# 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

 -->

# coding=utf-8
# Copyright 2022 The HuggingFace Team 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 clone 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.

from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        AutoTokenizer,
        TFAutoModelForCausalLM,
        TFAutoModelForSeq2SeqLM,
        TFAutoModelForSpeechSeq2Seq,
        TFAutoModelForVision2Seq,
        TFBartForConditionalGeneration,
        TFLogitsProcessorList,
        TFMinLengthLogitsProcessor,
        tf_top_k_top_p_filtering,
    )

if is_tensorflow_text_available():
    import tensorflow_text as text


@require_tf
class UtilsFunctionsTest(unittest.TestCase):
    # tests whether the top_k_top_p_filtering function behaves as expected
    def test_top_k_top_p_filtering(self):
        logits = tf.convert_to_tensor(
            [
                [
                    8.2220991,  # 3rd highest value; idx. 0
                    -0.5620044,
                    5.23229752,
                    4.0386393,
                    -6.8798378,
                    -0.54785802,
                    -3.2012153,
                    2.92777176,
                    1.88171953,
                    7.35341276,  # 5th highest value; idx. 9
                    8.43207833,  # 2nd highest value; idx. 10
                    -9.85711836,
                    -5.96209236,
                    -1.13039161,
                    -7.1115294,
                    -0.8369633,
                    -5.3186408,
                    7.06427407,
                    0.81369344,
                    -0.82023817,
                    -5.9179796,
                    0.58813443,
                    -6.99778438,
                    4.71551189,
                    -0.18771637,
                    7.44020759,  # 4th highest value; idx. 25
                    9.38450987,  # 1st highest value; idx. 26
                    2.12662941,
                    -9.32562038,
                    2.35652522,
                ],  # cummulative prob of 5 highest values <= 0.6
                [
                    0.58425518,
                    4.53139238,
                    -5.57510464,
                    -6.28030699,
                    -7.19529503,
                    -4.02122551,
                    1.39337037,
                    -6.06707057,
                    1.59480517,
                    -9.643119,
                    0.03907799,
                    0.67231762,
                    -8.88206726,
                    6.27115922,  # 4th highest value; idx. 13
                    2.28520723,
                    4.82767506,
                    4.30421368,
                    8.8275313,  # 2nd highest value; idx. 17
                    5.44029958,  # 5th highest value; idx. 18
                    -4.4735794,
                    7.38579536,  # 3rd highest value; idx. 20
                    -2.91051663,
                    2.61946077,
                    -2.5674762,
                    -9.48959302,
                    -4.02922645,
                    -1.35416918,
                    9.67702323,  # 1st highest value; idx. 27
                    -5.89478553,
                    1.85370467,
                ],  # cummulative prob of 5 highest values <= 0.6
            ],
            dtype=tf.float32,
        )

        non_inf_expected_idx = tf.convert_to_tensor(
            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
            dtype=tf.int32,
        )  # expected non filtered idx as noted above

        non_inf_expected_output = tf.convert_to_tensor(
            [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
            dtype=tf.float32,
        )  # expected non filtered values as noted above

        output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)

        non_inf_output = output[output != -float("inf")]
        non_inf_idx = tf.cast(
            tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
            dtype=tf.int32,
        )

        tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
        tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)


@require_tf
class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
    # setting framework_dependent_parameters needs to be gated, just like its contents' imports
    if is_tf_available():
        framework_dependent_parameters = {
            "AutoModelForCausalLM": TFAutoModelForCausalLM,
            "AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeq2Seq,
            "AutoModelForSeq2SeqLM": TFAutoModelForSeq2SeqLM,
            "AutoModelForVision2Seq": TFAutoModelForVision2Seq,
            "LogitsProcessorList": TFLogitsProcessorList,
            "MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
            "create_tensor_fn": tf.convert_to_tensor,
            "floats_tensor": floats_tensor,
            "return_tensors": "tf",
        }

    @slow
    def test_generate_tf_function_export_fixed_input_length(self):
        # TF-only test: tf.saved_model export
        test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        input_length = 2
        max_new_tokens = 2

        class DummyModel(tf.Module):
            def __init__(self, model):
                super(DummyModel, self).__init__()
                self.model = model

