We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
The type of `n_gram` is mislabeled as bool, which should be int type.
## 🐛 Bug

In Translation Task:
The type of `n_gram` is mislabeled as bool, which should be int type.

### To Reproduce

<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
```
flash translation from_hf_datasets --help
```
The error raised:
```
translation: error: Configuration check failed :: Parser key "model.n_gram": Expected a <class 'bool'> but got "4"
```


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `flash/text/seq2seq/translation/model.py`
Content:
```
1 # Copyright The PyTorch Lightning team.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 from typing import Any, Dict, Optional, Union
15 
16 from torchmetrics import BLEUScore
17 
18 from flash.core.utilities.imports import _TM_GREATER_EQUAL_0_7_0
19 from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
20 from flash.text.seq2seq.core.model import Seq2SeqTask
21 
22 
23 class TranslationTask(Seq2SeqTask):
24     """The ``TranslationTask`` is a :class:`~flash.Task` for Seq2Seq text translation. For more details, see
25     :ref:`translation`.
26 
27     You can change the backbone to any translation model from `HuggingFace/transformers
28     <https://huggingface.co/models?filter=pytorch&pipeline_tag=translation>`__ using the ``backbone`` argument.
29 
30     Args:
31         backbone: backbone model to use for the task.
32         max_source_length: The maximum length to pad / truncate input sequences to.
33         max_target_length: The maximum length to pad / truncate target sequences to.
34         padding: The type of padding to apply. One of: "longest" or ``True``, "max_length", "do_not_pad" or
35             ``False``.
36         loss_fn: Loss function for training.
37         optimizer: Optimizer to use for training.
38         lr_scheduler: The LR scheduler to use during training.
39         metrics: Metrics to compute for training and evaluation. Defauls to calculating the BLEU metric.
40             Changing this argument currently has no effect.
41         learning_rate: Learning rate to use for training, defaults to `1e-5`
42         num_beams: Number of beams to use in validation when generating predictions. Defaults to `4`
43         n_gram: Maximum n_grams to use in metric calculation. Defaults to `4`
44         smooth: Apply smoothing in BLEU calculation. Defaults to `True`
45         enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
46     """
47 
48     def __init__(
49         self,
50         backbone: str = "t5-small",
51         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
52         max_source_length: int = 128,
53         max_target_length: int = 128,
54         padding: Union[str, bool] = "max_length",
55         loss_fn: LOSS_FN_TYPE = None,
56         optimizer: OPTIMIZER_TYPE = "Adam",
57         lr_scheduler: LR_SCHEDULER_TYPE = None,
58         metrics: METRICS_TYPE = None,
59         learning_rate: Optional[float] = None,
60         num_beams: Optional[int] = 4,
61         n_gram: bool = 4,
62         smooth: bool = True,
63         enable_ort: bool = False,
64     ):
65         self.save_hyperparameters()
66         super().__init__(
67             backbone=backbone,
68             tokenizer_kwargs=tokenizer_kwargs,
69             max_source_length=max_source_length,
70             max_target_length=max_target_length,
71             padding=padding,
72             loss_fn=loss_fn,
73             optimizer=optimizer,
74             lr_scheduler=lr_scheduler,
75             metrics=metrics,
76             learning_rate=learning_rate,
77             num_beams=num_beams,
78             enable_ort=enable_ort,
79         )
80         self.bleu = BLEUScore(
81             n_gram=n_gram,
82             smooth=smooth,
83         )
84 
85     @property
86     def task(self) -> str:
87         return "translation"
88 
89     def compute_metrics(self, generated_tokens, batch, prefix):
90         reference_corpus = self.decode(batch["labels"])
91         # wrap targets in list as score expects a list of potential references
92         reference_corpus = [[reference] for reference in reference_corpus]
93 
94         translate_corpus = self.decode(generated_tokens)
95         translate_corpus = [line for line in translate_corpus]
96 
97         if _TM_GREATER_EQUAL_0_7_0:
98             result = self.bleu(translate_corpus, reference_corpus)
99         else:
100             result = self.bleu(reference_corpus, translate_corpus)
101         self.log(f"{prefix}_bleu_score", result, on_step=False, on_epoch=True, prog_bar=True)
102
