# 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 Inc. 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.
""" Neighborhood Attention Transformer model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


logger = logging.get_logger(__name__)

NAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class NatConfig(BackboneConfigMixin, PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Nat
    [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        patch_size (`int`, *optional*, defaults to 4):
            The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embed_dim (`int`, *optional*, defaults to 64):
            Dimensionality of patch embedding.
        depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
            Number of layers in each level of the encoder.
        num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
            Number of attention heads in each layer of the Transformer encoder.
        kernel_size (`int`, *optional*, defaults to 7):
            Neighborhood Attention kernel size.
        mlp_ratio (`float`, *optional*, defaults to 3.0):
            Ratio of MLP hidden dimensionality to embedding dimensionality.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not a learnable bias should be added to the queries, keys and values.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            Stochastic depth rate.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        layer_scale_init_value (`float`, *optional*, defaults to 0.0):
            The initial value for the layer scale. Disabled if <=0.
        out_features (`List[str]`, *optional*):
            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
            corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
        out_indices (`List[int]`, *optional*):
            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
            If unset and `out_features` is unset, will default to the last stage.

    Example:

    ```python
    >>> from transformers import NatConfig, NatModel

    >>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
    >>> configuration = NatConfig()

    >>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
    >>> model = NatModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "nat"

    attribute_map = {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }

    def __init__(
        self,
        patch_size=4,
        num_channels=3,
        embed_dim=64,
        depths=[3, 4, 6, 5],
        num_heads=[2, 4, 8, 16],
        kernel_size=7,
        mlp_ratio=3.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        layer_scale_init_value=0.0,
        out_features=None,
        out_indices=None,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_layers = len(depths)
        self.num_heads = num_heads
        self.kernel_size = kernel_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
        self.layer_scale_init_value = layer_scale_init_value
        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
        )

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. 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.
""" Neighborhood Attention Transformer model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


logger = logging.get_logger(__name__)

NAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class NatConfig(BackboneConfigMixin, PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Nat
    [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        patch_size (`int`, *optional*, defaults to 4):
            The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embed_dim (`int`, *optional*, defaults to 64):
            Dimensionality of patch embedding.
        depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
            Number of layers in each level of the encoder.
        num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
            Number of attention heads in each layer of the Transformer encoder.
        kernel_size (`int`, *optional*, defaults to 7):
            Neighborhood Attention kernel size.
        mlp_ratio (`float`, *optional*, defaults to 3.0):
            Ratio of MLP hidden dimensionality to embedding dimensionality.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not a learnable bias should be added to the queries, keys and values.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            Stochastic depth rate.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        layer_scale_init_value (`float`, *optional*, defaults to 0.0):
            The initial value for the layer scale. Disabled if <=0.
        out_features (`List[str]`, *optional*):
            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
            corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
        out_indices (`List[int]`, *optional*):
            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
            If unset and `out_features` is unset, will default to the last stage.

    Example:

    ```python
    >>> from transformers import NatConfig, NatModel

    >>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
    >>> configuration = NatConfig()

    >>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
    >>> model = NatModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "nat"

    attribute_map = {
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }

    def __init__(
        self,
        patch_size=4,
        num_channels=3,
        embed_dim=64,
        depths=[3, 4, 6, 5],
        num_heads=[2, 4, 8, 16],
        kernel_size=7,
        mlp_ratio=3.0,
        qkv_bias=True,
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        drop_path_rate=0.1,
        hidden_act="gelu",
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        layer_scale_init_value=0.0,
        out_features=None,
        out_indices=None,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.patch_size = patch_size
        self.num_channels = num_channels
        self.embed_dim = embed_dim
        self.depths = depths
        self.num_layers = len(depths)
        self.num_heads = num_heads
        self.kernel_size = kernel_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.drop_path_rate = drop_path_rate
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.initializer_range = initializer_range
        # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
        self.layer_scale_init_value = layer_scale_init_value
        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
        )
