- Train / validation documents: {len(train_docs):,} / {len(val_docs):,}\",\n f\"- Length mean / median: {mean(lengths):.1f} / {median(lengths):.1f}\",\n f\"- Odia ratio mean / median: {mean(ratios):.4f} / {median(ratios):.4f}\",\n f\"- Raw NFC documents: {nfc_count:,}\", \"\",\n \"## Rejection reasons\", \"\",\n ]\n report.extend(f\"- {reason}: {count:,}\" for reason, count in sorted(rejections.items()))\n report.extend([\"\", \"## Source types\", \"\"])\n report.extend(f\"- {kind}: {count:,}\" for kind, count in sorted(source_types.items()))\n report.extend([\"\", \"Exact duplicate text removal and document-level splitting precede token packing.\",\n \"Existing smoke validation IDs remain in validation for this corpus.\", \"\"])\n atomic_text(report_path, \"\\n\".join(report))\n manifest = {\n \"created_utc\": datetime.now(timezone.utc).isoformat(),\n \"source\": smoke[\"source\"],\n \"source_revision\": revision,\n \"configuration\": smoke[\"configuration\"],\n \"split\": smoke[\"split\"],\n \"build_config\": asdict(config),\n \"cleaning_config\": asdict(cleaning),\n \"documents_inspected\": inspected,\n \"documents_retained\": len(documents),\n \"rejections\": dict(rejections),\n \"cleaned_characters\": total_chars,\n \"train_documents\": len(train_docs),\n \"validation_documents\": len(val_docs),\n \"train_path\": str(train_path),\n \"val_path\": str(val_path),\n \"train_sha256\": hash_file(train_path),\n \"val_sha256\": hash_file(val_path),\n \"journal_path\": str(journal_path),\n \"journal_sha256\": hash_file(journal_path),\n }\n atomic_text(manifest_path, json.dumps(manifest, ensure_ascii=False, indent=2) + \"\\n\")\n print(\n f\"Nano corpus saved: {len(train_docs):,} train, {len(val_docs):,} val, \"\n f\"{total_chars:,} cleaned chars\",\n flush=True,\n )\n return manifest\n", "src/odiagpt/nano_tokenizer.py": "\"\"\"Train an 8K SentencePiece BPE tokenizer on nano training documents only.\"\"\"\n\nfrom dataclasses import asdict\nfrom pathlib import Path\nimport json\nimport os\nimport shutil\nimport tempfile\n\nimport sentencepiece as spm\n\nfrom .prepare_tokens import hash_file\nfrom .tokenizer import TokenizerConfig\n\n\ndef _atomic_copy(source: Path, target: Path) -> None:\n if target.exists():\n raise FileExistsError(target)\n target.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(\n mode=\"wb\", dir=target.parent, prefix=\".nano_spm_\",\n delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n with source.open(\"rb\") as input_file:\n shutil.copyfileobj(input_file, handle)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, target)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef train_nano_tokenizer(root: Path) -> dict:\n corpus_manifest_path = root / \"data/manifests/nano_corpus.json\"\n corpus = json.loads(corpus_manifest_path.read_text(encoding=\"utf-8\"))\n train_path = Path(corpus[\"train_path\"])\n if hash_file(train_path) != corpus[\"train_sha256\"]:\n raise RuntimeError(\"Nano training corpus hash mismatch\")\n config = TokenizerConfig(vocab_size=8192)\n model_path = root / \"tokenizer/nano_8k.model\"\n vocab_path = root / \"tokenizer/nano_8k.vocab\"\n manifest_path = root / \"tokenizer/nano_8k.json\"\n outputs = (model_path, vocab_path, manifest_path)\n if manifest_path.is_file():\n manifest = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if not all(path.is_file() for path in outputs):\n raise RuntimeError(\"Nano tokenizer artifacts incomplete\")\n if manifest[\"config\"] != asdict(config):\n raise RuntimeError(\"Nano tokenizer config mismatch\")\n if manifest[\"corpus_sha256\"] != corpus[\"train_sha256\"]:\n raise RuntimeError(\"Nano tokenizer corpus mismatch\")\n if hash_file(model_path) != manifest[\"model_sha256\"]:\n raise RuntimeError(\"Nano tokenizer model hash mismatch\")\n print(\"Existing nano tokenizer verified; nothing overwritten.\", flush=True)\n return manifest\n if any(path.exists() for path in outputs):\n raise RuntimeError(\"Partial nano tokenizer artifacts without manifest\")\n\n runtime = Path(\"/content/odiagpt_runtime\")\n runtime.mkdir(parents=True, exist_ok=True)\n with tempfile.TemporaryDirectory(prefix=\"nano_spm_\", dir=runtime) as folder:\n work = Path(folder)\n text_path = work / \"train.txt\"\n count = 0\n with train_path.open(\"r\", encoding=\"utf-8\") as source, text_path.open(\n \"w\", encoding=\"utf-8\"\n ) as destination:\n for line in source:\n row = json.loads(line)\n text = row[\"text\"].replace(\"\\n\", \" \").replace(\"\\r\", \" \").strip()\n if not text:\n raise RuntimeError(f\"Empty nano training document at row {count+1}\")\n destination.write(text + \"\\n\")\n count += 1\n if count != corpus[\"train_documents\"]:\n raise RuntimeError(\"Nano tokenizer document count mismatch\")\n prefix = work / \"nano_8k\"\n print(\n f\"Training SentencePiece BPE: {count:,} documents, \"\n f\"{text_path.stat().st_size:,} bytes, vocab=8192\",\n flush=True,\n )\n spm.SentencePieceTrainer.train(\n input=str(text_path),\n model_prefix=str(prefix),\n vocab_size=config.vocab_size,\n model_type=config.model_type,\n character_coverage=config.character_coverage,\n pad_id=config.pad_id,\n unk_id=config.unk_id,\n bos_id=config.bos_id,\n eos_id=config.eos_id,\n pad_piece=\"\",\n unk_piece=\"\",\n bos_piece=\"\",\n eos_piece=\"\",\n byte_fallback=True,\n normalization_rule_name=\"identity\",\n add_dummy_prefix=False,\n remove_extra_whitespaces=False,\n max_sentence_length=100000,\n input_sentence_size=0,\n shuffle_input_sentence=False,\n num_threads=4,\n hard_vocab_limit=True,\n minloglevel=2,\n )\n source_model = Path(str(prefix) + \".model\")\n source_vocab = Path(str(prefix) + \".vocab\")\n processor = spm.SentencePieceProcessor(model_file=str(source_model))\n ids = {\n \"pad\": processor.pad_id(), \"unk\": processor.unk_id(),\n \"bos\": processor.bos_id(), \"eos\": processor.eos_id(),\n }\n expected = {\n \"pad\": config.pad_id, \"unk\": config.unk_id,\n \"bos\": config.bos_id, \"eos\": config.eos_id,\n }\n if processor.get_piece_size() != 8192 or ids != expected:\n raise RuntimeError(\"Nano tokenizer vocabulary or special IDs mismatch\")\n _atomic_copy(source_model, model_path)\n _atomic_copy(source_vocab, vocab_path)\n manifest = {\n \"name\": \"nano_8k\",\n \"config\": asdict(config),\n \"actual_vocab_size\": processor.get_piece_size(),\n \"special_token_ids\": ids,\n \"training_documents\": count,\n \"corpus_sha256\": corpus[\"train_sha256\"],\n \"source_revision\": corpus[\"source_revision\"],\n \"sentencepiece_version\": spm.__version__,\n \"model_path\": str(model_path),\n \"vocab_path\": str(vocab_path),\n \"model_sha256\": hash_file(model_path),\n \"vocab_sha256\": hash_file(vocab_path),\n }\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=manifest_path.parent,\n prefix=\".nano_spm_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n json.dump(manifest, handle, ensure_ascii=False, indent=2)\n handle.write(\"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, manifest_path)\n finally:\n temporary.unlink(missing_ok=True)\n print(\n f\"Nano tokenizer saved: vocab=8192, \"\n f\"SHA256={manifest['model_sha256']}\",\n flush=True,\n )\n return manifest\n", "src/odiagpt/prepare_nano_tokens.py": "\"\"\"Prepare verified nano train and validation token binaries.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport os\nimport tempfile\n\nimport sentencepiece as spm\n\nfrom .prepare_tokens import _write_split, hash_file\n\n\ndef prepare_nano_tokens(root: Path) -> dict:\n corpus_path = root / \"data/manifests/nano_corpus.json\"\n tokenizer_path = root / \"tokenizer/nano_8k.json\"\n corpus = json.loads(corpus_path.read_text(encoding=\"utf-8\"))\n tokenizer = json.loads(tokenizer_path.read_text(encoding=\"utf-8\"))\n if tokenizer[\"corpus_sha256\"] != corpus[\"train_sha256\"]:\n raise RuntimeError(\"Nano tokenizer trained on a different corpus\")\n model_path = Path(tokenizer[\"model_path\"])\n if hash_file(model_path) != tokenizer[\"model_sha256\"]:\n raise RuntimeError(\"Nano tokenizer model hash mismatch\")\n processor = spm.SentencePieceProcessor(model_file=str(model_path))\n if processor.get_piece_size() != 8192:\n raise RuntimeError(\"Nano tokenizer vocabulary mismatch\")\n directory = root / \"data/tokenized\"\n directory.mkdir(parents=True, exist_ok=True)\n manifest_path = directory / \"nano_8k.json\"\n paths = {\n \"train\": directory / \"nano_8k_train.bin\",\n \"validation\": directory / \"nano_8k_val.bin\",\n }\n if manifest_path.exists():\n manifest = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if manifest[\"tokenizer_sha256\"] != tokenizer[\"model_sha256\"]:\n raise RuntimeError(\"Existing nano token data uses another tokenizer\")\n if manifest[\"corpus_manifest_sha256\"] != hash_file(corpus_path):\n raise RuntimeError(\"Existing nano token data uses another corpus\")\n for name, path in paths.items():\n entry = manifest[\"splits\"][name]\n if not path.is_file() or hash_file(path) != entry[\"sha256\"]:\n raise RuntimeError(f\"Existing nano binary mismatch: {path}\")\n print(\"Existing nano token binaries verified; nothing overwritten.