            @tf.function(
                input_signature=(
                    tf.TensorSpec((None, input_length), tf.int32, name="input_ids"),
                    tf.TensorSpec((None, input_length), tf.int32, name="attention_mask"),
                ),
                jit_compile=True,
            )
            def serving(self, input_ids, attention_mask):
                outputs = self.model.generate(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    max_new_tokens=max_new_tokens,
                    return_dict_in_generate=True,
                )
                return {"sequences": outputs["sequences"]}

        dummy_input_ids = [[2, 0], [102, 103]]
        dummy_attention_masks = [[1, 0], [1, 1]]
        dummy_model = DummyModel(model=test_model)
        with tempfile.TemporaryDirectory() as tmp_dir:
            tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
            serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
            for batch_size in range(1, len(dummy_input_ids) + 1):
                inputs = {
                    "input_ids": tf.constant(dummy_input_ids[:batch_size]),
                    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
                }
                tf_func_outputs = serving_func(**inputs)["sequences"]
                tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens)
                tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)

    @slow
    def test_generate_tf_function_export_fixed_batch_size(self):
        # TF-only test: tf.saved_model export
        test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        batch_size = 1
        max_new_tokens = 2

        class DummyModel(tf.Module):
            def __init__(self, model):
                super(DummyModel, self).__init__()
                self.model = model

            @tf.function(
                input_signature=(
                    tf.TensorSpec((batch_size, None), tf.int32, name="input_ids"),
                    tf.TensorSpec((batch_size, None), tf.int32, name="attention_mask"),
                ),
                jit_compile=True,
            )
            def serving(self, input_ids, attention_mask):
                outputs = self.model.generate(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    max_new_tokens=max_new_tokens,
                    return_dict_in_generate=True,
                )
                return {"sequences": outputs["sequences"]}

        dummy_input_ids = [[2], [102, 103]]
        dummy_attention_masks = [[1], [1, 1]]
        dummy_model = DummyModel(model=test_model)
        with tempfile.TemporaryDirectory() as tmp_dir:
            tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
            serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
            for input_row in range(len(dummy_input_ids)):
                inputs = {
                    "input_ids": tf.constant([dummy_input_ids[input_row]]),
                    "attention_mask": tf.constant([dummy_attention_masks[input_row]]),
                }
                tf_func_outputs = serving_func(**inputs)["sequences"]
                tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens)
                tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)

    @slow
    @require_tensorflow_text
    def test_generate_tf_function_export_with_tf_tokenizer(self):
        # TF-only test: tf.saved_model export
        with tempfile.TemporaryDirectory() as tmp_dir:
            # file needed to load the TF tokenizer
            hf_hub_download(repo_id="google/flan-t5-small", filename="spiece.model", local_dir=tmp_dir)

            class CompleteSentenceTransformer(tf.keras.layers.Layer):
                def __init__(self):
                    super().__init__()
                    self.tokenizer = text.SentencepieceTokenizer(
                        model=tf.io.gfile.GFile(os.path.join(tmp_dir, "spiece.model"), "rb").read()
                    )
                    self.model = TFAutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")

                def call(self, inputs, *args, **kwargs):
                    tokens = self.tokenizer.tokenize(inputs)
                    input_ids, attention_mask = text.pad_model_inputs(
                        tokens, max_seq_length=64, pad_value=self.model.config.pad_token_id
                    )
                    outputs = self.model.generate(input_ids=input_ids, attention_mask=attention_mask)
                    return self.tokenizer.detokenize(outputs)

            complete_model = CompleteSentenceTransformer()
            inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="inputs")
            outputs = complete_model(inputs)
            keras_model = tf.keras.Model(inputs, outputs)
            keras_model.save(tmp_dir)

    def test_eos_token_id_int_and_list_top_k_top_sampling(self):
        # Has PT equivalent: this test relies on random sampling
        generation_kwargs = {
            "do_sample": True,
            "num_beams": 1,
            "top_p": 0.7,
            "top_k": 10,
            "temperature": 0.7,
        }
        expectation = 14

