[Streaming] retry on requests errors
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6963). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. ci failures are r-unrelated to this PR, merging <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) | 0.005532 / 0.011353 (-0.005821) | 0.004018 / 0.011008 (-0.006991) | 0.064685 / 0.038508 (0.026177) | 0.031303 / 0.023109 (0.008194) | 0.254670 / 0.275898 (-0.021228) | 0.271357 / 0.323480 (-0.052123) | 0.003372 / 0.007986 (-0.004614) | 0.004153 / 0.004328 (-0.000175) | 0.050381 / 0.004250 (0.046131) | 0.046837 / 0.037052 (0.009784) | 0.253166 / 0.258489 (-0.005323) | 0.294257 / 0.293841 (0.000416) | 0.029746 / 0.128546 (-0.098800) | 0.012519 / 0.075646 (-0.063127) | 0.208822 / 0.419271 (-0.210449) | 0.036925 / 0.043533 (-0.006608) | 0.247636 / 0.255139 (-0.007503) | 0.269102 / 0.283200 (-0.014097) | 0.019021 / 0.141683 (-0.122662) | 1.138825 / 1.452155 (-0.313330) | 1.203301 / 1.492716 (-0.289415) |

### 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) | 0.095950 / 0.018006 (0.077944) | 0.303347 / 0.000490 (0.302857) | 0.000221 / 0.000200 (0.000022) | 0.000042 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019014 / 0.037411 (-0.018397) | 0.062220 / 0.014526 (0.047694) | 0.074811 / 0.176557 (-0.101745) | 0.122917 / 0.737135 (-0.614218) | 0.075765 / 0.296338 (-0.220574) |

### 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) | 0.288359 / 0.215209 (0.073150) | 2.849491 / 2.077655 (0.771837) | 1.479448 / 1.504120 (-0.024672) | 1.350560 / 1.541195 (-0.190635) | 1.366079 / 1.468490 (-0.102411) | 0.733609 / 4.584777 (-3.851168) | 2.416014 / 3.745712 (-1.329698) | 2.954834 / 5.269862 (-2.315028) | 1.985703 / 4.565676 (-2.579974) | 0.080589 / 0.424275 (-0.343686) | 0.005581 / 0.007607 (-0.002026) | 0.343706 / 0.226044 (0.117661) | 3.416257 / 2.268929 (1.147329) | 1.865937 / 55.444624 (-53.578687) | 1.545911 / 6.876477 (-5.330566) | 1.711004 / 2.142072 (-0.431069) | 0.821231 / 4.805227 (-3.983996) | 0.138865 / 6.500664 (-6.361799) | 0.046466 / 0.075469 (-0.029003) |

### 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) | 0.965632 / 1.841788 (-0.876155) | 11.812101 / 8.074308 (3.737792) | 9.399156 / 10.191392 (-0.792236) | 0.143325 / 0.680424 (-0.537099) | 0.014824 / 0.534201 (-0.519377) | 0.306143 / 0.579283 (-0.273140) | 0.264063 / 0.434364 (-0.170301) | 0.347820 / 0.540337 (-0.192517) | 0.476818 / 1.386936 (-0.910118) |

</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) | 0.005978 / 0.011353 (-0.005375) | 0.004482 / 0.011008 (-0.006526) | 0.053788 / 0.038508 (0.015280) | 0.033963 / 0.023109 (0.010853) | 0.267258 / 0.275898 (-0.008640) | 0.290916 / 0.323480 (-0.032563) | 0.004485 / 0.007986 (-0.003500) | 0.002876 / 0.004328 (-0.001453) | 0.048637 / 0.004250 (0.044386) | 0.042050 / 0.037052 (0.004997) | 0.278607 / 0.258489 (0.020118) | 0.315411 / 0.293841 (0.021570) | 0.032059 / 0.128546 (-0.096487) | 0.012851 / 0.075646 (-0.062795) | 0.061672 / 0.419271 (-0.357600) | 0.034545 / 0.043533 (-0.008988) | 0.262068 / 0.255139 (0.006929) | 0.291197 / 0.283200 (0.007997) | 0.019092 / 0.141683 (-0.122591) | 1.108690 / 1.452155 (-0.343464) | 1.161025 / 1.492716 (-0.331691) |

### 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) | 0.096775 / 0.018006 (0.078768) | 0.306825 / 0.000490 (0.306335) | 0.000210 / 0.000200 (0.000010) | 0.000054 / 0.000054 (-0.000000) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023160 / 0.037411 (-0.014251) | 0.078794 / 0.014526 (0.064268) | 0.088954 / 0.176557 (-0.087602) | 0.129488 / 0.737135 (-0.607648) | 0.091239 / 0.296338 (-0.205099) |

### 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) | 0.292911 / 0.215209 (0.077702) | 2.910802 / 2.077655 (0.833148) | 1.569310 / 1.504120 (0.065191) | 1.433807 / 1.541195 (-0.107388) | 1.478619 / 1.468490 (0.010129) | 0.720982 / 4.584777 (-3.863795) | 0.972104 / 3.745712 (-2.773608) | 3.026941 / 5.269862 (-2.242921) | 1.919170 / 4.565676 (-2.646506) | 0.079292 / 0.424275 (-0.344983) | 0.005227 / 0.007607 (-0.002380) | 0.345363 / 0.226044 (0.119319) | 3.416149 / 2.268929 (1.147221) | 1.938377 / 55.444624 (-53.506248) | 1.626037 / 6.876477 (-5.250440) | 1.644405 / 2.142072 (-0.497668) | 0.802485 / 4.805227 (-4.002742) | 0.135114 / 6.500664 (-6.365550) | 0.042015 / 0.075469 (-0.033454) |

### 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) | 1.014812 / 1.841788 (-0.826976) | 12.583844 / 8.074308 (4.509536) | 10.522495 / 10.191392 (0.331103) | 0.143336 / 0.680424 (-0.537088) | 0.015843 / 0.534201 (-0.518357) | 0.306556 / 0.579283 (-0.272727) | 0.129654 / 0.434364 (-0.304710) | 0.340442 / 0.540337 (-0.199896) | 0.445220 / 1.386936 (-0.941716) |

</details>
</details>

![](https://cml.dev/watermark.png#5cab892dcd26fb51938634e13e300c6611ab66e0 "CML watermark")

reported in https://discuss.huggingface.co/t/speeding-up-streaming-of-large-datasets-fineweb/90714/6  when training using a streaming a dataloader

cc @Wauplin it looks like the retries from `hfh` are not always enough. In this PR I let `datasets` do additional retries (that users can configure in `datasets.config`) since I couldn't find an easy way to increase the max_retries for `hfh` users in general.