# What does this PR do?

As title states, acts on deprecation that will be going through in this PR https://github.com/huggingface/accelerate/pull/1537 to avoid nightly failures

Fixes # (issue)


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sgugger
Act on deprecations in Accelerate no_trainer examples. # What does this PR do?

As title states, acts on deprecation that will be going through in this PR https://github.com/huggingface/accelerate/pull/1537 to avoid nightly failures

Fixes # (issue)


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sgugger
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


class TextClassificationToolTester(unittest.TestCase, ToolTesterMixin):
    def setUp(self):
        self.tool = load_tool("text-classification")
        self.tool.setup()
        self.remote_tool = load_tool("text-classification", remote=True)

    def test_exact_match_arg(self):
        result = self.tool("That's quite cool", ["positive", "negative"])
        self.assertEqual(result, "positive")

    def test_exact_match_arg_remote(self):
        result = self.remote_tool("That's quite cool", ["positive", "negative"])
        self.assertEqual(result, "positive")

    def test_exact_match_kwarg(self):
        result = self.tool(text="That's quite cool", labels=["positive", "negative"])
        self.assertEqual(result, "positive")

    def test_exact_match_kwarg_remote(self):
        result = self.remote_tool(text="That's quite cool", labels=["positive", "negative"])
        self.assertEqual(result, "positive")

# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


class TextClassificationToolTester(unittest.TestCase, ToolTesterMixin):
    def setUp(self):
        self.tool = load_tool("text-classification")
        self.tool.setup()
        self.remote_tool = load_tool("text-classification", remote=True)

    def test_exact_match_arg(self):
        result = self.tool("That's quite cool", ["positive", "negative"])
        self.assertEqual(result, "positive")

    def test_exact_match_arg_remote(self):
        result = self.remote_tool("That's quite cool", ["positive", "negative"])
        self.assertEqual(result, "positive")

    def test_exact_match_kwarg(self):
        result = self.tool(text="That's quite cool", labels=["positive", "negative"])
        self.assertEqual(result, "positive")

    def test_exact_match_kwarg_remote(self):
        result = self.remote_tool(text="That's quite cool", labels=["positive", "negative"])
        self.assertEqual(result, "positive")
