# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2020, Microsoft and the HuggingFace Inc. team.
#
# 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.
""" DeBERTa-v2 model configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


logger = logging.get_logger(__name__)

DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}


class DebertaV2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`DebertaV2Model`]. It is used to instantiate a
    DeBERTa-v2 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 DeBERTa
    [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) architecture.

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

    Arguments:
        vocab_size (`int`, *optional*, defaults to 128100):
            Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`DebertaV2Model`].
        hidden_size (`int`, *optional*, defaults to 1536):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 24):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 6144):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` 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.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 0):
            The vocabulary size of the `token_type_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`].
        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-7):
            The epsilon used by the layer normalization layers.
        relative_attention (`bool`, *optional*, defaults to `True`):
            Whether use relative position encoding.
        max_relative_positions (`int`, *optional*, defaults to -1):
            The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
            as `max_position_embeddings`.
        pad_token_id (`int`, *optional*, defaults to 0):
            The value used to pad input_ids.
        position_biased_input (`bool`, *optional*, defaults to `False`):
            Whether add absolute position embedding to content embedding.
        pos_att_type (`List[str]`, *optional*):
            The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
            `["p2c", "c2p"]`, `["p2c", "c2p"]`.
        layer_norm_eps (`float`, optional, defaults to 1e-12):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import DebertaV2Config, DebertaV2Model

    >>> # Initializing a DeBERTa-v2 microsoft/deberta-v2-xlarge style configuration
    >>> configuration = DebertaV2Config()

    >>> # Initializing a model (with random weights) from the microsoft/deberta-v2-xlarge style configuration
    >>> model = DebertaV2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "deberta-v2"

    def __init__(
        self,
        vocab_size=128100,
        hidden_size=1536,
        num_hidden_layers=24,
        num_attention_heads=24,
        intermediate_size=6144,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=0,
        initializer_range=0.02,
        layer_norm_eps=1e-7,
        relative_attention=False,
        max_relative_positions=-1,
        pad_token_id=0,
        position_biased_input=True,
        pos_att_type=None,
        pooler_dropout=0,
        pooler_hidden_act="gelu",
        **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.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.relative_attention = relative_attention
        self.max_relative_positions = max_relative_positions
        self.pad_token_id = pad_token_id
        self.position_biased_input = position_biased_input

        # Backwards compatibility
        if type(pos_att_type) == str:
            pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")]

        self.pos_att_type = pos_att_type
        self.vocab_size = vocab_size
        self.layer_norm_eps = layer_norm_eps

        self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
        self.pooler_dropout = pooler_dropout
        self.pooler_hidden_act = pooler_hidden_act


class DebertaV2OnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
        else:
            dynamic_axis = {0: "batch", 1: "sequence"}
        if self._config.type_vocab_size > 0:
            return OrderedDict(
                [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]
            )
        else:
            return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])

    @property
    def default_onnx_opset(self) -> int:
        return 12

    def generate_dummy_inputs(
        self,
        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
        batch_size: int = -1,
        seq_length: int = -1,
        num_choices: int = -1,
        is_pair: bool = False,
        framework: Optional["TensorType"] = None,
        num_channels: int = 3,
        image_width: int = 40,
        image_height: int = 40,
        tokenizer: "PreTrainedTokenizerBase" = None,
    ) -> Mapping[str, Any]:
        dummy_inputs = super().generate_dummy_inputs(preprocessor=preprocessor, framework=framework)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
            del dummy_inputs["token_type_ids"]
        return dummy_inputs

# coding=utf-8
# Copyright 2020, Microsoft and the HuggingFace Inc. team.
#
# 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.
""" DeBERTa-v2 model configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


logger = logging.get_logger(__name__)

DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}


class DebertaV2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`DebertaV2Model`]. It is used to instantiate a
    DeBERTa-v2 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 DeBERTa
    [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) architecture.

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

    Arguments:
        vocab_size (`int`, *optional*, defaults to 128100):
            Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`DebertaV2Model`].
        hidden_size (`int`, *optional*, defaults to 1536):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 24):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 6144):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` 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.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 0):
            The vocabulary size of the `token_type_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`].
        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-7):
            The epsilon used by the layer normalization layers.
        relative_attention (`bool`, *optional*, defaults to `True`):
            Whether use relative position encoding.
        max_relative_positions (`int`, *optional*, defaults to -1):
            The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
            as `max_position_embeddings`.
        pad_token_id (`int`, *optional*, defaults to 0):
            The value used to pad input_ids.
        position_biased_input (`bool`, *optional*, defaults to `False`):
            Whether add absolute position embedding to content embedding.
        pos_att_type (`List[str]`, *optional*):
            The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
            `["p2c", "c2p"]`, `["p2c", "c2p"]`.
        layer_norm_eps (`float`, optional, defaults to 1e-12):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import DebertaV2Config, DebertaV2Model

    >>> # Initializing a DeBERTa-v2 microsoft/deberta-v2-xlarge style configuration
    >>> configuration = DebertaV2Config()

    >>> # Initializing a model (with random weights) from the microsoft/deberta-v2-xlarge style configuration
    >>> model = DebertaV2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "deberta-v2"

    def __init__(
        self,
        vocab_size=128100,
        hidden_size=1536,
        num_hidden_layers=24,
        num_attention_heads=24,
        intermediate_size=6144,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=0,
        initializer_range=0.02,
        layer_norm_eps=1e-7,
        relative_attention=False,
        max_relative_positions=-1,
        pad_token_id=0,
        position_biased_input=True,
        pos_att_type=None,
        pooler_dropout=0,
        pooler_hidden_act="gelu",
        **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.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.relative_attention = relative_attention
        self.max_relative_positions = max_relative_positions
        self.pad_token_id = pad_token_id
        self.position_biased_input = position_biased_input

        # Backwards compatibility
        if type(pos_att_type) == str:
            pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")]

        self.pos_att_type = pos_att_type
        self.vocab_size = vocab_size
        self.layer_norm_eps = layer_norm_eps

        self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
        self.pooler_dropout = pooler_dropout
        self.pooler_hidden_act = pooler_hidden_act


class DebertaV2OnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
        else:
            dynamic_axis = {0: "batch", 1: "sequence"}
        if self._config.type_vocab_size > 0:
            return OrderedDict(
                [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]
            )
        else:
            return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])

    @property
    def default_onnx_opset(self) -> int:
        return 12

    def generate_dummy_inputs(
        self,
        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
        batch_size: int = -1,
        seq_length: int = -1,
        num_choices: int = -1,
        is_pair: bool = False,
        framework: Optional["TensorType"] = None,
        num_channels: int = 3,
        image_width: int = 40,
        image_height: int = 40,
        tokenizer: "PreTrainedTokenizerBase" = None,
    ) -> Mapping[str, Any]:
        dummy_inputs = super().generate_dummy_inputs(preprocessor=preprocessor, framework=framework)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
            del dummy_inputs["token_type_ids"]
        return dummy_inputs
