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+# 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.
+"""GroupViT model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any, Optional
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+if TYPE_CHECKING:
+    from ...processing_utils import ProcessorMixin
+    from ...utils import TensorType
+
+
+logger = logging.get_logger(__name__)
+
+
+class GroupViTTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GroupViTTextModel`]. It is used to instantiate an
+    GroupViT 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 GroupViT
+    [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 49408):
+            Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented
+            by the `inputs_ids` passed when calling [`GroupViTModel`].
+        hidden_size (`int`, *optional*, defaults to 256):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 1024):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 4):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        max_position_embeddings (`int`, *optional*, defaults to 77):
+            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).
+        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 1.0):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+
+    Example:
+
+    ```python
+    >>> from transformers import GroupViTTextConfig, GroupViTTextModel
+
+    >>> # Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration
+    >>> configuration = GroupViTTextConfig()
+
+    >>> model = GroupViTTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "groupvit_text_model"
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=49408,
+        hidden_size=256,
+        intermediate_size=1024,
+        num_hidden_layers=12,
+        num_attention_heads=4,
+        max_position_embeddings=77,
+        hidden_act="quick_gelu",
+        layer_norm_eps=1e-5,
+        dropout=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=1.0,
+        pad_token_id=1,
+        bos_token_id=49406,
+        eos_token_id=49407,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+
+
+class GroupViTVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GroupViTVisionModel`]. It is used to instantiate
+    an GroupViT 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 GroupViT
+    [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) 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 384):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 1536):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        depths (`list[int]`, *optional*, defaults to [6, 3, 3]):
+            The number of layers in each encoder block.
+        num_group_tokens (`list[int]`, *optional*, defaults to [64, 8, 0]):
+            The number of group tokens for each stage.
+        num_output_groups (`list[int]`, *optional*, defaults to [64, 8, 8]):
+            The number of output groups for each stage, 0 means no group.
+        num_attention_heads (`int`, *optional*, defaults to 6):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        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"` `"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            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.
+        initializer_factor (`float`, *optional*, defaults to 1.0):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+
+    Example:
+
+    ```python
+    >>> from transformers import GroupViTVisionConfig, GroupViTVisionModel
+
+    >>> # Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration
+    >>> configuration = GroupViTVisionConfig()
+
+    >>> model = GroupViTVisionModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "groupvit_vision_model"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        hidden_size=384,
+        intermediate_size=1536,
+        depths=[6, 3, 3],
+        num_hidden_layers=12,
+        num_group_tokens=[64, 8, 0],
+        num_output_groups=[64, 8, 8],
+        num_attention_heads=6,
+        image_size=224,
+        patch_size=16,
+        num_channels=3,
+        hidden_act="gelu",
+        layer_norm_eps=1e-5,
+        dropout=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=1.0,
+        assign_eps=1.0,
+        assign_mlp_ratio=[0.5, 4],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.depths = depths
+        if num_hidden_layers != sum(depths):
+            logger.warning(
+                f"Manually setting num_hidden_layers to {num_hidden_layers}, but we expect num_hidden_layers ="
+                f" sum(depth) = {sum(depths)}"
+            )
+        self.num_hidden_layers = num_hidden_layers
+        self.num_group_tokens = num_group_tokens
+        self.num_output_groups = num_output_groups
+        self.num_attention_heads = num_attention_heads
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.hidden_act = hidden_act
+        self.layer_norm_eps = layer_norm_eps
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.assign_eps = assign_eps
+        self.assign_mlp_ratio = assign_mlp_ratio
+
+
+class GroupViTConfig(PretrainedConfig):
+    r"""
+    [`GroupViTConfig`] is the configuration class to store the configuration of a [`GroupViTModel`]. It is used to
+    instantiate a GroupViT model according to the specified arguments, defining the text model and vision model
+    configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the GroupViT
+    [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        text_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`GroupViTTextConfig`].
+        vision_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`GroupViTVisionConfig`].
+        projection_dim (`int`, *optional*, defaults to 256):
+            Dimensionality of text and vision projection layers.
+        projection_intermediate_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of intermediate layer of text and vision projection layers.
+        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
+            The initial value of the *logit_scale* parameter. Default is used as per the original GroupViT
+            implementation.
+        kwargs (*optional*):
+            Dictionary of keyword arguments.
+    """
+
+    model_type = "groupvit"
+    sub_configs = {"text_config": GroupViTTextConfig, "vision_config": GroupViTVisionConfig}
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        projection_dim=256,
+        projection_intermediate_dim=4096,
+        logit_scale_init_value=2.6592,
+        **kwargs,
+    ):
+        # If `_config_dict` exist, we use them for the backward compatibility.
