# What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
Migrate Trainer from `Repository` to `upload_folder`. # What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
# coding=utf-8
# Copyright 2022 Google AI and 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.
""" Audio Spectogram Transformer (AST) model configuration"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "MIT/ast-finetuned-audioset-10-10-0.4593": (
        "https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
    ),
}


class ASTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ASTModel`]. It is used to instantiate an AST
    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 AST
    [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
    architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        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.
        patch_size (`int`, *optional*, defaults to `16`):
            The size (resolution) of each patch.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        frequency_stride (`int`, *optional*, defaults to 10):
            Frequency stride to use when patchifying the spectrograms.
        time_stride (`int`, *optional*, defaults to 10):
            Temporal stride to use when patchifying the spectrograms.
        max_length (`int`, *optional*, defaults to 1024):
            Temporal dimension of the spectrograms.
        num_mel_bins (`int`, *optional*, defaults to 128):
            Frequency dimension of the spectrograms (number of Mel-frequency bins).

    Example:

    ```python
    >>> from transformers import ASTConfig, ASTModel

    >>> # Initializing a AST MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> configuration = ASTConfig()

    >>> # Initializing a model (with random weights) from the MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> model = ASTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "audio-spectrogram-transformer"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        patch_size=16,
        qkv_bias=True,
        frequency_stride=10,
        time_stride=10,
        max_length=1024,
        num_mel_bins=128,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.patch_size = patch_size
        self.qkv_bias = qkv_bias
        self.frequency_stride = frequency_stride
        self.time_stride = time_stride
        self.max_length = max_length
        self.num_mel_bins = num_mel_bins

# coding=utf-8
# Copyright 2022 Google AI and 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.
""" Audio Spectogram Transformer (AST) model configuration"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "MIT/ast-finetuned-audioset-10-10-0.4593": (
        "https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
    ),
}


class ASTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ASTModel`]. It is used to instantiate an AST
    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 AST
    [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
    architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        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.
        patch_size (`int`, *optional*, defaults to `16`):
            The size (resolution) of each patch.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        frequency_stride (`int`, *optional*, defaults to 10):
            Frequency stride to use when patchifying the spectrograms.
        time_stride (`int`, *optional*, defaults to 10):
            Temporal stride to use when patchifying the spectrograms.
        max_length (`int`, *optional*, defaults to 1024):
            Temporal dimension of the spectrograms.
        num_mel_bins (`int`, *optional*, defaults to 128):
            Frequency dimension of the spectrograms (number of Mel-frequency bins).

    Example:

    ```python
    >>> from transformers import ASTConfig, ASTModel

    >>> # Initializing a AST MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> configuration = ASTConfig()

    >>> # Initializing a model (with random weights) from the MIT/ast-finetuned-audioset-10-10-0.4593 style configuration
    >>> model = ASTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "audio-spectrogram-transformer"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        patch_size=16,
        qkv_bias=True,
        frequency_stride=10,
        time_stride=10,
        max_length=1024,
        num_mel_bins=128,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.patch_size = patch_size
        self.qkv_bias = qkv_bias
        self.frequency_stride = frequency_stride
        self.time_stride = time_stride
        self.max_length = max_length
        self.num_mel_bins = num_mel_bins
