Make get_from_cache use custom temp filename that is locked

This PR ensures that the temporary filename created is the same as the one that is locked, while writing to the cache.

This PR stops using `tempfile` to generate the temporary filename.

Additionally, the behavior now is aligned for both `resume_download` `True` and `False`.

Refactor temp_file_manager so that it uses the filename that is locked: 
- Use: `cache_path + ".incomplete"`, when the locked one is `cache_path + ".lock"`

Before it was using `tempfile` inside `cache_dir`, which was not locked: although very improbable name collision (8 random characters), this was not impossible when huge number of multiple processes.

Maybe related to "Stale file handle" issues caused by `tempfile`: 
- [ ] https://huggingface.co/datasets/tapaco/discussions/4
- [ ] https://huggingface.co/datasets/xcsr/discussions/1
- [ ] https://huggingface.co/datasets/covost2/discussions/3
```
Error code:   ConfigNamesError
Exception:    OSError
Message:      [Errno 116] Stale file handle
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 61, in compute_config_names_response
                  for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1188, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 907, in get_module
                  dataset_readme_path = self.download_dataset_readme_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 896, in download_dataset_readme_file
                  return cached_path(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 183, in cached_path
                  output_path = get_from_cache(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 611, in get_from_cache
                  http_get(
                File "/usr/local/lib/python3.9/tempfile.py", line 496, in __exit__
                  result = self.file.__exit__(exc, value, tb)
              OSError: [Errno 116] Stale file handle
```
- the stale file handle error can be raised when `tempfile` tries to close (when exiting its context manager) a filename that has been already closed by other process
  - note that `tempfile` filenames are randomly generated but not locked in our code

CC: @severo 

_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) | 0.007241 / 0.011353 (-0.004112) | 0.004574 / 0.011008 (-0.006434) | 0.120481 / 0.038508 (0.081973) | 0.040492 / 0.023109 (0.017383) | 0.391399 / 0.275898 (0.115501) | 0.422844 / 0.323480 (0.099365) | 0.004441 / 0.007986 (-0.003545) | 0.004544 / 0.004328 (0.000216) | 0.089482 / 0.004250 (0.085231) | 0.052939 / 0.037052 (0.015887) | 0.393649 / 0.258489 (0.135160) | 0.433852 / 0.293841 (0.140011) | 0.035882 / 0.128546 (-0.092664) | 0.010172 / 0.075646 (-0.065474) | 0.410331 / 0.419271 (-0.008940) | 0.061481 / 0.043533 (0.017948) | 0.405066 / 0.255139 (0.149927) | 0.417732 / 0.283200 (0.134532) | 0.121647 / 0.141683 (-0.020035) | 1.790624 / 1.452155 (0.338469) | 1.863398 / 1.492716 (0.370681) |

### 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) | 0.250650 / 0.018006 (0.232644) | 0.489044 / 0.000490 (0.488554) | 0.010421 / 0.000200 (0.010222) | 0.000106 / 0.000054 (0.000051) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030340 / 0.037411 (-0.007071) | 0.128318 / 0.014526 (0.113792) | 0.140463 / 0.176557 (-0.036093) | 0.205762 / 0.737135 (-0.531373) | 0.147996 / 0.296338 (-0.148342) |

### 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) | 0.493158 / 0.215209 (0.277949) | 4.858346 / 2.077655 (2.780691) | 2.242942 / 1.504120 (0.738822) | 2.010092 / 1.541195 (0.468897) | 2.076765 / 1.468490 (0.608275) | 0.636669 / 4.584777 (-3.948108) | 4.478027 / 3.745712 (0.732314) | 2.157843 / 5.269862 (-3.112019) | 1.305133 / 4.565676 (-3.260543) | 0.079220 / 0.424275 (-0.345055) | 0.013858 / 0.007607 (0.006251) | 0.604501 / 0.226044 (0.378457) | 5.950071 / 2.268929 (3.681143) | 2.738373 / 55.444624 (-52.706251) | 2.380275 / 6.876477 (-4.496201) | 2.517108 / 2.142072 (0.375035) | 0.772249 / 4.805227 (-4.032979) | 0.169874 / 6.500664 (-6.330790) | 0.078026 / 0.075469 (0.002557) |