        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        text = """Hello, my dog is cute and"""
        tokens = tokenizer(text, return_tensors="tf")
        model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")

        eos_token_id = 638
        # forces the generation to happen on CPU, to avoid GPU-related quirks
        with tf.device(":/CPU:0"):
            tf.random.set_seed(0)
            generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
        self.assertTrue(expectation == len(generated_tokens[0]))

        eos_token_id = [638, 198]
        with tf.device(":/CPU:0"):
            tf.random.set_seed(0)
            generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
        self.assertTrue(expectation == len(generated_tokens[0]))

    def test_model_kwarg_encoder_signature_filtering(self):
        # Has PT equivalent: ample use of framework-specific code
        bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        article = """Hugging Face is a technology company based in New York and Paris."""
        input_ids = bart_tokenizer(article, return_tensors="tf").input_ids
        bart_model = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart")
        output = bart_model.generate(input_ids).numpy()

        # Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an
        # argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of
        # the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and
        # saves the day.
        class FakeBart(TFBartForConditionalGeneration):
            def call(self, input_ids, foo=None, **kwargs):
                return super().call(input_ids, **kwargs)

        bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart")
        fake_output = bart_model.generate(input_ids, foo="bar").numpy()
        self.assertTrue(np.array_equal(output, fake_output))

        # Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail
        # because it doesn't do signature filtering.
        class FakeEncoder(bart_model.model.encoder.__class__):
            def call(self, input_ids, **kwargs):
                return super().call(input_ids, **kwargs)

        fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared)
        bart_model.model.encoder = fake_encoder

        # Normal generation still works (the output will be different because the encoder weights are different)
        fake_output = bart_model.generate(input_ids).numpy()
        with self.assertRaises(ValueError):
            # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
            bart_model.generate(input_ids, foo="bar")

# coding=utf-8
# Copyright 2022 The HuggingFace Team 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 clone 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.

from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        AutoTokenizer,
        TFAutoModelForCausalLM,
        TFAutoModelForSeq2SeqLM,
        TFAutoModelForSpeechSeq2Seq,
        TFAutoModelForVision2Seq,
        TFBartForConditionalGeneration,
        TFLogitsProcessorList,
        TFMinLengthLogitsProcessor,
        tf_top_k_top_p_filtering,
    )

if is_tensorflow_text_available():
    import tensorflow_text as text


@require_tf
class UtilsFunctionsTest(unittest.TestCase):
    # tests whether the top_k_top_p_filtering function behaves as expected
    def test_top_k_top_p_filtering(self):
        logits = tf.convert_to_tensor(
            [
                [
                    8.2220991,  # 3rd highest value; idx. 0
                    -0.5620044,
                    5.23229752,
                    4.0386393,
                    -6.8798378,
                    -0.54785802,
                    -3.2012153,
                    2.92777176,
                    1.88171953,
                    7.35341276,  # 5th highest value; idx. 9
                    8.43207833,  # 2nd highest value; idx. 10
                    -9.85711836,
                    -5.96209236,
                    -1.13039161,
                    -7.1115294,
                    -0.8369633,
                    -5.3186408,
                    7.06427407,
                    0.81369344,
                    -0.82023817,
                    -5.9179796,
                    0.58813443,
                    -6.99778438,
                    4.71551189,
                    -0.18771637,
                    7.44020759,  # 4th highest value; idx. 25
                    9.38450987,  # 1st highest value; idx. 26
                    2.12662941,
                    -9.32562038,
                    2.35652522,
                ],  # cummulative prob of 5 highest values <= 0.6
                [
                    0.58425518,
                    4.53139238,
                    -5.57510464,
                    -6.28030699,
                    -7.19529503,
                    -4.02122551,
                    1.39337037,
                    -6.06707057,
                    1.59480517,
                    -9.643119,
                    0.03907799,
                    0.67231762,
                    -8.88206726,
                    6.27115922,  # 4th highest value; idx. 13
                    2.28520723,
                    4.82767506,
                    4.30421368,
                    8.8275313,  # 2nd highest value; idx. 17
                    5.44029958,  # 5th highest value; idx. 18
                    -4.4735794,
                    7.38579536,  # 3rd highest value; idx. 20
                    -2.91051663,
                    2.61946077,
                    -2.5674762,
                    -9.48959302,
                    -4.02922645,
                    -1.35416918,
                    9.67702323,  # 1st highest value; idx. 27
                    -5.89478553,
                    1.85370467,
                ],  # cummulative prob of 5 highest values <= 0.6
            ],
            dtype=tf.float32,
        )

