# coding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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""" CLIP model configuration"""

import copy
import os
from typing import Union

from ...utils.log import logger
from ..configuration_utils import (
    PretrainedConfig,
    convert_to_legacy_config,
    flatten_model_config,
)

__all__ = [
    "CLIPTextConfig",
    "CLIPVisionConfig",
    "CLIPConfig",
]


class Old2NewPretrainedConfig(PretrainedConfig):
    old_config_dict = [
        "image_resolution",
        "vision_layers",
        "vision_heads",
        "vision_embed_dim",
        "vision_patch_size",
        "vision_mlp_ratio",
        "vision_hidden_act",
        "max_text_length",
        "vocab_size",
        "text_embed_dim",
        "text_heads",
        "text_layers",
        "text_hidden_act",
        "projection_dim",
        "initializer_range",
        "initializer_factor",
        "logit_scale_init_value",
        "init_class",
    ]
    text_name_mapping = {
        "max_text_length": "max_position_embeddings",
        "vocab_size": "vocab_size",
        "text_embed_dim": "hidden_size",
        "text_heads": "num_attention_heads",
        "text_layers": "num_hidden_layers",
        "text_hidden_act": "hidden_act",
        "initializer_range": "initializer_range",
        "initializer_factor": "initializer_factor",
        "projection_dim": "projection_dim",
    }
    vision_name_mapping = {
        "image_resolution": "image_size",
        "vision_layers": "num_hidden_layers",
        "vision_heads": "num_attention_heads",
        "vision_embed_dim": "hidden_size",
        "vision_patch_size": "patch_size",
        "vision_hidden_act": "hidden_act",
        "initializer_range": "initializer_range",
        "initializer_factor": "initializer_factor",
        "projection_dim": "projection_dim",
    }

    @classmethod
    def from_dict(cls, config_dict, **kwargs) -> "PretrainedConfig":
        """
        Instantiates a [`PretrainedConfig`] from a Python dictionary of parameters.

        Args:
            config_dict (`Dict[str, Any]`):
                Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
                retrieved from a pretrained checkpoint by leveraging the [`~PretrainedConfig.get_config_dict`] method.
            kwargs (`Dict[str, Any]`):
                Additional parameters from which to initialize the configuration object.

        Returns:
            [`PretrainedConfig`]: The configuration object instantiated from those parameters.
        """
        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
        # Those arguments may be passed along for our internal telemetry.
        # We remove them so they don't appear in `return_unused_kwargs`.
        # convert local config to legacy config
        # do standard config map: there are some old-school pretrained-config not refactored.
        config_dict = convert_to_legacy_config(cls.attribute_map, config_dict)
        config_dict = flatten_model_config(config_dict)

        # check old_config?
        is_old_config = "vision_layers" in config_dict or "text_layers" in config_dict
        if is_old_config:
            # convert to new_config
            old_config_dict = {}
            for old_name in cls.old_config_dict:
                value = config_dict.pop(old_name, None)
                if value is not None:
                    old_config_dict[old_name] = value

            # convert text config
            if cls.model_type in ["clip", "clip_text_model"]:
                text_config = {}
                for old_name, new_name in cls.text_name_mapping.items():
                    old_value = old_config_dict.get(old_name, None)
                    if old_value is not None:
                        text_config[new_name] = old_value
                if "hidden_size" in text_config:
                    text_config["intermediate_size"] = 4 * text_config["hidden_size"]

                if cls.model_type == "clip":
                    config_dict["text_config_dict"] = text_config
                else:
                    config_dict.update(text_config)

            # convert vision config
            if cls.model_type in ["clip", "clip_vision_model"]:
                vision_config = {}
                for old_name, new_name in cls.vision_name_mapping.items():
                    old_value = old_config_dict.get(old_name, None)
                    if old_value is not None:
                        vision_config[new_name] = old_value
                if "hidden_size" in vision_config:
                    radio = old_config_dict.get("vision_mlp_ratio", 4)
                    vision_config["intermediate_size"] = radio * vision_config["hidden_size"]
                if cls.model_type == "clip":
                    config_dict["vision_config_dict"] = vision_config
                else:
                    config_dict.update(vision_config)

            if cls.model_type == "clip":
                # convert common config
                if "projection_dim" in old_config_dict:
                    config_dict["projection_dim"] = old_config_dict["projection_dim"]
                if "logit_scale_init_value" in old_config_dict:
                    config_dict["logit_scale_init_value"] = old_config_dict["logit_scale_init_value"]

        config = cls(**config_dict)

        if hasattr(config, "pruned_heads"):
            config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())