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/flash/text/seq2seq/translation/model.py b/flash/text/seq2seq/translation/model.py
--- a/flash/text/seq2seq/translation/model.py
+++ b/flash/text/seq2seq/translation/model.py
@@ -58,7 +58,7 @@
         metrics: METRICS_TYPE = None,
         learning_rate: Optional[float] = None,
         num_beams: Optional[int] = 4,
-        n_gram: bool = 4,
+        n_gram: int = 4,
         smooth: bool = True,
         enable_ort: bool = False,
     ):

{"golden_diff": "diff --git a/flash/text/seq2seq/translation/model.py b/flash/text/seq2seq/translation/model.py\n--- a/flash/text/seq2seq/translation/model.py\n+++ b/flash/text/seq2seq/translation/model.py\n@@ -58,7 +58,7 @@\n         metrics: METRICS_TYPE = None,\n         learning_rate: Optional[float] = None,\n         num_beams: Optional[int] = 4,\n-        n_gram: bool = 4,\n+        n_gram: int = 4,\n         smooth: bool = True,\n         enable_ort: bool = False,\n     ):\n", "issue": "The type of `n_gram` is mislabeled as bool, which should be int type.\n## \ud83d\udc1b Bug\r\n\r\nIn Translation Task:\r\nThe type of `n_gram` is mislabeled as bool, which should be int type.\r\n\r\n### To Reproduce\r\n\r\n<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->\r\n```\r\nflash translation from_hf_datasets --help\r\n```\r\nThe error raised:\r\n```\r\ntranslation: error: Configuration check failed :: Parser key \"model.n_gram\": Expected a <class 'bool'> but got \"4\"\r\n```\r\n\n", "before_files": [{"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional, Union\n\nfrom torchmetrics import BLEUScore\n\nfrom flash.core.utilities.imports import _TM_GREATER_EQUAL_0_7_0\nfrom flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE\nfrom flash.text.seq2seq.core.model import Seq2SeqTask\n\n\nclass TranslationTask(Seq2SeqTask):\n    \"\"\"The ``TranslationTask`` is a :class:`~flash.Task` for Seq2Seq text translation. For more details, see\n    :ref:`translation`.\n\n    You can change the backbone to any translation model from `HuggingFace/transformers\n    <https://huggingface.co/models?filter=pytorch&pipeline_tag=translation>`__ using the ``backbone`` argument.\n\n    Args:\n        backbone: backbone model to use for the task.\n        max_source_length: The maximum length to pad / truncate input sequences to.\n        max_target_length: The maximum length to pad / truncate target sequences to.\n        padding: The type of padding to apply. One of: \"longest\" or ``True``, \"max_length\", \"do_not_pad\" or\n            ``False``.\n        loss_fn: Loss function for training.\n        optimizer: Optimizer to use for training.\n        lr_scheduler: The LR scheduler to use during training.\n        metrics: Metrics to compute for training and evaluation. Defauls to calculating the BLEU metric.\n            Changing this argument currently has no effect.\n        learning_rate: Learning rate to use for training, defaults to `1e-5`\n        num_beams: Number of beams to use in validation when generating predictions. Defaults to `4`\n        n_gram: Maximum n_grams to use in metric calculation. Defaults to `4`\n        smooth: Apply smoothing in BLEU calculation. Defaults to `True`\n        enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training\n    \"\"\"\n\n    def __init__(\n        self,\n        backbone: str = \"t5-small\",\n        tokenizer_kwargs: Optional[Dict[str, Any]] = None,\n        max_source_length: int = 128,\n        max_target_length: int = 128,\n        padding: Union[str, bool] = \"max_length\",\n        loss_fn: LOSS_FN_TYPE = None,\n        optimizer: OPTIMIZER_TYPE = \"Adam\",\n        lr_scheduler: LR_SCHEDULER_TYPE = None,\n        metrics: METRICS_TYPE = None,\n        learning_rate: Optional[float] = None,\n        num_beams: Optional[int] = 4,\n        n_gram: bool = 4,\n        smooth: bool = True,\n        enable_ort: bool = False,\n    ):\n        self.save_hyperparameters()\n        super().