        non_inf_expected_idx = tf.convert_to_tensor(
            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
            dtype=tf.int32,
        )  # expected non filtered idx as noted above

        non_inf_expected_output = tf.convert_to_tensor(
            [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
            dtype=tf.float32,
        )  # expected non filtered values as noted above

        output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)

        non_inf_output = output[output != -float("inf")]
        non_inf_idx = tf.cast(
            tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
            dtype=tf.int32,
        )

        tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
        tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)


@require_tf
class TFGenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
    # setting framework_dependent_parameters needs to be gated, just like its contents' imports
    if is_tf_available():
        framework_dependent_parameters = {
            "AutoModelForCausalLM": TFAutoModelForCausalLM,
            "AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeq2Seq,
            "AutoModelForSeq2SeqLM": TFAutoModelForSeq2SeqLM,
            "AutoModelForVision2Seq": TFAutoModelForVision2Seq,
            "LogitsProcessorList": TFLogitsProcessorList,
            "MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
            "create_tensor_fn": tf.convert_to_tensor,
            "floats_tensor": floats_tensor,
            "return_tensors": "tf",
        }

    @slow
    def test_generate_tf_function_export_fixed_input_length(self):
        # TF-only test: tf.saved_model export
        test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        input_length = 2
        max_new_tokens = 2

        class DummyModel(tf.Module):
            def __init__(self, model):
                super(DummyModel, self).__init__()
                self.model = model

            @tf.function(
                input_signature=(
                    tf.TensorSpec((None, input_length), tf.int32, name="input_ids"),
                    tf.TensorSpec((None, input_length), tf.int32, name="attention_mask"),
                ),
                jit_compile=True,
            )
            def serving(self, input_ids, attention_mask):
                outputs = self.model.generate(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    max_new_tokens=max_new_tokens,
                    return_dict_in_generate=True,
                )
                return {"sequences": outputs["sequences"]}

        dummy_input_ids = [[2, 0], [102, 103]]
        dummy_attention_masks = [[1, 0], [1, 1]]
        dummy_model = DummyModel(model=test_model)
        with tempfile.TemporaryDirectory() as tmp_dir:
            tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
            serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
            for batch_size in range(1, len(dummy_input_ids) + 1):
                inputs = {
                    "input_ids": tf.constant(dummy_input_ids[:batch_size]),
                    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
                }
                tf_func_outputs = serving_func(**inputs)["sequences"]
                tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens)
                tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)

    @slow
    def test_generate_tf_function_export_fixed_batch_size(self):
        # TF-only test: tf.saved_model export
        test_model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        batch_size = 1
        max_new_tokens = 2

        class DummyModel(tf.Module):
            def __init__(self, model):
                super(DummyModel, self).__init__()
                self.model = model

            @tf.function(
                input_signature=(
                    tf.TensorSpec((batch_size, None), tf.int32, name="input_ids"),
                    tf.TensorSpec((batch_size, None), tf.int32, name="attention_mask"),
                ),
                jit_compile=True,
            )
            def serving(self, input_ids, attention_mask):
                outputs = self.model.generate(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    max_new_tokens=max_new_tokens,
                    return_dict_in_generate=True,
                )
                return {"sequences": outputs["sequences"]}

