# coding=utf-8
# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
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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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""" Longformer configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
    from ...onnx.config import PatchingSpec
    from ...tokenization_utils_base import PreTrainedTokenizerBase


logger = logging.get_logger(__name__)


class LongformerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LongformerModel`] or a [`TFLongformerModel`]. It
    is used to instantiate a Longformer model according to the specified arguments, defining the model architecture.

    This is the configuration class to store the configuration of a [`LongformerModel`]. It is used to instantiate an
    Longformer 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 LongFormer
    [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) architecture with a sequence
    length 4,096.

    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 30522):
            Vocabulary size of the Longformer model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`LongformerModel`] or [`TFLongformerModel`].
        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" (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"` 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 when calling [`LongformerModel`] or
            [`TFLongformerModel`].
        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.
        attention_window (`int` or `List[int]`, *optional*, defaults to 512):
            Size of an attention window around each token. If an `int`, use the same size for all layers. To specify a
            different window size for each layer, use a `List[int]` where `len(attention_window) == num_hidden_layers`.

    Example:

    ```python
    >>> from transformers import LongformerConfig, LongformerModel

    >>> # Initializing a Longformer configuration
    >>> configuration = LongformerConfig()

    >>> # Initializing a model from the configuration
    >>> model = LongformerModel(configuration)

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

    model_type = "longformer"

    def __init__(
        self,
        attention_window: Union[List[int], int] = 512,
        sep_token_id: int = 2,
        pad_token_id: int = 1,
        bos_token_id: int = 0,
        eos_token_id: int = 2,
        vocab_size: int = 30522,
        hidden_size: int = 768,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 12,
        intermediate_size: int = 3072,
        hidden_act: str = "gelu",
        hidden_dropout_prob: float = 0.1,
        attention_probs_dropout_prob: float = 0.1,
        max_position_embeddings: int = 512,
        type_vocab_size: int = 2,
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1e-12,
        onnx_export: bool = False,
        **kwargs,
    ):
        """Constructs LongformerConfig."""
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.attention_window = attention_window
        self.sep_token_id = sep_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        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.onnx_export = onnx_export


class LongformerOnnxConfig(OnnxConfig):
    def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: "List[PatchingSpec]" = None):
        super().__init__(config, task, patching_specs)
        config.onnx_export = True

    @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"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("global_attention_mask", dynamic_axis),
            ]
        )

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        outputs = super().outputs
        if self.task == "default":
            outputs["pooler_output"] = {0: "batch"}
        return outputs

    @property
    def atol_for_validation(self) -> float:
        """
        What absolute tolerance value to use during model conversion validation.

        Returns:
            Float absolute tolerance value.
        """
        return 1e-4

    @property
    def default_onnx_opset(self) -> int:
        # needs to be >= 14 to support tril operator
        return max(super().default_onnx_opset, 14)

    def generate_dummy_inputs(
        self,
        tokenizer: "PreTrainedTokenizerBase",
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        inputs = super().generate_dummy_inputs(
            preprocessor=tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        inputs["global_attention_mask"] = torch.zeros_like(inputs["input_ids"])
        # make every second token global
        inputs["global_attention_mask"][:, ::2] = 1

        return inputs