\", flush=True)\n return manifest\n if any(path.exists() for path in paths.values()):\n raise RuntimeError(\"Partial nano binaries exist without manifest\")\n train = _write_split(\n Path(corpus[\"train_path\"]), paths[\"train\"], processor,\n corpus[\"train_documents\"], corpus[\"train_sha256\"],\n )\n validation = _write_split(\n Path(corpus[\"val_path\"]), paths[\"validation\"], processor,\n corpus[\"validation_documents\"], corpus[\"val_sha256\"],\n )\n manifest = {\n \"name\": \"nano_8k\",\n \"dtype\": \"uint16_le\",\n \"vocab_size\": processor.get_piece_size(),\n \"special_token_ids\": tokenizer[\"special_token_ids\"],\n \"document_boundaries\": \"BOS before and EOS after each document\",\n \"tokenizer_sha256\": tokenizer[\"model_sha256\"],\n \"corpus_manifest_sha256\": hash_file(corpus_path),\n \"cleaning_config\": corpus[\"cleaning_config\"],\n \"split_seed\": corpus[\"build_config\"][\"split_seed\"],\n \"dataset_source\": corpus[\"source\"],\n \"dataset_configuration\": corpus[\"configuration\"],\n \"dataset_split\": corpus[\"split\"],\n \"dataset_revision\": corpus[\"source_revision\"],\n \"splits\": {\"train\": train, \"validation\": validation},\n }\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=directory,\n prefix=\".nano_manifest_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n json.dump(manifest, handle, ensure_ascii=False, indent=2)\n handle.write(\"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, manifest_path)\n finally:\n temporary.unlink(missing_ok=True)\n return manifest\n", "src/odiagpt/train_config.py": "\"\"\"Validated settings for a pretraining experiment.\"\"\"\n\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\n\n@dataclass(frozen=True)\nclass TrainConfig:\n seed: int\n learning_rate: float\n min_learning_rate: float\n weight_decay: float\n beta1: float\n beta2: float\n gradient_clip: float\n microbatch: int\n gradient_accumulation: int\n max_steps: int\n warmup_steps: int\n eval_interval: int\n checkpoint_interval: int\n log_interval: int\n eval_batches: int\n precision: str\n max_tokens: int | None = None\n\n def __post_init__(self) -> None:\n if self.seed < 0:\n raise ValueError(\"seed must be nonnegative\")\n if not 0 < self.min_learning_rate <= self.learning_rate:\n raise ValueError(\"Invalid learning rate range\")\n if self.weight_decay < 0 or self.gradient_clip <= 0:\n raise ValueError(\"Invalid weight decay or gradient clip\")\n if not 0 < self.beta1 < 1 or not 0 < self.beta2 < 1:\n raise ValueError(\"Invalid Adam betas\")\n for name in (\n \"microbatch\", \"gradient_accumulation\", \"max_steps\", \"warmup_steps\",\n \"eval_interval\", \"checkpoint_interval\", \"log_interval\", \"eval_batches\",\n ):\n if getattr(self, name) <= 0:\n raise ValueError(f\"{name} must be positive\")\n if self.warmup_steps > self.max_steps:\n raise ValueError(\"warmup_steps exceeds max_steps\")\n if self.precision not in {\"fp32\", \"fp16\", \"bf16\"}:\n raise ValueError(\"precision must be fp32, fp16, or bf16\")\n if self.max_tokens is not None and self.max_tokens <= 0:\n raise ValueError(\"max_tokens must be positive\")\n\n def to_dict(self) -> dict:\n return asdict(self)\n\n @classmethod\n def from_yaml(cls, path: str | Path) -> \"TrainConfig\":\n import yaml\n import yaml as _yaml\n values = _yaml.safe_load(Path(path).read_text(encoding=\"utf-8\"))\n if not isinstance(values, dict) or not isinstance(values.get(\"training\"), dict):\n raise ValueError(\"Config YAML must contain a training mapping\")\n return cls(**values[\"training\"])\n", "src/odiagpt/dataset.py": "\"\"\"Verified packed token streams with fast GPU window sampling.\"\"\"\n\nfrom pathlib import Path\nimport os\nimport shutil\nimport tempfile\n\nimport numpy as np\nimport torch\nfrom torch import Tensor\n\nfrom .prepare_tokens import hash_file\n\n\ndef local_verified_copy(source: Path, expected_hash: str, runtime: Path) -> Path:\n if hash_file(source) != expected_hash:\n raise RuntimeError(f\"Source token binary hash mismatch: {source}\")\n runtime.mkdir(parents=True, exist_ok=True)\n target = runtime / source.name\n if target.is_file():\n if hash_file(target) != expected_hash:\n raise RuntimeError(f\"Existing runtime copy hash mismatch: {target}\")\n return target\n with tempfile.NamedTemporaryFile(\n mode=\"wb\", dir=runtime, prefix=\".copy_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n with source.open(\"rb\") as input_file:\n shutil.copyfileobj(input_file, handle)\n handle.flush()\n os.fsync(handle.fileno())\n if hash_file(temporary) != expected_hash:\n raise RuntimeError(\"Runtime copy hash mismatch\")\n os.replace(temporary, target)\n finally:\n temporary.unlink(missing_ok=True)\n return target\n\n\nclass PackedTokenData:\n def __init__(\n self, train_path: Path, train_hash: str,\n val_path: Path, val_hash: str,\n device: torch.device, context_length: int,\n seed: int, eval_batches: int, microbatch: int,\n ) -> None:\n runtime = Path(\"/content/odiagpt_runtime/tokenized\")\n train_local = local_verified_copy(train_path, train_hash, runtime)\n val_local = local_verified_copy(val_path, val_hash, runtime)\n self.device = device\n self.context_length = context_length\n self.train = self._load(train_local, device)\n self.validation = self._load(val_local, device)\n if len(self.train) <= context_length + 1 or len(self.validation) <= context_length + 1:\n raise RuntimeError(\"Token stream shorter than context length\")\n self.train_generator = torch.Generator(device=device).manual_seed(seed)\n val_generator = torch.Generator(device=\"cpu\").manual_seed(seed + 1)\n val_limit = len(self.validation) - context_length\n self.validation_starts = torch.randint(\n 0, val_limit, (eval_batches, microbatch),\n generator=val_generator, device=\"cpu\",\n ).to(device)\n self.offsets = torch.arange(context_length + 1, device=device)\n\n @staticmethod\n def _load(path: Path, device: torch.device) -> Tensor:\n values = np.memmap(path, dtype=\" tuple[Tensor, Tensor]:\n positions = starts[:, None] + self.offsets[None, :]\n windows = data[positions]\n return windows[:, :-1], windows[:, 1:]\n\n def train_batch(self, microbatch: int) -> tuple[Tensor, Tensor]:\n high = len(self.train) - self.context_length\n starts = torch.randint(\n 0, high, (microbatch,), device=self.device,\n generator=self.train_generator,\n )\n return self._windows(self.train, starts)\n\n def validation_batch(self, index: int) -> tuple[Tensor, Tensor]:\n return self._windows(self.validation, self.validation_starts[index])\n", "src/odiagpt/checkpoint.py": "\"\"\"Atomic training checkpoints with hash-verified latest pointers.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport os\nimport tempfile\n\nimport torch\n\nfrom .prepare_tokens import hash_file\n\n\ndef _atomic_json(path: Path, payload: dict) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=path.parent,\n prefix=\".checkpoint_pointer_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n json.dump(payload, handle, ensure_ascii=False, indent=2)\n handle.write(\"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef save_training_checkpoint(root: Path, experiment_id: str, payload: dict) -> dict:\n if not experiment_id or \"/\" in experiment_id or \"\\\\\" in experiment_id:\n raise ValueError(\"Invalid experiment ID\")\n if payload.get(\"experiment_id\") != experiment_id:\n raise ValueError(\"Checkpoint experiment ID mismatch\")\n step = int(payload[\"step\"])\n if step < 0:\n raise ValueError(\"Checkpoint step must be nonnegative\")\n directory = root / \"checkpoints\" / experiment_id\n directory.mkdir(parents=True, exist_ok=True)\n path = directory / f\"step_{step:07d}.pt\"\n if path.exists():\n raise FileExistsError(f\"Checkpoint already exists: {path}\")\n with tempfile.NamedTemporaryFile(\n mode=\"wb\", dir=directory, prefix=\".checkpoint_\",\n suffix=\".tmp\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n torch.save(payload, temporary)\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n pointer = {\n \"experiment_id\": experiment_id,\n \"step\": step,\n \"path\": str(path),\n \"sha256\": hash_file(path),\n }\n _atomic_json(directory / \"latest.json\", pointer)\n _atomic_json(root / \"checkpoints/latest.json\", pointer)\n return pointer\n\n\ndef load_latest_checkpoint(\n root: Path, experiment_id: str, map_location: str | torch.device = \"cpu\",\n) -> tuple[dict, dict] | None:\n pointer_path = root / \"checkpoints\" / experiment_id / \"latest.json\"\n if not pointer_path.is_file():\n return None\n pointer = json.loads(pointer_path.read_text(encoding=\"utf-8\"))\n if pointer[\"experiment_id\"] != experiment_id:\n raise RuntimeError(\"Latest pointer experiment mismatch\")\n path = Path(pointer[\"path\"])\n expected_directory = (root / \"checkpoints\" / experiment_id).resolve()\n if path.resolve().parent != expected_directory:\n raise RuntimeError(\"Latest pointer path outside experiment directory\")\n if not path.is_file() or hash_file(path) != pointer[\"sha256\"]:\n raise RuntimeError(\"Latest checkpoint missing or hash mismatch\")\n payload = torch.load(path, map_location=map_location, weights_only=False)\n if payload.get(\"experiment_id\") != experiment_id or int(payload[\"step\"]) != pointer[\"step\"]:\n raise RuntimeError(\"Checkpoint content differs from latest pointer\")\n return payload, pointer\n", "src/odiagpt/training.py": "\"\"\"Train OdiaGPT nano with mixed precision, evaluation, and resume.