Title: Disable `tqdm` bars in non-interactive environments
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2024-01-29T15:18:21Z

Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default).

For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6627). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.004944 / 0.011353 (-0.006409) | 0.003279 / 0.011008 (-0.007729) | 0.063041 / 0.038508 (0.024533) | 0.029888 / 0.023109 (0.006779) | 0.259138 / 0.275898 (-0.016760) | 0.276907 / 0.323480 (-0.046573) | 0.004015 / 0.007986 (-0.003970) | 0.002647 / 0.004328 (-0.001682) | 0.048944 / 0.004250 (0.044693) | 0.039412 / 0.037052 (0.002360) | 0.278069 / 0.258489 (0.019580) | 0.299139 / 0.293841 (0.005298) | 0.027272 / 0.128546 (-0.101274) | 0.010445 / 0.075646 (-0.065202) | 0.206925 / 0.419271 (-0.212347) | 0.035589 / 0.043533 (-0.007944) | 0.256805 / 0.255139 (0.001666) | 0.275128 / 0.283200 (-0.008072) | 0.017888 / 0.141683 (-0.123795) | 1.136983 / 1.452155 (-0.315172) | 1.167495 / 1.492716 (-0.325222) |

### 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) | 0.088167 / 0.018006 (0.070161) | 0.297360 / 0.000490 (0.296871) | 0.000231 / 0.000200 (0.000031) | 0.000049 / 0.000054 (-0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018114 / 0.037411 (-0.019297) | 0.061217 / 0.014526 (0.046691) | 0.072269 / 0.176557 (-0.104288) | 0.120607 / 0.737135 (-0.616528) | 0.073517 / 0.296338 (-0.222822) |

### 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) | 0.282580 / 0.215209 (0.067371) | 2.758650 / 2.077655 (0.680995) | 1.425125 / 1.504120 (-0.078995) | 1.303182 / 1.541195 (-0.238013) | 1.341035 / 1.468490 (-0.127455) | 0.549485 / 4.584777 (-4.035292) | 2.346297 / 3.745712 (-1.399415) | 2.686457 / 5.269862 (-2.583405) | 1.684789 / 4.565676 (-2.880888) | 0.061279 / 0.424275 (-0.362996) | 0.004902 / 0.007607 (-0.002705) | 0.333089 / 0.226044 (0.107044) | 3.297016 / 2.268929 (1.028087) | 1.765614 / 55.444624 (-53.679010) | 1.499314 / 6.876477 (-5.377162) | 1.501275 / 2.142072 (-0.640797) | 0.619039 / 4.805227 (-4.186189) | 0.114284 / 6.500664 (-6.386380) | 0.041481 / 0.075469 (-0.033988) |

### 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) | 0.973924 / 1.841788 (-0.867863) | 11.268266 / 8.074308 (3.193958) | 10.304738 / 10.191392 (0.113346) | 0.129297 / 0.680424 (-0.551127) | 0.014894 / 0.534201 (-0.519307) | 0.287658 / 0.579283 (-0.291626) | 0.266476 / 0.434364 (-0.167888) | 0.322199 / 0.540337 (-0.218138) | 0.419568 / 1.386936 (-0.967368) |

</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) | 0.005220 / 0.011353 (-0.006133) | 0.003310 / 0.011008 (-0.007698) | 0.049707 / 0.038508 (0.011199) | 0.031148 / 0.023109 (0.008039) | 0.284644 / 0.275898 (0.008746) | 0.302767 / 0.323480 (-0.020712) | 0.004245 / 0.007986 (-0.003740) | 0.002677 / 0.004328 (-0.001651) | 0.049870 / 0.004250 (0.045620) | 0.043922 / 0.037052 (0.006870) | 0.294955 / 0.258489 (0.036466) | 0.322144 / 0.293841 (0.028303) | 0.047211 / 0.128546 (-0.081336) | 0.010492 / 0.075646 (-0.065155) | 0.058152 / 0.419271 (-0.361120) | 0.033508 / 0.043533 (-0.010025) | 0.281266 / 0.255139 (0.026127) | 0.300010 / 0.283200 (0.016810) | 0.017616 / 0.141683 (-0.124067) | 1.124658 / 1.452155 (-0.327496) | 1.167222 / 1.492716 (-0.325495) |

### 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) | 0.089085 / 0.018006 (0.071079) | 0.297912 / 0.000490 (0.297423) | 0.000211 / 0.000200 (0.000011) | 0.000056 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021669 / 0.037411 (-0.015742) | 0.075648 / 0.014526 (0.061123) | 0.086054 / 0.176557 (-0.090503) | 0.125236 / 0.737135 (-0.611899) | 0.088146 / 0.296338 (-0.208192) |

### 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) | 0.295238 / 0.215209 (0.080029) | 2.870002 / 2.077655 (0.792347) | 1.582534 / 1.504120 (0.078414) | 1.466710 / 1.541195 (-0.074485) | 1.475352 / 1.468490 (0.006861) | 0.554745 / 4.584777 (-4.030032) | 2.412533 / 3.745712 (-1.333179) | 2.583863 / 5.269862 (-2.685999) | 1.689124 / 4.565676 (-2.876552) | 0.061353 / 0.424275 (-0.362922) | 0.005015 / 0.007607 (-0.002592) | 0.338733 / 0.226044 (0.112688) | 3.356710 / 2.268929 (1.087781) | 1.932143 / 55.444624 (-53.512481) | 1.660081 / 6.876477 (-5.216396) | 1.764961 / 2.142072 (-0.377111) | 0.640002 / 4.805227 (-4.165225) | 0.115251 / 6.500664 (-6.385413) | 0.040627 / 0.075469 (-0.034842) |

### 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) | 0.992296 / 1.841788 (-0.849492) | 11.821259 / 8.074308 (3.746951) | 10.715570 / 10.191392 (0.524178) | 0.142934 / 0.680424 (-0.537489) | 0.015680 / 0.534201 (-0.518521) | 0.287435 / 0.579283 (-0.291848) | 0.276817 / 0.434364 (-0.157547) | 0.327823 / 0.540337 (-0.212515) | 0.413404 / 1.386936 (-0.973532) |

</details>
</details>

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