        dummy_input_ids = [[2], [102, 103]]
        dummy_attention_masks = [[1], [1, 1]]
        dummy_model = DummyModel(model=test_model)
        with tempfile.TemporaryDirectory() as tmp_dir:
            tf.saved_model.save(dummy_model, tmp_dir, signatures={"serving_default": dummy_model.serving})
            serving_func = tf.saved_model.load(tmp_dir).signatures["serving_default"]
            for input_row in range(len(dummy_input_ids)):
                inputs = {
                    "input_ids": tf.constant([dummy_input_ids[input_row]]),
                    "attention_mask": tf.constant([dummy_attention_masks[input_row]]),
                }
                tf_func_outputs = serving_func(**inputs)["sequences"]
                tf_model_outputs = test_model.generate(**inputs, max_new_tokens=max_new_tokens)
                tf.debugging.assert_equal(tf_func_outputs, tf_model_outputs)

    @slow
    @require_tensorflow_text
    def test_generate_tf_function_export_with_tf_tokenizer(self):
        # TF-only test: tf.saved_model export
        with tempfile.TemporaryDirectory() as tmp_dir:
            # file needed to load the TF tokenizer
            hf_hub_download(repo_id="google/flan-t5-small", filename="spiece.model", local_dir=tmp_dir)

            class CompleteSentenceTransformer(tf.keras.layers.Layer):
                def __init__(self):
                    super().__init__()
                    self.tokenizer = text.SentencepieceTokenizer(
                        model=tf.io.gfile.GFile(os.path.join(tmp_dir, "spiece.model"), "rb").read()
                    )
                    self.model = TFAutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")

                def call(self, inputs, *args, **kwargs):
                    tokens = self.tokenizer.tokenize(inputs)
                    input_ids, attention_mask = text.pad_model_inputs(
                        tokens, max_seq_length=64, pad_value=self.model.config.pad_token_id
                    )
                    outputs = self.model.generate(input_ids=input_ids, attention_mask=attention_mask)
                    return self.tokenizer.detokenize(outputs)

            complete_model = CompleteSentenceTransformer()
            inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="inputs")
            outputs = complete_model(inputs)
            keras_model = tf.keras.Model(inputs, outputs)
            keras_model.save(tmp_dir)

    def test_eos_token_id_int_and_list_top_k_top_sampling(self):
        # Has PT equivalent: this test relies on random sampling
        generation_kwargs = {
            "do_sample": True,
            "num_beams": 1,
            "top_p": 0.7,
            "top_k": 10,
            "temperature": 0.7,
        }
        expectation = 14

        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        text = """Hello, my dog is cute and"""
        tokens = tokenizer(text, return_tensors="tf")
        model = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")

        eos_token_id = 638
        # forces the generation to happen on CPU, to avoid GPU-related quirks
        with tf.device(":/CPU:0"):
            tf.random.set_seed(0)
            generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
        self.assertTrue(expectation == len(generated_tokens[0]))

        eos_token_id = [638, 198]
        with tf.device(":/CPU:0"):
            tf.random.set_seed(0)
            generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
        self.assertTrue(expectation == len(generated_tokens[0]))

    def test_model_kwarg_encoder_signature_filtering(self):
        # Has PT equivalent: ample use of framework-specific code
        bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        article = """Hugging Face is a technology company based in New York and Paris."""
        input_ids = bart_tokenizer(article, return_tensors="tf").input_ids
        bart_model = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart")
        output = bart_model.generate(input_ids).numpy()

        # Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an
        # argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of
        # the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and
        # saves the day.
        class FakeBart(TFBartForConditionalGeneration):
            def call(self, input_ids, foo=None, **kwargs):
                return super().call(input_ids, **kwargs)

        bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart")
        fake_output = bart_model.generate(input_ids, foo="bar").numpy()
        self.assertTrue(np.array_equal(output, fake_output))

        # Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail
        # because it doesn't do signature filtering.
        class FakeEncoder(bart_model.model.encoder.__class__):
            def call(self, input_ids, **kwargs):
                return super().call(input_ids, **kwargs)

        fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared)
        bart_model.model.encoder = fake_encoder

        # Normal generation still works (the output will be different because the encoder weights are different)
        fake_output = bart_model.generate(input_ids).numpy()
        with self.assertRaises(ValueError):
            # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
            bart_model.generate(input_ids, foo="bar")