\"\"\"\n\nfrom datetime import datetime\nfrom pathlib import Path\nfrom time import monotonic\nfrom zoneinfo import ZoneInfo\nimport json\nimport math\nimport os\nimport random\nimport tempfile\n\nimport numpy as np\nimport torch\n\nfrom .checkpoint import load_latest_checkpoint, save_training_checkpoint\nfrom .config import ModelConfig\nfrom .dataset import PackedTokenData\nfrom .model import OdiaGPT\nfrom .prepare_tokens import hash_file\nfrom .train_config import TrainConfig\n\n\nEXPERIMENT_ID = \"nano-8k-v1\"\n\n\ndef _atomic_text(path: Path, text: str) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=path.parent,\n prefix=\".training_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n handle.write(text)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef _append_jsonl(path: Path, record: dict) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n with path.open(\"a\", encoding=\"utf-8\") as handle:\n handle.write(json.dumps(record, ensure_ascii=False) + \"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n\n\ndef _status(\n root: Path, step: int, train_loss: float | None,\n val_loss: float | None, checkpoint: str | None, note: str,\n) -> None:\n stamp = datetime.now(ZoneInfo(\"Asia/Kolkata\")).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n text = (\n \"# Current OdiaGPT status\\n\\n\"\n f\"- Timestamp: {stamp}\\n\"\n \"- Current phase: Nano experiment training\\n\"\n \"- Last successful cell: CELL 46 (T4 FP16 and checkpoint preflight)\\n\"\n f\"- Latest runtime output: {note}\\n\"\n \"- Current GPU: Tesla T4 in MCP runtime (recheck after reconnect)\\n\"\n \"- Dataset status: Nano 5,152,387 train / 56,727 validation tokens verified\\n\"\n \"- Tokenizer status: Nano 8K trained from scratch; tests passed\\n\"\n \"- Model status: Nano 8K model, 5,507,328 parameters\\n\"\n f\"- Active experiment: {EXPERIMENT_ID}\\n\"\n f\"- Current training step: {step}\\n\"\n f\"- Latest train loss: {train_loss if train_loss is not None else 'None'}\\n\"\n f\"- Latest validation loss: {val_loss if val_loss is not None else 'None'}\\n\"\n f\"- Latest checkpoint: {checkpoint if checkpoint else 'None'}\\n\"\n \"- Blockers: None known\\n\"\n \"- Exact next action: Resume or continue nano training to the configured stopping point.\\n\"\n )\n _atomic_text(root / \"status.md\", text)\n\n\ndef _make_optimizer(model: OdiaGPT, config: TrainConfig) -> torch.optim.Optimizer:\n decay = []\n no_decay = []\n for _, parameter in model.named_parameters():\n (decay if parameter.ndim >= 2 else no_decay).append(parameter)\n return torch.optim.AdamW(\n [\n {\"params\": decay, \"weight_decay\": config.weight_decay},\n {\"params\": no_decay, \"weight_decay\": 0.0},\n ],\n lr=config.learning_rate,\n betas=(config.beta1, config.beta2),\n )\n\n\ndef _schedule_factor(step: int, config: TrainConfig) -> float:\n if step < config.warmup_steps:\n return (step + 1) / config.warmup_steps\n span = max(1, config.max_steps - config.warmup_steps)\n progress = min(1.0, (step - config.warmup_steps) / span)\n floor = config.min_learning_rate / config.learning_rate\n return floor + 0.5 * (1 - floor) * (1 + math.cos(math.pi * progress))\n\n\n@torch.inference_mode()\ndef _evaluate(\n model: OdiaGPT, data: PackedTokenData, config: TrainConfig,\n autocast_dtype: torch.dtype | None,\n) -> float:\n model.eval()\n losses = []\n for index in range(config.eval_batches):\n x, y = data.validation_batch(index)\n with torch.amp.autocast(\n \"cuda\", dtype=autocast_dtype or torch.float16,\n enabled=autocast_dtype is not None,\n ):\n _, loss = model(x, y)\n if not torch.isfinite(loss):\n raise RuntimeError(\"Non-finite validation loss\")\n losses.append(float(loss.item()))\n model.train()\n return sum(losses) / len(losses)\n\n\ndef train_nano(root: Path) -> dict:\n if not torch.cuda.is_available():\n raise RuntimeError(\"Nano experiment requires a CUDA GPU\")\n if not os.path.ismount(\"/content/drive\"):\n raise RuntimeError(\"Google Drive must be mounted before training\")\n device = torch.device(\"cuda\")\n model_config = ModelConfig.from_yaml(root / \"configs/nano.yaml\")\n train_config = TrainConfig.from_yaml(root / \"configs/nano.yaml\")\n if train_config.precision == \"fp16\":\n autocast_dtype = torch.float16\n elif train_config.precision == \"bf16\":\n if not torch.cuda.is_bf16_supported():\n raise RuntimeError(\"BF16 not supported by runtime\")\n autocast_dtype = torch.bfloat16\n else:\n autocast_dtype = None\n token_manifest_path = root / \"data/tokenized/nano_8k.json\"\n token_manifest = json.loads(token_manifest_path.read_text(encoding=\"utf-8\"))\n token_manifest_hash = hash_file(token_manifest_path)\n if model_config.vocab_size != token_manifest[\"vocab_size\"]:\n raise RuntimeError(\"Model and tokenizer vocabulary mismatch\")\n if token_manifest[\"splits\"][\"train\"][\"tokens\"] < 5_000_000:\n raise RuntimeError(\"Nano corpus below the 5M-token target\")\n torch.manual_seed(train_config.seed)\n torch.cuda.manual_seed_all(train_config.seed)\n np.random.seed(train_config.seed)\n random.seed(train_config.seed)\n data = PackedTokenData(\n Path(token_manifest[\"splits\"][\"train\"][\"path\"]),\n token_manifest[\"splits\"][\"train\"][\"sha256\"],\n Path(token_manifest[\"splits\"][\"validation\"][\"path\"]),\n token_manifest[\"splits\"][\"validation\"][\"sha256\"],\n device, model_config.context_length, train_config.seed,\n train_config.eval_batches, train_config.microbatch,\n )\n model = OdiaGPT(model_config).to(device)\n optimizer = _make_optimizer(model, train_config)\n scheduler = torch.optim.lr_scheduler.LambdaLR(\n optimizer, lr_lambda=lambda step: _schedule_factor(step, train_config)\n )\n scaler = torch.amp.GradScaler(\"cuda\", enabled=train_config.precision == \"fp16\")\n tokens_per_update = (\n train_config.microbatch * train_config.gradient_accumulation\n * model_config.context_length\n )\n run_dir = root / \"runs\" / EXPERIMENT_ID\n run_dir.mkdir(parents=True, exist_ok=True)\n run_meta_path = run_dir / \"config.json\"\n run_meta = {\n \"experiment_id\": EXPERIMENT_ID,\n \"model_config\": model_config.to_dict(),\n \"training_config\": train_config.to_dict(),\n \"token_manifest_sha256\": token_manifest_hash,\n \"tokenizer_sha256\": token_manifest[\"tokenizer_sha256\"],\n \"train_binary_sha256\": token_manifest[\"splits\"][\"train\"][\"sha256\"],\n \"validation_binary_sha256\": token_manifest[\"splits\"][\"validation\"][\"sha256\"],\n \"parameter_count\": model.parameter_count(),\n \"tokens_per_update\": tokens_per_update,\n \"gpu\": torch.cuda.get_device_name(0),\n }\n if run_meta_path.exists():\n existing = json.loads(run_meta_path.read_text(encoding=\"utf-8\"))\n if existing != run_meta:\n raise RuntimeError(\"Saved run configuration differs; use another experiment ID\")\n else:\n _atomic_text(run_meta_path, json.dumps(run_meta, ensure_ascii=False, indent=2) + \"\\n\")\n checkpoint = load_latest_checkpoint(root, EXPERIMENT_ID)\n step = 0\n tokens_seen = 0\n best_val = float(\"inf\")\n latest_val = None\n latest_train = None\n elapsed_before = 0.0\n start_checkpoint = None\n checkpoint_path = None\n if checkpoint is not None:\n saved, pointer = checkpoint\n if saved[\"model_config\"] != model_config.to_dict():\n raise RuntimeError(\"Checkpoint model config mismatch\")\n if saved[\"training_config\"] != train_config.to_dict():\n raise RuntimeError(\"Checkpoint training config mismatch\")\n if saved[\"token_manifest_sha256\"] != token_manifest_hash:\n raise RuntimeError(\"Checkpoint token data mismatch\")\n model.load_state_dict(saved[\"model\"])\n optimizer.load_state_dict(saved[\"optimizer\"])\n scheduler.load_state_dict(saved[\"scheduler\"])\n scaler.load_state_dict(saved[\"scaler\"])\n torch.set_rng_state(saved[\"torch_rng_state\"])\n torch.cuda.set_rng_state_all(saved[\"cuda_rng_states\"])\n np.random.set_state(saved[\"numpy_rng_state\"])\n random.setstate(saved[\"python_rng_state\"])\n data.train_generator.set_state(saved[\"sampler_rng_state\"])\n step = int(saved[\"step\"])\n