+        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
+        # of confusion!).
+        text_config_dict = kwargs.pop("text_config_dict", None)
+        vision_config_dict = kwargs.pop("vision_config_dict", None)
+
+        super().__init__(**kwargs)
+
+        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
+        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
+        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
+        if text_config_dict is not None:
+            if text_config is None:
+                text_config = {}
+
+            # This is the complete result when using `text_config_dict`.
+            _text_config_dict = GroupViTTextConfig(**text_config_dict).to_dict()
+
+            # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
+            for key, value in _text_config_dict.items():
+                if key in text_config and value != text_config[key] and key != "transformers_version":
+                    # If specified in `text_config_dict`
+                    if key in text_config_dict:
+                        message = (
+                            f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
+                            f'The value `text_config_dict["{key}"]` will be used instead.'
+                        )
+                    # If inferred from default argument values (just to be super careful)
+                    else:
+                        message = (
+                            f"`text_config_dict` is provided which will be used to initialize `GroupViTTextConfig`. "
+                            f'The value `text_config["{key}"]` will be overridden.'
+                        )
+                    logger.info(message)
+
+            # Update all values in `text_config` with the ones in `_text_config_dict`.
+            text_config.update(_text_config_dict)
+
+        if vision_config_dict is not None:
+            if vision_config is None:
+                vision_config = {}
+
+            # This is the complete result when using `vision_config_dict`.
+            _vision_config_dict = GroupViTVisionConfig(**vision_config_dict).to_dict()
+            # convert keys to string instead of integer
+            if "id2label" in _vision_config_dict:
+                _vision_config_dict["id2label"] = {
+                    str(key): value for key, value in _vision_config_dict["id2label"].items()
+                }
+
+            # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
+            for key, value in _vision_config_dict.items():
+                if key in vision_config and value != vision_config[key] and key != "transformers_version":
+                    # If specified in `vision_config_dict`
+                    if key in vision_config_dict:
+                        message = (
+                            f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
+                            f'values. The value `vision_config_dict["{key}"]` will be used instead.'
+                        )
+                    # If inferred from default argument values (just to be super careful)
+                    else:
+                        message = (
+                            f"`vision_config_dict` is provided which will be used to initialize `GroupViTVisionConfig`."
+                            f' The value `vision_config["{key}"]` will be overridden.'
+                        )
+                    logger.info(message)
+
+            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
+            vision_config.update(_vision_config_dict)
+
+        if text_config is None:
+            text_config = {}
+            logger.info("`text_config` is `None`. Initializing the `GroupViTTextConfig` with default values.")
+
+        if vision_config is None:
+            vision_config = {}
+            logger.info("`vision_config` is `None`. initializing the `GroupViTVisionConfig` with default values.")
+
+        self.text_config = GroupViTTextConfig(**text_config)
+        self.vision_config = GroupViTVisionConfig(**vision_config)
+
+        self.projection_dim = projection_dim
+        self.projection_intermediate_dim = projection_intermediate_dim
+        self.logit_scale_init_value = logit_scale_init_value
+        self.initializer_range = 0.02
+        self.initializer_factor = 1.0
+        self.output_segmentation = False
+
+
+class GroupViTOnnxConfig(OnnxConfig):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("input_ids", {0: "batch", 1: "sequence"}),
+                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
+                ("attention_mask", {0: "batch", 1: "sequence"}),
+            ]
+        )
+
+    @property
+    def outputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("logits_per_image", {0: "batch"}),
+                ("logits_per_text", {0: "batch"}),
+                ("text_embeds", {0: "batch"}),
+                ("image_embeds", {0: "batch"}),
+            ]
+        )
+
+    @property
+    def atol_for_validation(self) -> float:
+        return 1e-4
+
+    def generate_dummy_inputs(
+        self,
+        processor: "ProcessorMixin",
+        batch_size: int = -1,
+        seq_length: int = -1,
+        framework: Optional["TensorType"] = None,
+    ) -> Mapping[str, Any]:
+        text_input_dict = super().generate_dummy_inputs(
+            processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
+        )
+        image_input_dict = super().generate_dummy_inputs(
+            processor.image_processor, batch_size=batch_size, framework=framework
+        )
+        return {**text_input_dict, **image_input_dict}
+
+    @property
+    def default_onnx_opset(self) -> int:
+        return 14
+
+
+__all__ = ["GroupViTConfig", "GroupViTOnnxConfig", "GroupViTTextConfig", "GroupViTVisionConfig"]