# 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 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.
""" BiT 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__)

BIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}


class BitConfig(BackboneConfigMixin, PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
    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 BiT
    [google/bit-50](https://huggingface.co/google/bit-50) architecture.

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

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embedding_size (`int`, *optional*, defaults to 64):
            Dimensionality (hidden size) for the embedding layer.
        hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
            Dimensionality (hidden size) at each stage.
        depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
            Depth (number of layers) for each stage.
        layer_type (`str`, *optional*, defaults to `"preactivation"`):
            The layer to use, it can be either `"preactivation"` or `"bottleneck"`.
        hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
            are supported.
        global_padding (`str`, *optional*):
            Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`.
        num_groups (`int`, *optional*, defaults to `32`):
            Number of groups used for the `BitGroupNormActivation` layers.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            The drop path rate for the stochastic depth.
        embedding_dynamic_padding (`bool`, *optional*, defaults to `False`):
            Whether or not to make use of dynamic padding for the embedding layer.
        output_stride (`int`, *optional*, defaults to 32):
            The output stride of the model.
        width_factor (`int`, *optional*, defaults to 1):
            The width factor for the model.
        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 BitConfig, BitModel

    >>> # Initializing a BiT bit-50 style configuration
    >>> configuration = BitConfig()

    >>> # Initializing a model (with random weights) from the bit-50 style configuration
    >>> model = BitModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """
    model_type = "bit"
    layer_types = ["preactivation", "bottleneck"]
    supported_padding = ["SAME", "VALID"]

    def __init__(
        self,
        num_channels=3,
        embedding_size=64,
        hidden_sizes=[256, 512, 1024, 2048],
        depths=[3, 4, 6, 3],
        layer_type="preactivation",
        hidden_act="relu",
        global_padding=None,
        num_groups=32,
        drop_path_rate=0.0,
        embedding_dynamic_padding=False,
        output_stride=32,
        width_factor=1,
        out_features=None,
        out_indices=None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        if layer_type not in self.layer_types:
            raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
        if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                global_padding = global_padding.upper()
            else:
                raise ValueError(f"Padding strategy {global_padding} not supported")
        self.num_channels = num_channels
        self.embedding_size = embedding_size
        self.hidden_sizes = hidden_sizes
        self.depths = depths
        self.layer_type = layer_type
        self.hidden_act = hidden_act
        self.global_padding = global_padding
        self.num_groups = num_groups
        self.drop_path_rate = drop_path_rate
        self.embedding_dynamic_padding = embedding_dynamic_padding
        self.output_stride = output_stride
        self.width_factor = width_factor

        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.
""" BiT 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__)

BIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}


class BitConfig(BackboneConfigMixin, PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
    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 BiT
    [google/bit-50](https://huggingface.co/google/bit-50) architecture.

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

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embedding_size (`int`, *optional*, defaults to 64):
            Dimensionality (hidden size) for the embedding layer.
        hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
            Dimensionality (hidden size) at each stage.
        depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
            Depth (number of layers) for each stage.
        layer_type (`str`, *optional*, defaults to `"preactivation"`):
            The layer to use, it can be either `"preactivation"` or `"bottleneck"`.
        hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
            are supported.
        global_padding (`str`, *optional*):
            Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`.
        num_groups (`int`, *optional*, defaults to `32`):
            Number of groups used for the `BitGroupNormActivation` layers.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            The drop path rate for the stochastic depth.
        embedding_dynamic_padding (`bool`, *optional*, defaults to `False`):
            Whether or not to make use of dynamic padding for the embedding layer.
        output_stride (`int`, *optional*, defaults to 32):
            The output stride of the model.
        width_factor (`int`, *optional*, defaults to 1):
            The width factor for the model.
        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 BitConfig, BitModel

    >>> # Initializing a BiT bit-50 style configuration
    >>> configuration = BitConfig()

    >>> # Initializing a model (with random weights) from the bit-50 style configuration
    >>> model = BitModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """
    model_type = "bit"
    layer_types = ["preactivation", "bottleneck"]
    supported_padding = ["SAME", "VALID"]

    def __init__(
        self,
        num_channels=3,
        embedding_size=64,
        hidden_sizes=[256, 512, 1024, 2048],
        depths=[3, 4, 6, 3],
        layer_type="preactivation",
        hidden_act="relu",
        global_padding=None,
        num_groups=32,
        drop_path_rate=0.0,
        embedding_dynamic_padding=False,
        output_stride=32,
        width_factor=1,
        out_features=None,
        out_indices=None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        if layer_type not in self.layer_types:
            raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
        if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                global_padding = global_padding.upper()
            else:
                raise ValueError(f"Padding strategy {global_padding} not supported")
        self.num_channels = num_channels
        self.embedding_size = embedding_size
        self.hidden_sizes = hidden_sizes
        self.depths = depths
        self.layer_type = layer_type
        self.hidden_act = hidden_act
        self.global_padding = global_padding
        self.num_groups = num_groups
        self.drop_path_rate = drop_path_rate
        self.embedding_dynamic_padding = embedding_dynamic_padding
        self.output_stride = output_stride
        self.width_factor = width_factor

        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
        )