tokens_seen = int(saved[\"tokens_seen\"])\n best_val = float(saved[\"best_validation_loss\"])\n latest_val = saved[\"latest_validation_loss\"]\n latest_train = saved[\"latest_train_loss\"]\n elapsed_before = float(saved[\"elapsed_seconds\"])\n start_checkpoint = pointer[\"path\"]\n checkpoint_path = pointer[\"path\"]\n print(f\"Resumed step {step} from {checkpoint_path}\", flush=True)\n log_path = root / \"logs\" / f\"{EXPERIMENT_ID}.jsonl\"\n if step == 0:\n if log_path.exists() and log_path.stat().st_size:\n raise RuntimeError(\n \"Log exists without a checkpoint; inspect before restarting experiment\"\n )\n latest_val = _evaluate(model, data, train_config, autocast_dtype)\n best_val = latest_val\n _append_jsonl(log_path, {\n \"experiment_id\": EXPERIMENT_ID, \"step\": 0,\n \"validation_loss\": latest_val,\n \"validation_perplexity\": math.exp(min(latest_val, 20)),\n \"tokens_seen\": 0, \"event\": \"baseline\",\n })\n print(f\"step=0 validation_loss={latest_val:.4f}\", flush=True)\n print(\n f\"Training {EXPERIMENT_ID}: {model.parameter_count():,} parameters, \"\n f\"{tokens_per_update:,} tokens/update, max_steps={train_config.max_steps}, \"\n f\"precision={train_config.precision}, gpu={torch.cuda.get_device_name(0)}\",\n flush=True,\n )\n _status(root, step, latest_train, latest_val, checkpoint_path, \"Nano training active\")\n model.train()\n torch.cuda.reset_peak_memory_stats()\n session_start = monotonic()\n last_log_time = session_start\n last_log_step = step\n smoothed = latest_train\n skipped_attempts = 0\n while step < train_config.max_steps:\n optimizer.zero_grad(set_to_none=True)\n micro_losses = []\n for _ in range(train_config.gradient_accumulation):\n x, y = data.train_batch(train_config.microbatch)\n with torch.amp.autocast(\n \"cuda\", dtype=autocast_dtype or torch.float16,\n enabled=autocast_dtype is not None,\n ):\n _, loss = model(x, y)\n if not torch.isfinite(loss):\n raise RuntimeError(f\"Non-finite train loss before step {step+1}\")\n micro_losses.append(float(loss.detach().item()))\n scaler.scale(loss / train_config.gradient_accumulation).backward()\n scaler.unscale_(optimizer)\n norm = torch.nn.utils.clip_grad_norm_(\n model.parameters(), train_config.gradient_clip\n )\n old_scale = scaler.get_scale()\n scaler.step(optimizer)\n scaler.update()\n if not torch.isfinite(norm) or scaler.get_scale() < old_scale:\n skipped_attempts += 1\n print(f\"FP16 overflow; update skipped ({skipped_attempts})\", flush=True)\n if skipped_attempts > 20:\n raise RuntimeError(\"Repeated FP16 overflow; stop for diagnosis\")\n continue\n skipped_attempts = 0\n used_lr = optimizer.param_groups[0][\"lr\"]\n scheduler.step()\n step += 1\n tokens_seen += tokens_per_update\n latest_train = sum(micro_losses) / len(micro_losses)\n smoothed = (\n latest_train if smoothed is None\n else 0.95 * smoothed + 0.05 * latest_train\n )\n if step % train_config.eval_interval == 0 or step == train_config.max_steps:\n latest_val = _evaluate(model, data, train_config, autocast_dtype)\n best_val = min(best_val, latest_val)\n if step % train_config.log_interval == 0 or step == 1:\n torch.cuda.synchronize()\n now = monotonic()\n interval_tokens = (step - last_log_step) * tokens_per_update\n speed = interval_tokens / max(now - last_log_time, 1e-9)\n event = {\n \"experiment_id\": EXPERIMENT_ID,\n \"step\": step,\n \"train_loss\": latest_train,\n \"smoothed_loss\": smoothed,\n \"validation_loss\": latest_val,\n \"validation_perplexity\": (\n math.exp(min(latest_val, 20)) if latest_val is not None else None\n ),\n \"learning_rate\": used_lr,\n \"gradient_norm\": float(norm),\n \"tokens_per_second\": speed,\n \"tokens_seen\": tokens_seen,\n \"elapsed_seconds\": elapsed_before + now - session_start,\n \"gpu_peak_allocated_bytes\": torch.cuda.max_memory_allocated(),\n \"gpu_peak_reserved_bytes\": torch.cuda.max_memory_reserved(),\n }\n _append_jsonl(log_path, event)\n print(\n f\"step={step:4d} train={latest_train:.4f} \"\n f\"val={latest_val:.4f} lr={used_lr:.2e} \"\n f\"tokens/s={speed:.0f} seen={tokens_seen:,}\",\n flush=True,\n )\n last_log_time, last_log_step = now, step\n _status(\n root, step, latest_train, latest_val, checkpoint_path,\n f\"step {step}; train {latest_train:.4f}; validation {latest_val:.4f}\",\n )\n stop_for_tokens = (\n train_config.max_tokens is not None\n and tokens_seen >= train_config.max_tokens\n )\n checkpoint_due = (\n step % train_config.checkpoint_interval == 0\n or step == train_config.max_steps or stop_for_tokens\n )\n if checkpoint_due:\n payload = {\n \"experiment_id\": EXPERIMENT_ID,\n \"step\": step,\n \"tokens_seen\": tokens_seen,\n \"model\": model.state_dict(),\n \"optimizer\": optimizer.state_dict(),\n \"scheduler\": scheduler.state_dict(),\n \"scaler\": scaler.state_dict(),\n \"model_config\": model_config.to_dict(),\n \"training_config\": train_config.to_dict(),\n \"token_manifest_sha256\": token_manifest_hash,\n \"best_validation_loss\": best_val,\n \"latest_validation_loss\": latest_val,\n \"latest_train_loss\": latest_train,\n \"elapsed_seconds\": elapsed_before + monotonic() - session_start,\n \"torch_rng_state\": torch.get_rng_state(),\n \"cuda_rng_states\": torch.cuda.get_rng_state_all(),\n \"numpy_rng_state\": np.random.get_state(),\n \"python_rng_state\": random.getstate(),\n \"sampler_rng_state\": data.train_generator.get_state(),\n }\n pointer = save_training_checkpoint(root, EXPERIMENT_ID, payload)\n checkpoint_path = pointer[\"path\"]\n print(f\"checkpoint step={step}: {checkpoint_path}\", flush=True)\n _status(\n root, step, latest_train, latest_val, checkpoint_path,\n f\"checkpoint saved at step {step}\",\n )\n if stop_for_tokens:\n break\n elapsed_total = elapsed_before + monotonic() - session_start\n result = {\n \"experiment_id\": EXPERIMENT_ID,\n \"date\": datetime.now(ZoneInfo(\"Asia/Kolkata\")).isoformat(),\n \"model_config\": model_config.to_dict(),\n \"parameter_count\": model.parameter_count(),\n \"dataset\": token_manifest[\"dataset_source\"] + \" verified/ori nano\",\n \"token_count\": token_manifest[\"splits\"][\"train\"][\"tokens\"],\n \"tokenizer\": \"nano_8k\",\n \"seed\": train_config.seed,\n \"GPU\": torch.cuda.get_device_name(0),\n \"precision\": train_config.precision,\n \"context_length\": model_config.context_length,\n \"microbatch\": train_config.microbatch,\n \"gradient_accumulation\": train_config.gradient_accumulation,\n \"effective_batch_tokens\": tokens_per_update,\n \"optimizer\": \"AdamW\",\n \"learning_rate\": train_config.learning_rate,\n \"training_steps\": step,\n \"total_tokens_seen\": tokens_seen,\n \"training_loss\": latest_train,\n \"validation_loss\": latest_val,\n \"perplexity\": math.exp(min(latest_val, 20)),\n \"tokens_per_second\": tokens_seen / max(elapsed_total, 1e-9),\n \"peak_VRAM\": torch.cuda.max_memory_allocated(),\n \"start_checkpoint\": start_checkpoint,\n \"final_checkpoint\": checkpoint_path,\n \"result\": \"completed\",\n \"notes\": \"Initial nano experiment on 8K tokenizer and 5.15M-token corpus.\",\n }\n history = root / \"experiments.jsonl\"\n existing_ids = {\n json.loads(line)[\"experiment_id\"]\n for line in history.read_text(encoding=\"utf-8\").splitlines()\n if line.strip()\n }\n if EXPERIMENT_ID not in existing_ids:\n _append_jsonl(history, result)\n report = (\n \"# Nano experiment\\n\\n\"\n f\"- GPU: {result['GPU']}\\n\"\n f\"- Parameters: {result['parameter_count']:,}\\n\"\n f\"- Precision: {result['precision']}\\n\"\n f\"- Steps: {step:,}\\n\"\n f\"- Tokens seen: {tokens_seen:,}\\n\"\n f\"- Final train loss: {latest_train:.4f}\\n\"\n f\"- Final validation loss: {latest_val:.4f}\\n\"\n f\"- Validation perplexity: {result['perplexity']:.2f}\\n\"\n f\"- Peak allocated VRAM: {result['peak_VRAM']/2**30:.3f} GiB\\n\"\n f\"- Final checkpoint: {checkpoint_path}\\n\"\n )\n _atomic_text(root / \"reports/nano_experiment.md\", report)\n _status(\n root, step, latest_train, latest_val, checkpoint_path,\n f\"Nano training completed at step {step}\",\n )\n print(\n f\"COMPLETED {EXPERIMENT_ID}: train={latest_train:.4f}, \"\n f\"val={latest_val:.4f}, tokens={tokens_seen:,}\",\n flush=True,\n )\n return result\n", "src/odiagpt/generation.py": "\"\"\"Autoregressive OdiaGPT generation without library generate APIs.