Minor tqdm fixes
State: closed

`GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560). 

Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎)

_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) | 0.006479 / 0.011353 (-0.004874) | 0.004592 / 0.011008 (-0.006416) | 0.097239 / 0.038508 (0.058731) | 0.028609 / 0.023109 (0.005499) | 0.309225 / 0.275898 (0.033327) | 0.340015 / 0.323480 (0.016535) | 0.004857 / 0.007986 (-0.003129) | 0.004649 / 0.004328 (0.000320) | 0.074770 / 0.004250 (0.070520) | 0.038351 / 0.037052 (0.001299) | 0.313360 / 0.258489 (0.054871) | 0.350256 / 0.293841 (0.056416) | 0.030770 / 0.128546 (-0.097776) | 0.011591 / 0.075646 (-0.064055) | 0.322444 / 0.419271 (-0.096828) | 0.043704 / 0.043533 (0.000171) | 0.311790 / 0.255139 (0.056651) | 0.339183 / 0.283200 (0.055984) | 0.088041 / 0.141683 (-0.053642) | 1.490649 / 1.452155 (0.038494) | 1.561789 / 1.492716 (0.069072) |

### 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) | 0.208984 / 0.018006 (0.190978) | 0.406105 / 0.000490 (0.405616) | 0.003152 / 0.000200 (0.002952) | 0.000074 / 0.000054 (0.000019) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022622 / 0.037411 (-0.014790) | 0.095819 / 0.014526 (0.081294) | 0.105132 / 0.176557 (-0.071424) | 0.165684 / 0.737135 (-0.571451) | 0.106706 / 0.296338 (-0.189632) |

### 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) | 0.426126 / 0.215209 (0.210917) | 4.233864 / 2.077655 (2.156209) | 1.918727 / 1.504120 (0.414607) | 1.729905 / 1.541195 (0.188710) | 1.760342 / 1.468490 (0.291852) | 0.695449 / 4.584777 (-3.889328) | 3.413531 / 3.745712 (-0.332181) | 1.904557 / 5.269862 (-3.365305) | 1.270604 / 4.565676 (-3.295072) | 0.083018 / 0.424275 (-0.341257) | 0.012760 / 0.007607 (0.005152) | 0.523991 / 0.226044 (0.297947) | 5.236132 / 2.268929 (2.967204) | 2.360959 / 55.444624 (-53.083665) | 1.996533 / 6.876477 (-4.879943) | 2.072934 / 2.142072 (-0.069138) | 0.804133 / 4.805227 (-4.001094) | 0.150976 / 6.500664 (-6.349688) | 0.065503 / 0.075469 (-0.009966) |

### 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) | 1.211828 / 1.841788 (-0.629960) | 13.657743 / 8.074308 (5.583435) | 13.887148 / 10.191392 (3.695756) | 0.145996 / 0.680424 (-0.534428) | 0.016562 / 0.534201 (-0.517639) | 0.380359 / 0.579283 (-0.198924) | 0.388698 / 0.434364 (-0.045666) | 0.440373 / 0.540337 (-0.099965) | 0.531753 / 1.386936 (-0.855183) |

</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) | 0.006444 / 0.011353 (-0.004909) | 0.004569 / 0.011008 (-0.006439) | 0.076239 / 0.038508 (0.037731) | 0.028462 / 0.023109 (0.005352) | 0.365540 / 0.275898 (0.089642) | 0.398242 / 0.323480 (0.074762) | 0.005785 / 0.007986 (-0.002200) | 0.003346 / 0.004328 (-0.000982) | 0.076296 / 0.004250 (0.072046) | 0.039853 / 0.037052 (0.002800) | 0.367684 / 0.258489 (0.109195) | 0.409570 / 0.293841 (0.115730) | 0.030536 / 0.128546 (-0.098010) | 0.011534 / 0.075646 (-0.064112) | 0.084962 / 0.419271 (-0.334309) | 0.042708 / 0.043533 (-0.000825) | 0.344058 / 0.255139 (0.088919) | 0.389096 / 0.283200 (0.105897) | 0.090559 / 0.141683 (-0.051124) | 1.507101 / 1.452155 (0.054946) | 1.563977 / 1.492716 (0.071260) |

### 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) | 0.228740 / 0.018006 (0.210734) | 0.396890 / 0.000490 (0.396400) | 0.000392 / 0.000200 (0.000192) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025052 / 0.037411 (-0.012360) | 0.099951 / 0.014526 (0.085426) | 0.106847 / 0.176557 (-0.069710) | 0.156666 / 0.737135 (-0.580469) | 0.110344 / 0.296338 (-0.185994) |

### 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) | 0.442363 / 0.215209 (0.227154) | 4.429571 / 2.077655 (2.351917) | 2.076501 / 1.504120 (0.572381) | 1.875226 / 1.541195 (0.334031) | 1.909093 / 1.468490 (0.440603) | 0.703047 / 4.584777 (-3.881730) | 3.457036 / 3.745712 (-0.288676) | 2.866648 / 5.269862 (-2.403214) | 1.524430 / 4.565676 (-3.041246) | 0.083687 / 0.424275 (-0.340588) | 0.012251 / 0.007607 (0.004643) | 0.543945 / 0.226044 (0.317901) | 5.440559 / 2.268929 (3.171630) | 2.522924 / 55.444624 (-52.921700) | 2.188770 / 6.876477 (-4.687707) | 2.249632 / 2.142072 (0.107559) | 0.813499 / 4.805227 (-3.991728) | 0.152861 / 6.500664 (-6.347803) | 0.067189 / 0.075469 (-0.008280) |

### 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) | 1.284255 / 1.841788 (-0.557533) | 14.207864 / 8.074308 (6.133556) | 14.279691 / 10.191392 (4.088299) | 0.167027 / 0.680424 (-0.513396) | 0.016455 / 0.534201 (-0.517746) | 0.380798 / 0.579283 (-0.198485) | 0.390013 / 0.434364 (-0.044351) | 0.445493 / 0.540337 (-0.094845) | 0.526278 / 1.386936 (-0.860658) |

</details>
</details>

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