__init__(\n            backbone=backbone,\n            tokenizer_kwargs=tokenizer_kwargs,\n            max_source_length=max_source_length,\n            max_target_length=max_target_length,\n            padding=padding,\n            loss_fn=loss_fn,\n            optimizer=optimizer,\n            lr_scheduler=lr_scheduler,\n            metrics=metrics,\n            learning_rate=learning_rate,\n            num_beams=num_beams,\n            enable_ort=enable_ort,\n        )\n        self.bleu = BLEUScore(\n            n_gram=n_gram,\n            smooth=smooth,\n        )\n\n    @property\n    def task(self) -> str:\n        return \"translation\"\n\n    def compute_metrics(self, generated_tokens, batch, prefix):\n        reference_corpus = self.decode(batch[\"labels\"])\n        # wrap targets in list as score expects a list of potential references\n        reference_corpus = [[reference] for reference in reference_corpus]\n\n        translate_corpus = self.decode(generated_tokens)\n        translate_corpus = [line for line in translate_corpus]\n\n        if _TM_GREATER_EQUAL_0_7_0:\n            result = self.bleu(translate_corpus, reference_corpus)\n        else:\n            result = self.bleu(reference_corpus, translate_corpus)\n        self.log(f\"{prefix}_bleu_score\", result, on_step=False, on_epoch=True, prog_bar=True)\n", "path": "flash/text/seq2seq/translation/model.py"}], "after_files": [{"content": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional, Union\n\nfrom torchmetrics import BLEUScore\n\nfrom flash.core.utilities.imports import _TM_GREATER_EQUAL_0_7_0\nfrom flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE\nfrom flash.text.seq2seq.core.model import Seq2SeqTask\n\n\nclass TranslationTask(Seq2SeqTask):\n    \"\"\"The ``TranslationTask`` is a :class:`~flash.Task` for Seq2Seq text translation. For more details, see\n    :ref:`translation`.\n\n    You can change the backbone to any translation model from `HuggingFace/transformers\n    <https://huggingface.co/models?filter=pytorch&pipeline_tag=translation>`__ using the ``backbone`` argument.\n\n    Args:\n        backbone: backbone model to use for the task.\n        max_source_length: The maximum length to pad / truncate input sequences to.\n        max_target_length: The maximum length to pad / truncate target sequences to.\n        padding: The type of padding to apply. One of: \"longest\" or ``True``, \"max_length\", \"do_not_pad\" or\n            ``False``.\n        loss_fn: Loss function for training.\n        optimizer: Optimizer to use for training.\n        lr_scheduler: The LR scheduler to use during training.\n        metrics: Metrics to compute for training and evaluation. Defauls to calculating the BLEU metric.\n            Changing this argument currently has no effect.\n        learning_rate: Learning rate to use for training, defaults to `1e-5`\n        num_beams: Number of beams to use in validation when generating predictions. Defaults to `4`\n        n_gram: Maximum n_grams to use in metric calculation. Defaults to `4`\n        smooth: Apply smoothing in BLEU calculation. Defaults to `True`\n        enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training\n    \"\"\"\n\n    def __init__(\n        self,\n        backbone: str = \"t5-small\",\n        tokenizer_kwargs: Optional[Dict[str, Any]] = None,\n        max_source_length: int = 128,\n        max_target_length: int = 128,\n        padding: Union[str, bool] = \"max_length\",\n        loss_fn: LOSS_FN_TYPE = None,\n        optimizer: OPTIMIZER_TYPE = \"Adam\",\n        lr_scheduler: LR_SCHEDULER_TYPE = None,\n        metrics: METRICS_TYPE = None,\n        learning_rate: Optional[float] = None,\n        num_beams: Optional[int] = 4,\n        n_gram: int = 4,\n        smooth: bool = True,\n        enable_ort: bool = False,\n    ):\n        self.save_hyperparameters()\n        super().__init__(\n            backbone=backbone,\n            tokenizer_kwargs=tokenizer_kwargs,\n            max_source_length=max_source_length,\n            max_target_length=max_target_length,\n            padding=padding,\n            loss_fn=loss_fn,\n            optimizer=optimizer,\n            lr_scheduler=lr_scheduler,\n            metrics=metrics,\n            learning_rate=learning_rate,\n            num_beams=num_beams,\n            enable_ort=enable_ort,\n        )\n        self.bleu = BLEUScore(\n            n_gram=n_gram,\n            smooth=smooth,\n        )\n\n    @property\n    def task(self) -> str:\n        return \"translation\"\n\n    def compute_metrics(self, generated_tokens, batch, prefix):\n        reference_corpus = self.decode(batch[\"labels\"])\n        # wrap targets in list as score expects a list of potential references\n        reference_corpus = [[reference] for reference in reference_corpus]\n\n        translate_corpus = self.decode(generated_tokens)\n        translate_corpus = [line for line in translate_corpus]\n\n        if _TM_GREATER_EQUAL_0_7_0:\n            result = self.bleu(translate_corpus, reference_corpus)\n        else:\n            result = self.bleu(reference_corpus, translate_corpus)\n        self.log(f\"{prefix}_bleu_score\", result, on_step=False, on_epoch=True, prog_bar=True)\n", "path": "flash/text/seq2seq/translation/model.py"}]}