# coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
#
# 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,
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""" LayoutLM model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


logger = logging.get_logger(__name__)

LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}


class LayoutLMConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
    LayoutLM 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 LayoutLM
    [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.

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


    Args:
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
            *inputs_ids* passed to the forward method of [`LayoutLMModel`].
        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"`, `"silu"` 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 2):
            The vocabulary size of the `token_type_ids` passed into [`LayoutLMModel`].
        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.
        max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum value that the 2D position embedding might ever used. Typically set this to something large
            just in case (e.g., 1024).

    Examples:

    ```python
    >>> from transformers import LayoutLMConfig, LayoutLMModel

    >>> # Initializing a LayoutLM configuration
    >>> configuration = LayoutLMConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = LayoutLMModel(configuration)

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

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        max_2d_position_embeddings=1024,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        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.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.max_2d_position_embeddings = max_2d_position_embeddings


class LayoutLMOnnxConfig(OnnxConfig):
    def __init__(
        self,
        config: PretrainedConfig,
        task: str = "default",
        patching_specs: List[PatchingSpec] = None,
    ):
        super().__init__(config, task=task, patching_specs=patching_specs)
        self.max_2d_positions = config.max_2d_position_embeddings - 1

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("bbox", {0: "batch", 1: "sequence"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
                ("token_type_ids", {0: "batch", 1: "sequence"}),
            ]
        )

    def generate_dummy_inputs(
        self,
        tokenizer: PreTrainedTokenizer,
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        """
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            tokenizer: The tokenizer associated with this model configuration
            batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
            seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
            is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
            framework: The framework (optional) the tokenizer will generate tensor for

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        """

        input_dict = super().generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        # Generate a dummy bbox
        box = [48, 84, 73, 128]

        if not framework == TensorType.PYTORCH:
            raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")

        if not is_torch_available():
            raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
        import torch

        batch_size, seq_length = input_dict["input_ids"].shape
        input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1)
        return input_dict