\"\"\"\n\nimport torch\nfrom torch import Tensor\n\nfrom .model import OdiaGPT\n\n\n@torch.inference_mode()\ndef generate_tokens(\n model: OdiaGPT,\n prompt_ids: list[int],\n max_new_tokens: int,\n eos_id: int,\n pad_id: int,\n unk_id: int,\n bos_id: int,\n temperature: float = 1.0,\n top_k: int | None = None,\n top_p: float | None = None,\n seed: int = 0,\n) -> list[int]:\n if not prompt_ids or max_new_tokens <= 0:\n raise ValueError(\"Prompt and max_new_tokens must be nonempty/positive\")\n if temperature < 0:\n raise ValueError(\"temperature must be nonnegative\")\n if top_k is not None and top_k <= 0:\n raise ValueError(\"top_k must be positive\")\n if top_p is not None and not 0 < top_p <= 1:\n raise ValueError(\"top_p must be in (0, 1]\")\n if any(i < 0 or i >= model.config.vocab_size for i in prompt_ids):\n raise ValueError(\"Prompt token ID out of vocabulary range\")\n device = next(model.parameters()).device\n was_training = model.training\n model.eval()\n ids = torch.tensor(prompt_ids, dtype=torch.long, device=device).unsqueeze(0)\n generator = torch.Generator(device=device).manual_seed(seed)\n try:\n for _ in range(max_new_tokens):\n context = ids[:, -model.config.context_length:]\n logits, _ = model(context)\n next_logits = logits[:, -1, :].float().clone()\n next_logits[:, [pad_id, unk_id, bos_id]] = -torch.inf\n if temperature == 0:\n next_id = next_logits.argmax(dim=-1, keepdim=True)\n else:\n next_logits /= temperature\n if top_k is not None:\n k = min(top_k, next_logits.size(-1))\n boundary = torch.topk(next_logits, k=k, dim=-1).values[:, -1:]\n next_logits = next_logits.masked_fill(next_logits < boundary, -torch.inf)\n if top_p is not None and top_p < 1:\n sorted_logits, sorted_indices = torch.sort(\n next_logits, descending=True, dim=-1\n )\n sorted_probs = torch.softmax(sorted_logits, dim=-1)\n cumulative = sorted_probs.cumsum(dim=-1)\n remove = cumulative - sorted_probs > top_p\n sorted_logits = sorted_logits.masked_fill(remove, -torch.inf)\n next_logits = torch.full_like(next_logits, -torch.inf).scatter(\n dim=-1, index=sorted_indices, src=sorted_logits\n )\n probs = torch.softmax(next_logits, dim=-1)\n if not torch.isfinite(probs).all():\n raise RuntimeError(\"Invalid sampling probabilities\")\n next_id = torch.multinomial(probs, num_samples=1, generator=generator)\n ids = torch.cat((ids, next_id), dim=1)\n if int(next_id.item()) == eos_id:\n break\n finally:\n model.train(was_training)\n return ids.squeeze(0).tolist()\n", "src/odiagpt/pilot_data.py": "\"\"\"Build a larger, resumable Odia corpus for the pilot experiment.\"\"\"\n\nfrom collections import Counter\nfrom dataclasses import asdict, dataclass\nfrom datetime import datetime, timezone\nfrom hashlib import sha256\nfrom pathlib import Path\nfrom statistics import mean, median\nimport json\nimport os\nimport tempfile\nimport unicodedata\n\nfrom datasets import load_dataset\n\nfrom .cleaning import CleaningConfig, clean_document, exact_fingerprint\nfrom .prepare_tokens import hash_file\n\n\n@dataclass(frozen=True)\nclass PilotCorpusConfig:\n target_clean_chars: int = 110_000_000\n max_stream_rows: int = 100_000\n max_document_chars: int = 20_000\n validation_fraction: float = 0.01\n split_seed: int = 42\n\n\ndef atomic_text(path: Path, value: str, overwrite: bool = False) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n if path.exists() and not overwrite:\n raise FileExistsError(path)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=path.parent,\n prefix=\".pilot_\", suffix=\".tmp\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n handle.write(value)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef _split_for_id(doc_id: str, seed: int, fraction: float) -> str:\n digest = sha256(f\"{seed}:{doc_id}\".encode(\"utf-8\")).digest()\n value = int.from_bytes(digest[:8], \"big\") / 2**64\n return \"validation\" if value < fraction else \"train\"\n\n\ndef _strip_boilerplate(raw: str, lines: set[str]) -> tuple[str, int]:\n parts = raw.splitlines()\n retained = [part for part in parts if part.strip() not in lines]\n return \"\\n\".join(retained), len(parts) - len(retained)\n\n\ndef build_pilot_corpus(root: Path, config: PilotCorpusConfig | None = None) -> dict:\n config = config or PilotCorpusConfig()\n if config.target_clean_chars <= 0 or config.max_stream_rows <= 0:\n raise ValueError(\"Invalid pilot corpus limits\")\n if not 0 < config.validation_fraction < 1:\n raise ValueError(\"Invalid validation fraction\")\n nano = json.loads((root / \"data/manifests/nano_corpus.json\").read_text(encoding=\"utf-8\"))\n cleaning = CleaningConfig(**nano[\"cleaning_config\"])\n boilerplate_path = root / \"data/metadata/boilerplate_lines_v1.json\"\n boilerplate_manifest = json.loads(boilerplate_path.read_text(encoding=\"utf-8\"))\n if boilerplate_manifest[\"nano_train_sha256\"] != nano[\"train_sha256\"]:\n raise RuntimeError(\"Boilerplate list came from a different nano train split\")\n boilerplate_lines = {item[\"text\"] for item in boilerplate_manifest[\"lines\"]}\n boilerplate_hash = hash_file(boilerplate_path)\n revision = nano[\"source_revision\"]\n train_path = root / \"data/processed/pilot_train.jsonl\"\n val_path = root / \"data/processed/pilot_val.jsonl\"\n manifest_path = root / \"data/manifests/pilot_corpus.json\"\n report_path = root / \"reports/pilot_data_quality_report.md\"\n journal_path = root / \"data/raw/pilot_accepted.jsonl\"\n progress_path = root / \"data/metadata/pilot_build_progress.json\"\n if manifest_path.is_file():\n result = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if result[\"build_config\"] != asdict(config):\n raise RuntimeError(\"Existing pilot corpus has a different config\")\n if result[\"source_revision\"] != revision:\n raise RuntimeError(\"Existing pilot corpus has a different source revision\")\n if result[\"boilerplate_manifest_sha256\"] != boilerplate_hash:\n raise RuntimeError(\"Existing pilot corpus used another boilerplate list\")\n if hash_file(train_path) != result[\"train_sha256\"]:\n raise RuntimeError(\"Existing pilot train hash mismatch\")\n if hash_file(val_path) != result[\"val_sha256\"]:\n raise RuntimeError(\"Existing pilot validation hash mismatch\")\n print(\"Existing pilot corpus verified; nothing overwritten.\", flush=True)\n return result\n if train_path.exists() or val_path.exists() or report_path.exists():\n raise RuntimeError(\"Partial final pilot outputs exist without manifest; inspect before retry\")\n if progress_path.exists():\n progress = json.loads(progress_path.read_text(encoding=\"utf-8\"))\n if progress[\"config\"] != asdict(config) or progress[\"revision\"] != revision or progress[\"boilerplate_sha256\"] != boilerplate_hash:\n raise RuntimeError(\"Saved pilot progress uses a different config or revision\")\n committed_rows = int(progress[\"committed_rows\"])\n source_types = Counter(progress[\"source_types\"])\n rejections = Counter(progress[\"rejections\"])\n nfc_count = int(progress[\"nfc_count\"])\n boilerplate_removed = int(progress[\"boilerplate_lines_removed\"])\n boilerplate_affected = int(progress[\"boilerplate_docs_affected\"])\n else:\n committed_rows = 0\n source_types = Counter()\n rejections = Counter()\n nfc_count = 0\n boilerplate_removed = 0\n boilerplate_affected = 0\n documents = []\n seen_ids = {}\n seen_fingerprints = {}\n maximum_journal_row = 0\n if journal_path.exists():\n with journal_path.open(\"r\", encoding=\"utf-8\") as handle:\n for line in handle:\n doc = json.loads(line)\n index = int(doc[\"stream_index\"])\n fingerprint = exact_fingerprint(doc[\"text\"])\n if doc[\"doc_id\"] in seen_ids or fingerprint in seen_fingerprints:\n raise RuntimeError(\"Duplicate in pilot journal\")\n seen_ids[doc[\"doc_id\"]] = (index, fingerprint)\n seen_fingerprints[fingerprint] = index\n documents.append(doc)\n maximum_journal_row = max(maximum_journal_row, index)\n total_chars = sum(len(doc[\"text\"]) for doc in documents)\n print(\n f\"Pilot resume: committed_rows={committed_rows}, \"\n f\"journal_docs={len(documents)}, cleaned_chars={total_chars:,}\",\n flush=True,\n )\n dataset = load_dataset(\n nano[\"source\"], nano[\"configuration\"], split=nano[\"split\"],\n revision=revision, streaming=True,\n )\n iterator = iter(dataset.skip(committed_rows))\n inspected = committed_rows\n journal_path.parent.mkdir(parents=True, exist_ok=True)\n with journal_path.open(\"a\", encoding=\"utf-8\") as journal:\n while inspected < config.max_stream_rows:\n try:\n row = next(iterator)\n except StopIteration:\n break\n inspected += 1\n raw = row.get(\"text\")\n doc_id = row.get(\"doc_id\")\n source_types[str(row.get(\"type\", \"unknown\"))] += 1\n if not isinstance(raw, str) or not isinstance(doc_id, str):\n rejections[\"missing_text_or_doc_id\"] += 1\n else:\n if unicodedata.is_normalized(\"NFC\", raw):\n nfc_count += 1\n raw, removed = _strip_boilerplate(raw, boilerplate_lines)\n boilerplate_removed += removed\n boilerplate_affected += int(removed > 0)\n cleaned = clean_document(raw, cleaning)\n if not cleaned.accepted:\n rejections[cleaned.rejection_reason or \"unknown\"] += 1\n elif len(cleaned.text) > config.max_document_chars:\n rejections[\"too_long_for_pilot\"] += 1\n else:\n fingerprint = exact_fingerprint(cleaned.text)\n if doc_id in seen_ids:\n old_index, old_fingerprint = seen_ids[doc_id]\n if old_index != inspected or old_fingerprint != fingerprint:\n