        # Update config with kwargs if needed
        if "num_labels" in kwargs and "id2label" in kwargs:
            num_labels = kwargs["num_labels"]
            id2label = kwargs["id2label"] if kwargs["id2label"] is not None else []
            if len(id2label) != num_labels:
                raise ValueError(
                    f"You passed along `num_labels={num_labels }` with an incompatible id to label map: "
                    f"{kwargs['id2label']}. Since those arguments are inconsistent with each other, you should remove "
                    "one of them."
                )
        to_remove = []
        for key, value in kwargs.items():
            if hasattr(config, key):
                setattr(config, key, value)
                if key != "dtype":
                    to_remove.append(key)
        for key in to_remove:
            kwargs.pop(key, None)

        logger.info(f"Model config {config}")
        if return_unused_kwargs:
            return config, kwargs
        else:
            return config


class CLIPTextConfig(Old2NewPretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate an CLIP
    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 CLIP
    [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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 CLIP text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`CLIPModel`].
        hidden_size (`int`, *optional*, defaults to 512):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2048):
            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 8):
            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 probabilitiy 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):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from paddlenlp.transformers import CLIPTextConfig, CLIPTextModel

    >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
    >>> configuration = CLIPTextConfig()

    >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
    >>> model = CLIPTextModel(configuration)

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

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        projection_dim=512,
        num_hidden_layers=12,
        num_attention_heads=8,
        max_position_embeddings=77,
        hidden_act="quick_gelu",
        layer_norm_eps=0.00001,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        # This differs from `CLIPTokenizer`'s default and from openai/clip
        # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
        pad_token_id=1,
        bos_token_id=49406,
        eos_token_id=49407,
        **kwargs
    ):
        kwargs["return_dict"] = kwargs.pop("return_dict", True)
        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.projection_dim = projection_dim
        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

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> PretrainedConfig:
        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        # get the text config dict if we are loading from CLIPConfig
        if config_dict.get("model_type") == "clip":
            projection_dim = config_dict.get("projection_dim", None)
            config_dict = config_dict["text_config"]
            if projection_dim is not None:
                config_dict["projection_dim"] = projection_dim

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )
        return cls.from_dict(config_dict, **kwargs)


class CLIPVisionConfig(Old2NewPretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate an CLIP
    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 CLIP
    [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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.
        intermediate_size (`int`, *optional*, defaults to 3072):
            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 12):
            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 32):
            The size (resolution) of each patch.
        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.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy 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):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from paddlenlp.transformers import CLIPVisionConfig, CLIPVisionModel

    >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
    >>> configuration = CLIPVisionConfig()

    >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
    >>> model = CLIPVisionModel(configuration)

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

    model_type = "clip_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        projection_dim=512,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        patch_size=32,
        hidden_act="quick_gelu",
        layer_norm_eps=0.00001,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        **kwargs
    ):
        kwargs["return_dict"] = kwargs.pop("return_dict", True)
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.projection_dim = projection_dim
        self.dropout = dropout
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.image_size = image_size
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> PretrainedConfig:
        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        # get the vision config dict if we are loading from CLIPConfig
        if config_dict.get("model_type") == "clip":
            projection_dim = config_dict.get("projection_dim", None)
            config_dict = config_dict["vision_config"]
            if projection_dim is not None:
                config_dict["projection_dim"] = projection_dim

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class CLIPConfig(Old2NewPretrainedConfig):
    r"""
    [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
    CLIP 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 CLIP
    [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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 [`CLIPTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`CLIPVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality 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 CLIP implementation.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from paddlenlp.transformers import CLIPConfig, CLIPModel

    >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
    >>> configuration = CLIPConfig()

    >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
    >>> model = CLIPModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

    >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig

    >>> # Initializing a CLIPText and CLIPVision configuration
    >>> config_text = CLIPTextConfig()
    >>> config_vision = CLIPVisionConfig()

    >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
    ```"""

    model_type = "clip"
    is_composition = True

    def __init__(
        self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
    ):
        kwargs["return_dict"] = kwargs.pop("return_dict", True)
        super().__init__(**kwargs)

        # If `_config_dict` exist, we use them for the backward compatibility.
        text_config_dict = kwargs.pop("text_config_dict", None)
        vision_config_dict = kwargs.pop("vision_config_dict", None)
        if text_config_dict is not None:
            text_config = text_config_dict
        if vision_config_dict is not None:
            vision_config = vision_config_dict

        if text_config is None:
            text_config = {}
            logger.info("text_config is None. Initializing the CLIPTextConfig with default values.")

        if vision_config is None:
            vision_config = {}
            logger.info("vision_config is None. initializing the CLIPVisionConfig with default values.")

        text_config["projection_dim"] = projection_dim
        vision_config["projection_dim"] = projection_dim
        self.text_config = CLIPTextConfig(**text_config)
        self.vision_config = CLIPVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.initializer_factor = 1.0

    @classmethod
    def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs):
        r"""
        Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
        configuration.

        Returns:
            [`CLIPConfig`]: An instance of a configuration object
        """

        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)

    def to_dict(self, *args, **kwargs):
        """
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        """
        output = copy.deepcopy(self.__dict__)
        output["text_config"] = self.text_config.to_dict()
        output["vision_config"] = self.vision_config.to_dict()
        output["model_type"] = self.__class__.model_type
        return output
