Title: Validate non-empty data_files
Author: albertvillanova (MEMBER)
State: closed
Created: 2023-04-27T09:51:36Z

This PR adds validation of `data_files`, so that they are non-empty (str, list, or dict) or `None` (default).

See: https://github.com/huggingface/datasets/pull/5787#discussion_r1178862327

Comments:
_The documentation is not available anymore as the PR was closed or merged._

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007818 / 0.011353 (-0.003535) | 0.005456 / 0.011008 (-0.005552) | 0.114685 / 0.038508 (0.076177) | 0.038398 / 0.023109 (0.015289) | 0.351289 / 0.275898 (0.075391) | 0.389170 / 0.323480 (0.065690) | 0.006213 / 0.007986 (-0.001773) | 0.005796 / 0.004328 (0.001467) | 0.085315 / 0.004250 (0.081065) | 0.049251 / 0.037052 (0.012198) | 0.368119 / 0.258489 (0.109630) | 0.394725 / 0.293841 (0.100884) | 0.040390 / 0.128546 (-0.088157) | 0.014076 / 0.075646 (-0.061570) | 0.393771 / 0.419271 (-0.025500) | 0.058929 / 0.043533 (0.015397) | 0.349526 / 0.255139 (0.094387) | 0.378409 / 0.283200 (0.095210) | 0.114354 / 0.141683 (-0.027329) | 1.749244 / 1.452155 (0.297089) | 1.847946 / 1.492716 (0.355229) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.241648 / 0.018006 (0.223641) | 0.468419 / 0.000490 (0.467929) | 0.004311 / 0.000200 (0.004111) | 0.000091 / 0.000054 (0.000036) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029978 / 0.037411 (-0.007433) | 0.121832 / 0.014526 (0.107306) | 0.133516 / 0.176557 (-0.043041) | 0.199174 / 0.737135 (-0.537961) | 0.138181 / 0.296338 (-0.158158) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.478346 / 0.215209 (0.263137) | 4.723967 / 2.077655 (2.646312) | 2.107724 / 1.504120 (0.603604) | 1.874810 / 1.541195 (0.333615) | 1.911568 / 1.468490 (0.443078) | 0.800966 / 4.584777 (-3.783811) | 4.399032 / 3.745712 (0.653320) | 2.346160 / 5.269862 (-2.923702) | 1.506673 / 4.565676 (-3.059004) | 0.099119 / 0.424275 (-0.325156) | 0.014055 / 0.007607 (0.006448) | 0.582419 / 0.226044 (0.356375) | 5.789147 / 2.268929 (3.520218) | 2.632443 / 55.444624 (-52.812182) | 2.217630 / 6.876477 (-4.658846) | 2.337709 / 2.142072 (0.195637) | 0.995345 / 4.805227 (-3.809882) | 0.200040 / 6.500664 (-6.300624) | 0.076855 / 0.075469 (0.001386) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.386104 / 1.841788 (-0.455683) | 17.109772 / 8.074308 (9.035464) | 16.147612 / 10.191392 (5.956220) | 0.162846 / 0.680424 (-0.517577) | 0.020692 / 0.534201 (-0.513509) | 0.495752 / 0.579283 (-0.083531) | 0.475715 / 0.434364 (0.041351) | 0.619826 / 0.540337 (0.079488) | 0.720745 / 1.386936 (-0.666191) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008255 / 0.011353 (-0.003098) | 0.006118 / 0.011008 (-0.004890) | 0.088004 / 0.038508 (0.049496) | 0.039225 / 0.023109 (0.016116) | 0.399290 / 0.275898 (0.123392) | 0.432272 / 0.323480 (0.108792) | 0.007382 / 0.007986 (-0.000603) | 0.004576 / 0.004328 (0.000248) | 0.086511 / 0.004250 (0.082260) | 0.050472 / 0.037052 (0.013420) | 0.404160 / 0.258489 (0.145671) | 0.445356 / 0.293841 (0.151515) | 0.041549 / 0.128546 (-0.086997) | 0.014148 / 0.075646 (-0.061498) | 0.101697 / 0.419271 (-0.317574) | 0.057474 / 0.043533 (0.013941) | 0.395093 / 0.255139 (0.139954) | 0.418613 / 0.283200 (0.135414) | 0.123217 / 0.141683 (-0.018466) | 1.726146 / 1.452155 (0.273991) | 1.852746 / 1.492716 (0.360029) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.256876 / 0.018006 (0.238870) | 0.476336 / 0.000490 (0.475846) | 0.000465 / 0.000200 (0.000265) | 0.000068 / 0.000054 (0.000013) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034304 / 0.037411 (-0.003107) | 0.132617 / 0.014526 (0.118091) | 0.141712 / 0.176557 (-0.034845) | 0.198101 / 0.737135 (-0.539034) | 0.150877 / 0.296338 (-0.145461) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.504717 / 0.215209 (0.289508) | 5.035060 / 2.077655 (2.957405) | 2.494812 / 1.504120 (0.990692) | 2.306601 / 1.541195 (0.765406) | 2.481860 / 1.468490 (1.013370) | 0.826041 / 4.584777 (-3.758736) | 4.414748 / 3.745712 (0.669036) | 2.417899 / 5.269862 (-2.851963) | 1.574548 / 4.565676 (-2.991128) | 0.101712 / 0.424275 (-0.322563) | 0.014388 / 0.007607 (0.006781) | 0.616674 / 0.226044 (0.390630) | 6.180382 / 2.268929 (3.911453) | 2.969110 / 55.444624 (-52.475514) | 2.574383 / 6.876477 (-4.302094) | 2.711008 / 2.142072 (0.568935) | 0.997679 / 4.805227 (-3.807548) | 0.201241 / 6.500664 (-6.299423) | 0.076132 / 0.075469 (0.000663) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.542704 / 1.841788 (-0.299084) | 17.610700 / 8.074308 (9.536392) | 16.152973 / 10.191392 (5.961581) | 0.166040 / 0.680424 (-0.514384) | 0.020286 / 0.534201 (-0.513915) | 0.506724 / 0.579283 (-0.072559) | 0.484348 / 0.434364 (0.049984) | 0.606524 / 0.540337 (0.066187) | 0.734997 / 1.386936 (-0.651939) |

</details>
</details>

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