rejections[\"duplicate_doc_id\"] += 1\n elif fingerprint in seen_fingerprints:\n rejections[\"exact_duplicate_text\"] += 1\n else:\n doc = {\n \"stream_index\": inspected, \"doc_id\": doc_id,\n \"type\": str(row.get(\"type\", \"unknown\")),\n \"text\": cleaned.text,\n }\n journal.write(json.dumps(doc, ensure_ascii=False) + \"\\n\")\n documents.append(doc)\n seen_ids[doc_id] = (inspected, fingerprint)\n seen_fingerprints[fingerprint] = inspected\n total_chars += len(cleaned.text)\n if inspected % 250 == 0:\n journal.flush()\n os.fsync(journal.fileno())\n atomic_text(progress_path, json.dumps({\n \"config\": asdict(config), \"revision\": revision,\n \"boilerplate_sha256\": boilerplate_hash,\n \"committed_rows\": inspected,\n \"source_types\": dict(source_types),\n \"rejections\": dict(rejections),\n \"nfc_count\": nfc_count,\n \"boilerplate_lines_removed\": boilerplate_removed,\n \"boilerplate_docs_affected\": boilerplate_affected,\n \"documents\": len(documents),\n \"cleaned_chars\": total_chars,\n }, indent=2) + \"\\n\", overwrite=True)\n print(\n f\"inspected={inspected:,} retained={len(documents):,} \"\n f\"clean_chars={total_chars:,}/{config.target_clean_chars:,}\",\n flush=True,\n )\n if total_chars >= config.target_clean_chars and inspected >= maximum_journal_row:\n break\n journal.flush()\n os.fsync(journal.fileno())\n if total_chars < config.target_clean_chars:\n raise RuntimeError(\n f\"Pilot target not reached: {total_chars:,} cleaned chars after \"\n f\"{inspected:,} streamed rows. Journal and progress remain for retry.\"\n )\n if not documents:\n raise RuntimeError(\"No pilot documents retained\")\n\n nano_train_ids = {\n json.loads(line)[\"doc_id\"]\n for line in Path(nano[\"train_path\"]).read_text(encoding=\"utf-8\").splitlines()\n if line.strip()\n }\n nano_val_ids = {\n json.loads(line)[\"doc_id\"]\n for line in Path(nano[\"val_path\"]).read_text(encoding=\"utf-8\").splitlines()\n if line.strip()\n }\n if nano_train_ids & nano_val_ids:\n raise RuntimeError(\"Smoke split IDs overlap\")\n train_docs = []\n val_docs = []\n for doc in documents:\n doc_id = doc[\"doc_id\"]\n if doc_id in nano_train_ids:\n destination = train_docs\n elif doc_id in nano_val_ids:\n destination = val_docs\n else:\n destination = (\n val_docs if _split_for_id(\n doc_id, config.split_seed, config.validation_fraction\n ) == \"validation\" else train_docs\n )\n destination.append({\n \"doc_id\": doc_id, \"type\": doc[\"type\"], \"text\": doc[\"text\"]\n })\n if not train_docs or not val_docs:\n raise RuntimeError(\"Pilot train or validation split is empty\")\n if {d[\"doc_id\"] for d in train_docs} & {d[\"doc_id\"] for d in val_docs}:\n raise RuntimeError(\"Pilot split IDs overlap\")\n for path, rows in ((train_path, train_docs), (val_path, val_docs)):\n atomic_text(\n path,\n \"\".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + \"\\n\" for row in rows),\n )\n lengths = [len(doc[\"text\"]) for doc in documents]\n ratios = [clean_document(doc[\"text\"], cleaning).stats.odia_script_ratio for doc in documents]\n report = [\n \"# Pilot Odia data quality report\", \"\",\n \"This is a prefix of the fixed verified/ori stream; it is not a random sample.\",\n f\"- Dataset: {nano['source']}, {nano['configuration']}/{nano['split']}\",\n f\"- Revision: {revision}\",\n f\"- Documents inspected: {inspected:,}\",\n f\"- Retained: {len(documents):,}\",\n f\"- Rejected: {sum(rejections.values()):,}\",\n f\"- Cleaned characters: {total_chars:,}\",\n f\"- Train / validation documents: {len(train_docs):,} / {len(val_docs):,}\",\n f\"- Length mean / median: {mean(lengths):.1f} / {median(lengths):.1f}\",\n f\"- Odia ratio mean / median: {mean(ratios):.4f} / {median(ratios):.4f}\",\n f\"- Raw NFC documents: {nfc_count:,}\",\n f\"- Exact boilerplate lines removed: {boilerplate_removed:,}\",\n f\"- Documents affected by boilerplate removal: {boilerplate_affected:,}\",\n f\"- Boilerplate list SHA256: {boilerplate_hash}\", \"\",\n \"## Rejection reasons\", \"\",\n ]\n report.extend(f\"- {reason}: {count:,}\" for reason, count in sorted(rejections.items()))\n report.extend([\"\", \"## Source types\", \"\"])\n report.extend(f\"- {kind}: {count:,}\" for kind, count in sorted(source_types.items()))\n report.extend([\"\", \"Exact duplicate text removal and document-level splitting precede token packing.\",\n \"Retained existing nano validation IDs remain in validation for this corpus.\", \"\"])\n atomic_text(report_path, \"\\n\".join(report))\n manifest = {\n \"created_utc\": datetime.now(timezone.utc).isoformat(),\n \"source\": nano[\"source\"],\n \"source_revision\": revision,\n \"configuration\": nano[\"configuration\"],\n \"split\": nano[\"split\"],\n \"build_config\": asdict(config),\n \"cleaning_config\": asdict(cleaning),\n \"boilerplate_manifest_sha256\": boilerplate_hash,\n \"boilerplate_lines_removed\": boilerplate_removed,\n \"boilerplate_docs_affected\": boilerplate_affected,\n \"documents_inspected\": inspected,\n \"documents_retained\": len(documents),\n \"rejections\": dict(rejections),\n \"cleaned_characters\": total_chars,\n \"train_documents\": len(train_docs),\n \"validation_documents\": len(val_docs),\n \"train_path\": str(train_path),\n \"val_path\": str(val_path),\n \"train_sha256\": hash_file(train_path),\n \"val_sha256\": hash_file(val_path),\n \"journal_path\": str(journal_path),\n \"journal_sha256\": hash_file(journal_path),\n }\n atomic_text(manifest_path, json.dumps(manifest, ensure_ascii=False, indent=2) + \"\\n\")\n print(\n f\"Pilot corpus saved: {len(train_docs):,} train, {len(val_docs):,} val, \"\n f\"{total_chars:,} cleaned chars\",\n flush=True,\n )\n return manifest\n", "src/odiagpt/prepare_pilot_tokens.py": "\"\"\"Prepare pilot train and validation binaries using the nano 8K tokenizer.\"\"\"\n\nfrom pathlib import Path\nimport json\nimport os\nimport tempfile\n\nimport sentencepiece as spm\n\nfrom .prepare_tokens import _write_split, hash_file\n\n\ndef prepare_pilot_tokens(root: Path) -> dict:\n corpus_path = root / \"data/manifests/pilot_corpus.json\"\n nano_corpus_path = root / \"data/manifests/nano_corpus.json\"\n tokenizer_path = root / \"tokenizer/nano_8k.json\"\n corpus = json.loads(corpus_path.read_text(encoding=\"utf-8\"))\n nano_corpus = json.loads(nano_corpus_path.read_text(encoding=\"utf-8\"))\n tokenizer = json.loads(tokenizer_path.read_text(encoding=\"utf-8\"))\n if tokenizer[\"corpus_sha256\"] != nano_corpus[\"train_sha256\"]:\n raise RuntimeError(\"8K tokenizer training corpus hash mismatch\")\n if corpus[\"source_revision\"] != nano_corpus[\"source_revision\"]:\n raise RuntimeError(\"Pilot and tokenizer source revisions differ\")\n boilerplate_path = root / \"data/metadata/boilerplate_lines_v1.json\"\n if hash_file(boilerplate_path) != corpus[\"boilerplate_manifest_sha256\"]:\n raise RuntimeError(\"Pilot boilerplate filter hash mismatch\")\n model_path = Path(tokenizer[\"model_path\"])\n if hash_file(model_path) != tokenizer[\"model_sha256\"]:\n raise RuntimeError(\"Tokenizer model hash mismatch\")\n processor = spm.SentencePieceProcessor(model_file=str(model_path))\n if processor.get_piece_size() != 8192:\n raise RuntimeError(\"8K tokenizer vocabulary mismatch\")\n directory = root / \"data/tokenized\"\n directory.mkdir(parents=True, exist_ok=True)\n manifest_path = directory / \"pilot_8k.json\"\n paths = {\n \"train\": directory / \"pilot_8k_train.bin\",\n \"validation\": directory / \"pilot_8k_val.bin\",\n }\n if manifest_path.exists():\n manifest = json.loads(manifest_path.read_text(encoding=\"utf-8\"))\n if manifest[\"tokenizer_sha256\"] != tokenizer[\"model_sha256\"]:\n raise RuntimeError(\"Existing pilot token data uses another tokenizer\")\n if manifest[\"corpus_manifest_sha256\"] != hash_file(corpus_path):\n raise RuntimeError(\"Existing pilot token data uses another corpus\")\n for name, path in paths.items():\n entry = manifest[\"splits\"][name]\n if not path.is_file() or hash_file(path) != entry[\"sha256\"]:\n raise RuntimeError(f\"Existing pilot binary mismatch: {path}\")\n print(\"Existing pilot token binaries verified; nothing overwritten.\", flush=True)\n return manifest\n if any(path.exists() for path in paths.values()):\n raise RuntimeError(\"Partial pilot binaries exist without manifest\")\n print(\"Tokenizing pilot train documents...\", flush=True)\n train = _write_split(\n Path(corpus[\"train_path\"]), paths[\"train\"], processor,\n corpus[\"train_documents\"], corpus[\"train_sha256\"],\n )\n print(f\"Pilot train tokens: {train['tokens']:,}\", flush=True)\n print(\"Tokenizing pilot validation documents...