### 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) | 1.450200 / 1.841788 (-0.391588) | 17.810965 / 8.074308 (9.736657) | 15.518998 / 10.191392 (5.327606) | 0.200469 / 0.680424 (-0.479954) | 0.020777 / 0.534201 (-0.513424) | 0.504556 / 0.579283 (-0.074727) | 0.518493 / 0.434364 (0.084129) | 0.615335 / 0.540337 (0.074998) | 0.754065 / 1.386936 (-0.632871) |

</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) | 0.007224 / 0.011353 (-0.004129) | 0.004663 / 0.011008 (-0.006345) | 0.092151 / 0.038508 (0.053643) | 0.038359 / 0.023109 (0.015250) | 0.486413 / 0.275898 (0.210515) | 0.521596 / 0.323480 (0.198116) | 0.004207 / 0.007986 (-0.003778) | 0.003745 / 0.004328 (-0.000583) | 0.089840 / 0.004250 (0.085589) | 0.050996 / 0.037052 (0.013943) | 0.498090 / 0.258489 (0.239601) | 0.533647 / 0.293841 (0.239806) | 0.035151 / 0.128546 (-0.093395) | 0.010293 / 0.075646 (-0.065354) | 0.099056 / 0.419271 (-0.320215) | 0.057365 / 0.043533 (0.013833) | 0.470652 / 0.255139 (0.215513) | 0.509801 / 0.283200 (0.226602) | 0.115650 / 0.141683 (-0.026033) | 1.810860 / 1.452155 (0.358705) | 1.896775 / 1.492716 (0.404059) |

### 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) | 0.261887 / 0.018006 (0.243880) | 0.489919 / 0.000490 (0.489430) | 0.006117 / 0.000200 (0.005917) | 0.000134 / 0.000054 (0.000079) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.035033 / 0.037411 (-0.002378) | 0.141093 / 0.014526 (0.126567) | 0.152613 / 0.176557 (-0.023943) | 0.218351 / 0.737135 (-0.518785) | 0.158366 / 0.296338 (-0.137972) |

### 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) | 0.542219 / 0.215209 (0.327010) | 5.479358 / 2.077655 (3.401703) | 2.749586 / 1.504120 (1.245466) | 2.537686 / 1.541195 (0.996491) | 2.582351 / 1.468490 (1.113861) | 0.636750 / 4.584777 (-3.948027) | 4.537501 / 3.745712 (0.791789) | 2.141392 / 5.269862 (-3.128469) | 1.279711 / 4.565676 (-3.285965) | 0.079227 / 0.424275 (-0.345048) | 0.014141 / 0.007607 (0.006534) | 0.662070 / 0.226044 (0.436025) | 6.572144 / 2.268929 (4.303215) | 3.321349 / 55.444624 (-52.123275) | 2.928219 / 6.876477 (-3.948258) | 3.002732 / 2.142072 (0.860659) | 0.773808 / 4.805227 (-4.031419) | 0.166017 / 6.500664 (-6.334647) | 0.076424 / 0.075469 (0.000955) |

### 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) | 1.584325 / 1.841788 (-0.257463) | 18.359247 / 8.074308 (10.284938) | 16.977875 / 10.191392 (6.786483) | 0.195381 / 0.680424 (-0.485043) | 0.021048 / 0.534201 (-0.513153) | 0.512237 / 0.579283 (-0.067047) | 0.511435 / 0.434364 (0.077071) | 0.592856 / 0.540337 (0.052518) | 0.711905 / 1.386936 (-0.675031) |

</details>
</details>

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