Fix inferring module for unsupported data files
State: closed

This PR raises a FileNotFoundError instead:
```
FileNotFoundError: No (supported) data files or dataset script found in <dataset_name>
```

Fix #5785.

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

I think you can revert the last commit - it should fail if data_files={} IMO

The validation of non-empty data_files is addressed in this PR:
- #5802

<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) | 0.008622 / 0.011353 (-0.002730) | 0.005970 / 0.011008 (-0.005038) | 0.117797 / 0.038508 (0.079289) | 0.040955 / 0.023109 (0.017846) | 0.419538 / 0.275898 (0.143640) | 0.455816 / 0.323480 (0.132336) | 0.006481 / 0.007986 (-0.001505) | 0.004507 / 0.004328 (0.000178) | 0.089073 / 0.004250 (0.084822) | 0.052389 / 0.037052 (0.015337) | 0.420053 / 0.258489 (0.161564) | 0.466886 / 0.293841 (0.173045) | 0.042660 / 0.128546 (-0.085886) | 0.014673 / 0.075646 (-0.060973) | 0.411229 / 0.419271 (-0.008042) | 0.076993 / 0.043533 (0.033460) | 0.431693 / 0.255139 (0.176554) | 0.446283 / 0.283200 (0.163084) | 0.131408 / 0.141683 (-0.010275) | 1.820339 / 1.452155 (0.368184) | 1.952946 / 1.492716 (0.460230) |

### 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) | 0.246543 / 0.018006 (0.228537) | 0.489806 / 0.000490 (0.489317) | 0.013999 / 0.000200 (0.013800) | 0.000323 / 0.000054 (0.000269) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032541 / 0.037411 (-0.004870) | 0.130569 / 0.014526 (0.116043) | 0.139630 / 0.176557 (-0.036926) | 0.217018 / 0.737135 (-0.520118) | 0.147914 / 0.296338 (-0.148425) |

### 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) | 0.494767 / 0.215209 (0.279558) | 4.949313 / 2.077655 (2.871658) | 2.277023 / 1.504120 (0.772903) | 2.036677 / 1.541195 (0.495482) | 2.064461 / 1.468490 (0.595970) | 0.842484 / 4.584777 (-3.742293) | 4.720646 / 3.745712 (0.974934) | 4.025673 / 5.269862 (-1.244189) | 2.198606 / 4.565676 (-2.367070) | 0.103042 / 0.424275 (-0.321233) | 0.014794 / 0.007607 (0.007187) | 0.617867 / 0.226044 (0.391822) | 6.197146 / 2.268929 (3.928218) | 2.804927 / 55.444624 (-52.639697) | 2.426420 / 6.876477 (-4.450057) | 2.515182 / 2.142072 (0.373109) | 1.008098 / 4.805227 (-3.797129) | 0.204982 / 6.500664 (-6.295682) | 0.078643 / 0.075469 (0.003174) |

### 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) | 1.490790 / 1.841788 (-0.350997) | 17.268042 / 8.074308 (9.193734) | 17.129647 / 10.191392 (6.938255) | 0.170351 / 0.680424 (-0.510073) | 0.021317 / 0.534201 (-0.512884) | 0.517068 / 0.579283 (-0.062215) | 0.500200 / 0.434364 (0.065836) | 0.641974 / 0.540337 (0.101637) | 0.763984 / 1.386936 (-0.622952) |

</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) | 0.008358 / 0.011353 (-0.002995) | 0.005710 / 0.011008 (-0.005298) | 0.091077 / 0.038508 (0.052569) | 0.040413 / 0.023109 (0.017303) | 0.416634 / 0.275898 (0.140736) | 0.451122 / 0.323480 (0.127642) | 0.006417 / 0.007986 (-0.001569) | 0.004360 / 0.004328 (0.000032) | 0.089543 / 0.004250 (0.085292) | 0.051137 / 0.037052 (0.014085) | 0.420228 / 0.258489 (0.161739) | 0.458649 / 0.293841 (0.164808) | 0.041828 / 0.128546 (-0.086718) | 0.014268 / 0.075646 (-0.061379) | 0.105301 / 0.419271 (-0.313970) | 0.058931 / 0.043533 (0.015398) | 0.413445 / 0.255139 (0.158306) | 0.443882 / 0.283200 (0.160682) | 0.124946 / 0.141683 (-0.016737) | 1.842259 / 1.452155 (0.390104) | 1.948162 / 1.492716 (0.455445) |

### 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) | 0.235799 / 0.018006 (0.217792) | 0.487667 / 0.000490 (0.487177) | 0.001112 / 0.000200 (0.000912) | 0.000094 / 0.000054 (0.000039) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034233 / 0.037411 (-0.003178) | 0.136593 / 0.014526 (0.122068) | 0.145598 / 0.176557 (-0.030959) | 0.206545 / 0.737135 (-0.530590) | 0.150781 / 0.296338 (-0.145558) |

### 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) | 0.522345 / 0.215209 (0.307136) | 5.192092 / 2.077655 (3.114438) | 2.543182 / 1.504120 (1.039062) | 2.285212 / 1.541195 (0.744018) | 2.312803 / 1.468490 (0.844313) | 0.859334 / 4.584777 (-3.725443) | 4.620235 / 3.745712 (0.874523) | 3.964060 / 5.269862 (-1.305802) | 2.046347 / 4.565676 (-2.519330) | 0.105284 / 0.424275 (-0.318991) | 0.015051 / 0.007607 (0.007444) | 0.646530 / 0.226044 (0.420485) | 6.386396 / 2.268929 (4.117467) | 3.131833 / 55.444624 (-52.312791) | 2.761898 / 6.876477 (-4.114579) | 2.833216 / 2.142072 (0.691143) | 1.026024 / 4.805227 (-3.779204) | 0.206776 / 6.500664 (-6.293888) | 0.078845 / 0.075469 (0.003376) |

### 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) | 1.580851 / 1.841788 (-0.260937) | 17.826213 / 8.074308 (9.751905) | 16.929460 / 10.191392 (6.738068) | 0.232483 / 0.680424 (-0.447941) | 0.021123 / 0.534201 (-0.513078) | 0.522196 / 0.579283 (-0.057087) | 0.503495 / 0.434364 (0.069131) | 0.622777 / 0.540337 (0.082440) | 0.753272 / 1.386936 (-0.633664) |

</details>
</details>

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