\", flush=True)\n validation = _write_split(\n Path(corpus[\"val_path\"]), paths[\"validation\"], processor,\n corpus[\"validation_documents\"], corpus[\"val_sha256\"],\n )\n print(f\"Pilot validation tokens: {validation['tokens']:,}\", flush=True)\n manifest = {\n \"name\": \"pilot_8k\",\n \"dtype\": \"uint16_le\",\n \"vocab_size\": processor.get_piece_size(),\n \"special_token_ids\": tokenizer[\"special_token_ids\"],\n \"document_boundaries\": \"BOS before and EOS after each document\",\n \"tokenizer_sha256\": tokenizer[\"model_sha256\"],\n \"tokenizer_training_corpus_sha256\": nano_corpus[\"train_sha256\"],\n \"corpus_manifest_sha256\": hash_file(corpus_path),\n \"boilerplate_manifest_sha256\": corpus[\"boilerplate_manifest_sha256\"],\n \"cleaning_config\": corpus[\"cleaning_config\"],\n \"split_seed\": corpus[\"build_config\"][\"split_seed\"],\n \"dataset_source\": corpus[\"source\"],\n \"dataset_configuration\": corpus[\"configuration\"],\n \"dataset_split\": corpus[\"split\"],\n \"dataset_revision\": corpus[\"source_revision\"],\n \"splits\": {\"train\": train, \"validation\": validation},\n }\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=directory,\n prefix=\".pilot_manifest_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n json.dump(manifest, handle, ensure_ascii=False, indent=2)\n handle.write(\"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, manifest_path)\n finally:\n temporary.unlink(missing_ok=True)\n return manifest\n", "src/odiagpt/pilot_training.py": "\"\"\"Train OdiaGPT pilot with mixed precision, evaluation, and resume.\"\"\"\n\nfrom datetime import datetime\nfrom pathlib import Path\nfrom time import monotonic\nfrom zoneinfo import ZoneInfo\nimport json\nimport math\nimport os\nimport random\nimport tempfile\n\nimport numpy as np\nimport torch\n\nfrom .checkpoint import load_latest_checkpoint, save_training_checkpoint\nfrom .config import ModelConfig\nfrom .dataset import PackedTokenData\nfrom .model import OdiaGPT\nfrom .prepare_tokens import hash_file\nfrom .train_config import TrainConfig\n\n\nEXPERIMENT_ID = \"pilot-30m-v1\"\n\n\ndef _atomic_text(path: Path, text: str) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n with tempfile.NamedTemporaryFile(\n mode=\"w\", encoding=\"utf-8\", dir=path.parent,\n prefix=\".training_\", delete=False,\n ) as handle:\n temporary = Path(handle.name)\n try:\n handle.write(text)\n handle.flush()\n os.fsync(handle.fileno())\n os.replace(temporary, path)\n finally:\n temporary.unlink(missing_ok=True)\n\n\ndef _append_jsonl(path: Path, record: dict) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n with path.open(\"a\", encoding=\"utf-8\") as handle:\n handle.write(json.dumps(record, ensure_ascii=False) + \"\\n\")\n handle.flush()\n os.fsync(handle.fileno())\n\n\ndef _status(\n root: Path, step: int, train_loss: float | None,\n val_loss: float | None, checkpoint: str | None, note: str,\n) -> None:\n stamp = datetime.now(ZoneInfo(\"Asia/Kolkata\")).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n text = (\n \"# Current OdiaGPT status\\n\\n\"\n f\"- Timestamp: {stamp}\\n\"\n \"- Current phase: Pilot experiment training\\n\"\n \"- Last successful cell: CELL 77 (pilot config and SDPA benchmark verified)\\n\"\n f\"- Latest runtime output: {note}\\n\"\n \"- Current GPU: Tesla T4 in MCP runtime (recheck after reconnect)\\n\"\n \"- Dataset status: Pilot 28,462,726 train / 261,148 validation tokens verified\\n\"\n \"- Tokenizer status: Nano 8K trained from scratch; reused for pilot\\n\"\n \"- Model status: OdiaGPT-30M, 31,465,984 parameters\\n\"\n f\"- Active experiment: {EXPERIMENT_ID}\\n\"\n f\"- Current training step: {step}\\n\"\n f\"- Latest train loss: {train_loss if train_loss is not None else 'None'}\\n\"\n f\"- Latest validation loss: {val_loss if val_loss is not None else 'None'}\\n\"\n f\"- Latest checkpoint: {checkpoint if checkpoint else 'None'}\\n\"\n \"- Blockers: None known\\n\"\n \"- Exact next action: Resume or continue pilot training to the configured stopping point.\\n\"\n )\n _atomic_text(root / \"status.md\", text)\n\n\ndef _make_optimizer(model: OdiaGPT, config: TrainConfig) -> torch.optim.Optimizer:\n decay = []\n no_decay = []\n for _, parameter in model.named_parameters():\n (decay if parameter.ndim >= 2 else no_decay).append(parameter)\n return torch.optim.AdamW(\n [\n {\"params\": decay, \"weight_decay\": config.weight_decay},\n {\"params\": no_decay, \"weight_decay\": 0.0},\n ],\n lr=config.learning_rate,\n betas=(config.beta1, config.beta2),\n )\n\n\ndef _schedule_factor(step: int, config: TrainConfig) -> float:\n if step < config.warmup_steps:\n return (step + 1) / config.warmup_steps\n span = max(1, config.max_steps - config.warmup_steps)\n progress = min(1.0, (step - config.warmup_steps) / span)\n floor = config.min_learning_rate / config.learning_rate\n return floor + 0.5 * (1 - floor) * (1 + math.cos(math.pi * progress))\n\n\n@torch.inference_mode()\ndef _evaluate(\n model: OdiaGPT, data: PackedTokenData, config: TrainConfig,\n autocast_dtype: torch.dtype | None,\n) -> float:\n model.eval()\n losses = []\n for index in range(config.eval_batches):\n x, y = data.validation_batch(index)\n with torch.amp.autocast(\n \"cuda\", dtype=autocast_dtype or torch.float16,\n enabled=autocast_dtype is not None,\n ):\n _, loss = model(x, y)\n if not torch.isfinite(loss):\n raise RuntimeError(\"Non-finite validation loss\")\n losses.append(float(loss.item()))\n model.train()\n return sum(losses) / len(losses)\n\n\ndef train_pilot(root: Path) -> dict:\n if not torch.cuda.is_available():\n raise RuntimeError(\"Pilot experiment requires a CUDA GPU\")\n if not os.path.ismount(\"/content/drive\"):\n raise RuntimeError(\"Google Drive must be mounted before training\")\n device = torch.device(\"cuda\")\n model_config = ModelConfig.from_yaml(root / \"configs/odia30m.yaml\")\n train_config = TrainConfig.from_yaml(root / \"configs/odia30m.yaml\")\n if train_config.precision == \"fp16\":\n autocast_dtype = torch.float16\n elif train_config.precision == \"bf16\":\n if not torch.cuda.is_bf16_supported():\n raise RuntimeError(\"BF16 not supported by runtime\")\n autocast_dtype = torch.bfloat16\n else:\n autocast_dtype = None\n token_manifest_path = root / \"data/tokenized/pilot_8k.json\"\n token_manifest = json.loads(token_manifest_path.read_text(encoding=\"utf-8\"))\n token_manifest_hash = hash_file(token_manifest_path)\n if model_config.vocab_size != token_manifest[\"vocab_size\"]:\n raise RuntimeError(\"Model and tokenizer vocabulary mismatch\")\n if token_manifest[\"splits\"][\"train\"][\"tokens\"] < 25_000_000:\n raise RuntimeError(\"Pilot corpus below the 25M-token target\")\n torch.manual_seed(train_config.seed)\n torch.cuda.manual_seed_all(train_config.seed)\n np.random.seed(train_config.seed)\n random.seed(train_config.seed)\n data = PackedTokenData(\n Path(token_manifest[\"splits\"][\"train\"][\"path\"]),\n token_manifest[\"splits\"][\"train\"][\"sha256\"],\n Path(token_manifest[\"splits\"][\"validation\"][\"path\"]),\n token_manifest[\"splits\"][\"validation\"][\"sha256\"],\n device, model_config.context_length, train_config.seed,\n train_config.eval_batches, train_config.microbatch,\n )\n model = OdiaGPT(model_config).to(device)\n optimizer = _make_optimizer(model, train_config)\n scheduler = torch.optim.lr_scheduler.LambdaLR(\n optimizer, lr_lambda=lambda step: _schedule_factor(step, train_config)\n )\n scaler = torch.amp.GradScaler(\"cuda\", enabled=train_config.precision == \"fp16\")\n tokens_per_update = (\n train_config.microbatch * train_config.gradient_accumulation\n * model_config.context_length\n )\n run_dir = root / \"runs\" / EXPERIMENT_ID\n run_dir.mkdir(parents=True, exist_ok=True)\n run_meta_path = run_dir / \"config.json\"\n run_meta = {\n \"experiment_id\": EXPERIMENT_ID,\n \"model_config\": model_config.to_dict(),\n \"training_config\": train_config.to_dict(),\n \"token_manifest_sha256\": token_manifest_hash,\n \"tokenizer_sha256\": token_manifest[\"tokenizer_sha256\"],\n \"train_binary_sha256\": token_manifest[\"splits\"][\"train\"][\"sha256\"],\n \"validation_binary_sha256\": token_manifest[\"splits\"][\"validation\"][\"sha256\"],\n \"parameter_count\": model.parameter_count(),\n \"tokens_per_update\": tokens_per_update,\n \"gpu\": torch.cuda.get_device_name(0),\n }\n if run_meta_path.exists():\n existing = json.loads(run_meta_path.read_text(encoding=\"utf-8\"))\n if existing != run_meta:\n raise RuntimeError(\"Saved run configuration differs; use another experiment ID\")\n else:\n _atomic_text(run_meta_path, json.dumps(run_meta, ensure_ascii=False, indent=2) + \"\\n\")\n checkpoint = load_latest_checkpoint(root, EXPERIMENT_ID)\n step = 0\n tokens_seen = 0\n best_val = float(\"inf\")\n latest_val = None\n latest_train = None\n elapsed_before = 0.0\n start_checkpoint = None\n checkpoint_path = None\n if checkpoint is not None:\n saved, pointer = checkpoint\n if saved[\"model_config\"] != model_config.to_dict():\n raise RuntimeError(\"Checkpoint model config mismatch\")\n if