# What does this PR do?

<!--
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<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] 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.
- [X] 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).
- [X] 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.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Set `TF32` flag for PyTorch cuDNN backend. # What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] 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.
- [X] 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).
- [X] 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.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class CLIPProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        # fmt: off
        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

        image_processor_map = {
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.48145466, 0.4578275, 0.40821073],
            "image_std": [0.26862954, 0.26130258, 0.27577711],
        }
        self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
        with open(self.image_processor_file, "w", encoding="utf-8") as fp:
            json.dump(image_processor_map, fp)

    def get_tokenizer(self, **kwargs):
        return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs)

    def get_rust_tokenizer(self, **kwargs):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)

    def get_image_processor(self, **kwargs):
        return CLIPImageProcessor.from_pretrained(self.tmpdirname, **kwargs)

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
        or a list of PyTorch tensors if one specifies torchify=True.
        """

        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]

        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

        return image_inputs

    def test_save_load_pretrained_default(self):
        tokenizer_slow = self.get_tokenizer()
        tokenizer_fast = self.get_rust_tokenizer()
        image_processor = self.get_image_processor()

        processor_slow = CLIPProcessor(tokenizer=tokenizer_slow, image_processor=image_processor)
        processor_slow.save_pretrained(self.tmpdirname)
        processor_slow = CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=False)

        processor_fast = CLIPProcessor(tokenizer=tokenizer_fast, image_processor=image_processor)
        processor_fast.save_pretrained(self.tmpdirname)
        processor_fast = CLIPProcessor.from_pretrained(self.tmpdirname)

        self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
        self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
        self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
        self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
        self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)

        self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
        self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string())
        self.assertIsInstance(processor_slow.image_processor, CLIPImageProcessor)
        self.assertIsInstance(processor_fast.image_processor, CLIPImageProcessor)

    def test_save_load_pretrained_additional_features(self):
        processor = CLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = CLIPProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, CLIPImageProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_image_proc = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_tokenizer(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(list(inputs.keys()), processor.model_input_names)

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class CLIPProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        # fmt: off
        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

        image_processor_map = {
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.48145466, 0.4578275, 0.40821073],
            "image_std": [0.26862954, 0.26130258, 0.27577711],
        }
        self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
        with open(self.image_processor_file, "w", encoding="utf-8") as fp:
            json.dump(image_processor_map, fp)

    def get_tokenizer(self, **kwargs):
        return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs)

    def get_rust_tokenizer(self, **kwargs):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)

    def get_image_processor(self, **kwargs):
        return CLIPImageProcessor.from_pretrained(self.tmpdirname, **kwargs)

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def prepare_image_inputs(self):
        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
        or a list of PyTorch tensors if one specifies torchify=True.
        """

        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]

        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]

        return image_inputs

    def test_save_load_pretrained_default(self):
        tokenizer_slow = self.get_tokenizer()
        tokenizer_fast = self.get_rust_tokenizer()
        image_processor = self.get_image_processor()

        processor_slow = CLIPProcessor(tokenizer=tokenizer_slow, image_processor=image_processor)
        processor_slow.save_pretrained(self.tmpdirname)
        processor_slow = CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=False)

        processor_fast = CLIPProcessor(tokenizer=tokenizer_fast, image_processor=image_processor)
        processor_fast.save_pretrained(self.tmpdirname)
        processor_fast = CLIPProcessor.from_pretrained(self.tmpdirname)

        self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
        self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
        self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
        self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
        self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)

        self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
        self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string())
        self.assertIsInstance(processor_slow.image_processor, CLIPImageProcessor)
        self.assertIsInstance(processor_fast.image_processor, CLIPImageProcessor)

    def test_save_load_pretrained_additional_features(self):
        processor = CLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)

        processor = CLIPProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
        )

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)

        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor, CLIPImageProcessor)

    def test_image_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        image_input = self.prepare_image_inputs()

        input_image_proc = image_processor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")

        for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)

    def test_tokenizer(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

    def test_processor(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])

        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()

    def test_tokenizer_decode(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)

    def test_model_input_names(self):
        image_processor = self.get_image_processor()
        tokenizer = self.get_tokenizer()

        processor = CLIPProcessor(tokenizer=tokenizer, image_processor=image_processor)

        input_str = "lower newer"
        image_input = self.prepare_image_inputs()

        inputs = processor(text=input_str, images=image_input)

        self.assertListEqual(list(inputs.keys()), processor.model_input_names)