saved[\"training_config\"] != train_config.to_dict():\n raise RuntimeError(\"Checkpoint training config mismatch\")\n if saved[\"token_manifest_sha256\"] != token_manifest_hash:\n raise RuntimeError(\"Checkpoint token data mismatch\")\n model.load_state_dict(saved[\"model\"])\n optimizer.load_state_dict(saved[\"optimizer\"])\n scheduler.load_state_dict(saved[\"scheduler\"])\n scaler.load_state_dict(saved[\"scaler\"])\n torch.set_rng_state(saved[\"torch_rng_state\"])\n torch.cuda.set_rng_state_all(saved[\"cuda_rng_states\"])\n np.random.set_state(saved[\"numpy_rng_state\"])\n random.setstate(saved[\"python_rng_state\"])\n data.train_generator.set_state(saved[\"sampler_rng_state\"])\n step = int(saved[\"step\"])\n tokens_seen = int(saved[\"tokens_seen\"])\n best_val = float(saved[\"best_validation_loss\"])\n latest_val = saved[\"latest_validation_loss\"]\n latest_train = saved[\"latest_train_loss\"]\n elapsed_before = float(saved[\"elapsed_seconds\"])\n start_checkpoint = pointer[\"path\"]\n checkpoint_path = pointer[\"path\"]\n print(f\"Resumed step {step} from {checkpoint_path}\", flush=True)\n log_path = root / \"logs\" / f\"{EXPERIMENT_ID}.jsonl\"\n if step == 0:\n if log_path.exists() and log_path.stat().st_size:\n raise RuntimeError(\n \"Log exists without a checkpoint; inspect before restarting experiment\"\n )\n latest_val = _evaluate(model, data, train_config, autocast_dtype)\n best_val = latest_val\n _append_jsonl(log_path, {\n \"experiment_id\": EXPERIMENT_ID, \"step\": 0,\n \"validation_loss\": latest_val,\n \"validation_perplexity\": math.exp(min(latest_val, 20)),\n \"tokens_seen\": 0, \"event\": \"baseline\",\n })\n print(f\"step=0 validation_loss={latest_val:.4f}\", flush=True)\n print(\n f\"Training {EXPERIMENT_ID}: {model.parameter_count():,} parameters, \"\n f\"{tokens_per_update:,} tokens/update, max_steps={train_config.max_steps}, \"\n f\"precision={train_config.precision}, gpu={torch.cuda.get_device_name(0)}\",\n flush=True,\n )\n _status(root, step, latest_train, latest_val, checkpoint_path, \"Pilot training active\")\n model.train()\n torch.cuda.reset_peak_memory_stats()\n session_start = monotonic()\n last_log_time = session_start\n last_log_step = step\n smoothed = latest_train\n skipped_attempts = 0\n while step < train_config.max_steps:\n optimizer.zero_grad(set_to_none=True)\n micro_losses = []\n for _ in range(train_config.gradient_accumulation):\n x, y = data.train_batch(train_config.microbatch)\n with torch.amp.autocast(\n \"cuda\", dtype=autocast_dtype or torch.float16,\n enabled=autocast_dtype is not None,\n ):\n _, loss = model(x, y)\n if not torch.isfinite(loss):\n raise RuntimeError(f\"Non-finite train loss before step {step+1}\")\n micro_losses.append(float(loss.detach().item()))\n scaler.scale(loss / train_config.gradient_accumulation).backward()\n scaler.unscale_(optimizer)\n norm = torch.nn.utils.clip_grad_norm_(\n model.parameters(), train_config.gradient_clip\n )\n old_scale = scaler.get_scale()\n scaler.step(optimizer)\n scaler.update()\n if not torch.isfinite(norm) or scaler.get_scale() < old_scale:\n skipped_attempts += 1\n print(f\"FP16 overflow; update skipped ({skipped_attempts})\", flush=True)\n if skipped_attempts > 20:\n raise RuntimeError(\"Repeated FP16 overflow; stop for diagnosis\")\n continue\n skipped_attempts = 0\n used_lr = optimizer.param_groups[0][\"lr\"]\n scheduler.step()\n step += 1\n tokens_seen += tokens_per_update\n latest_train = sum(micro_losses) / len(micro_losses)\n smoothed = (\n latest_train if smoothed is None\n else 0.95 * smoothed + 0.05 * latest_train\n )\n if step % train_config.eval_interval == 0 or step == train_config.max_steps:\n latest_val = _evaluate(model, data, train_config, autocast_dtype)\n best_val = min(best_val, latest_val)\n if step % train_config.log_interval == 0 or step == 1:\n torch.cuda.synchronize()\n now = monotonic()\n interval_tokens = (step - last_log_step) * tokens_per_update\n speed = interval_tokens / max(now - last_log_time, 1e-9)\n event = {\n \"experiment_id\": EXPERIMENT_ID,\n \"step\": step,\n \"train_loss\": latest_train,\n \"smoothed_loss\": smoothed,\n \"validation_loss\": latest_val,\n \"validation_perplexity\": (\n math.exp(min(latest_val, 20)) if latest_val is not None else None\n ),\n \"learning_rate\": used_lr,\n \"gradient_norm\": float(norm),\n \"tokens_per_second\": speed,\n \"tokens_seen\": tokens_seen,\n \"elapsed_seconds\": elapsed_before + now - session_start,\n \"gpu_peak_allocated_bytes\": torch.cuda.max_memory_allocated(),\n \"gpu_peak_reserved_bytes\": torch.cuda.max_memory_reserved(),\n }\n _append_jsonl(log_path, event)\n print(\n f\"step={step:4d} train={latest_train:.4f} \"\n f\"val={latest_val:.4f} lr={used_lr:.2e} \"\n f\"tokens/s={speed:.0f} seen={tokens_seen:,}\",\n flush=True,\n )\n last_log_time, last_log_step = now, step\n _status(\n root, step, latest_train, latest_val, checkpoint_path,\n f\"step {step}; train {latest_train:.4f}; validation {latest_val:.4f}\",\n )\n stop_for_tokens = (\n train_config.max_tokens is not None\n and tokens_seen >= train_config.max_tokens\n )\n checkpoint_due = (\n step % train_config.checkpoint_interval == 0\n or step == train_config.max_steps or stop_for_tokens\n )\n if checkpoint_due:\n payload = {\n \"experiment_id\": EXPERIMENT_ID,\n \"step\": step,\n \"tokens_seen\": tokens_seen,\n \"model\": model.state_dict(),\n \"optimizer\": optimizer.state_dict(),\n \"scheduler\": scheduler.state_dict(),\n \"scaler\": scaler.state_dict(),\n \"model_config\": model_config.to_dict(),\n \"training_config\": train_config.to_dict(),\n \"token_manifest_sha256\": token_manifest_hash,\n \"best_validation_loss\": best_val,\n \"latest_validation_loss\": latest_val,\n \"latest_train_loss\": latest_train,\n \"elapsed_seconds\": elapsed_before + monotonic() - session_start,\n \"torch_rng_state\": torch.get_rng_state(),\n \"cuda_rng_states\": torch.cuda.get_rng_state_all(),\n \"numpy_rng_state\": np.random.get_state(),\n \"python_rng_state\": random.getstate(),\n \"sampler_rng_state\": data.train_generator.get_state(),\n }\n pointer = save_training_checkpoint(root, EXPERIMENT_ID, payload)\n checkpoint_path = pointer[\"path\"]\n print(f\"checkpoint step={step}: {checkpoint_path}\", flush=True)\n _status(\n root, step, latest_train, latest_val, checkpoint_path,\n f\"checkpoint saved at step {step}\",\n )\n if stop_for_tokens:\n break\n elapsed_total = elapsed_before + monotonic() - session_start\n result = {\n \"experiment_id\": EXPERIMENT_ID,\n \"date\": datetime.now(ZoneInfo(\"Asia/Kolkata\")).isoformat(),\n \"model_config\": model_config.to_dict(),\n \"parameter_count\": model.parameter_count(),\n \"dataset\": token_manifest[\"dataset_source\"] + \" verified/ori pilot\",\n \"token_count\": token_manifest[\"splits\"][\"train\"][\"tokens\"],\n \"tokenizer\": \"nano_8k\",\n \"seed\": train_config.seed,\n \"GPU\": torch.cuda.get_device_name(0),\n \"precision\": train_config.precision,\n \"context_length\": model_config.context_length,\n \"microbatch\": train_config.microbatch,\n \"gradient_accumulation\": train_config.gradient_accumulation,\n \"effective_batch_tokens\": tokens_per_update,\n \"optimizer\": \"AdamW\",\n \"learning_rate\": train_config.learning_rate,\n \"training_steps\": step,\n \"total_tokens_seen\": tokens_seen,\n \"training_loss\": latest_train,\n \"validation_loss\": latest_val,\n \"perplexity\": math.exp(min(latest_val, 20)),\n \"tokens_per_second\": tokens_seen / max(elapsed_total, 1e-9),\n \"peak_VRAM\": torch.cuda.max_memory_allocated(),\n \"start_checkpoint\": start_checkpoint,\n \"final_checkpoint\": checkpoint_path,\n \"result\": \"completed\",\n \"notes\": \"30M pilot on 28.46M-token cleaned corpus with SDPA.\",\n }\n history = root / \"experiments.jsonl\"\n existing_ids = {\n json.loads(line)[\"experiment_id\"]\n for line in history.read_text(encoding=\"utf-8\").splitlines()\n if line.strip()\n }\n if EXPERIMENT_ID not in existing_ids:\n _append_jsonl(history, result)\n report = (\n \"# Pilot experiment\\n\\n\"\n f\"- GPU: {result['GPU']}\\n\"\n f\"- Parameters: {result['parameter_count']:,}\\n\"\n f\"- Precision: {result['precision']}\\n\"\n f\"- Steps: {step:,}\\n\"\n f\"- Tokens seen: {tokens_seen:,}\\n\"\n f\"- Final train loss: {latest_train:.4f}\\n\"\n f\"- Final validation loss: {latest_val:.4f}\\n\"\n f\"- Validation perplexity: {result['perplexity']:.2f}\\n\"\n f\"- Peak allocated VRAM: {result['peak_VRAM']/2**30:.3f} GiB\\n\"\n f\"- Final checkpoint: {checkpoint_path}\\n\"\n )\n _atomic_text(root / \"reports/pilot_report.md\", report)\n _status(\n root, step, latest_train, latest_val, checkpoint_path,\n f\"Pilot training completed at step {step}\",\n )\n print(\n f\"COMPLETED {EXPERIMENT_ID}: train={latest_train:.4f}, \"\n f\"val={latest_val:.4f}, tokens={tokens_seen:,}\",\n flush=True,\n )\n return result\n"}