# Copyright 2022 Alibaba Group and 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.
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""" OFA model configuration"""
import warnings

from transformers import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger()

OFA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    'ofa-medium': 'https://huggingface.co/ofa-base/resolve/main/config.json',
    # OFA models are implemeted to be compatible with both huggingface
    # and modelscope frameworks. For all OFA models available on huggingface,
    # please refer to https://huggingface.co/models?filter=ofa
}


class OFAConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`~OFAModel`]. It is used to instantiate an OFA
    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 OFA [ofa-base](https://huggingface.co/ofa-base)
    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 50265):
            Vocabulary size of the OFA model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`~OFAModel`] or [`~TFOFAModel`].
        d_model (`int`, *optional*, defaults to 1024):
            Dimension of the layers and the pooler layer.
        encoder_layers (`int`, *optional*, defaults to 12):
            Number of encoder layers.
        decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        encoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`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.
        dropout (`float`, *optional*, defaults to 0.1):
            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.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        classifier_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for classifier.
        max_position_embeddings (`int`, *optional*, defaults to 1024):
            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).
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        encoder_layerdrop: (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        decoder_layerdrop: (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    """

    model_type = 'ofa'
    keys_to_ignore_at_inference = ['past_key_values']

    attribute_map = {
        'num_attention_heads': 'encoder_attention_heads',
        'hidden_size': 'd_model'
    }

    def __init__(self,
                 vocab_size=59457,
                 max_position_embeddings=1024,
                 encoder_layers=4,
                 encoder_ffn_dim=512 * 4,
                 encoder_attention_heads=8,
                 decoder_layers=4,
                 decoder_ffn_dim=512 * 4,
                 decoder_attention_heads=8,
                 encoder_layerdrop=0.0,
                 decoder_layerdrop=0.0,
                 use_cache=True,
                 is_encoder_decoder=True,
                 activation_function='gelu',
                 d_model=512,
                 dropout=0.1,
                 attention_dropout=0.0,
                 activation_dropout=0.0,
                 init_std=0.02,
                 classifier_dropout=0.0,
                 scale_embedding=False,
                 pad_token_id=1,
                 bos_token_id=0,
                 decoder_start_token_id=0,
                 eos_token_id=2,
                 forced_eos_token_id=2,
                 encoder_normalize_before=True,
                 decoder_normalize_before=True,
                 normformer=True,
                 encoder_drop_path_rate=0.0,
                 decoder_drop_path_rate=0.0,
                 layernorm_embedding=True,
                 patch_layernorm_embedding=True,
                 resnet_type='resnet101',
                 resnet_model_path=None,
                 resnet_drop_path_rate=0.0,
                 token_bucket_size=256,
                 image_bucket_size=42,
                 add_type_embedding=True,
                 share_decoder_input_output_embed=True,
                 attn_scale_factor=2.,
                 code_layernorm_embedding=True,
                 code_image_size=128,
                 entangle_position_embedding=False,
                 interpolate_position=False,
                 orig_patch_image_size=224,
                 share_attn_bias=False,
                 use_image_feature=True,
                 disable_entangle=False,
                 use_ofasys=False,
                 vit_type='vit_base',
                 vit_drop_path_rate=0.0,
                 **kwargs):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        self.encoder_normalize_before = encoder_normalize_before
        self.decoder_normalize_before = decoder_normalize_before
        self.normformer = normformer
        self.encoder_drop_path_rate = encoder_drop_path_rate
        self.decoder_drop_path_rate = decoder_drop_path_rate
        self.layernorm_embedding = layernorm_embedding
        self.patch_layernorm_embedding = patch_layernorm_embedding
        self.resnet_type = resnet_type
        self.resnet_model_path = resnet_model_path
        self.resnet_drop_path_rate = resnet_drop_path_rate
        self.token_bucket_size = token_bucket_size
        self.image_bucket_size = image_bucket_size
        self.add_type_embedding = add_type_embedding
        self.share_decoder_input_output_embed = share_decoder_input_output_embed
        self.attn_scale_factor = attn_scale_factor
        self.code_layernorm_embedding = code_layernorm_embedding
        self.code_image_size = code_image_size
        self.entangle_position_embedding = entangle_position_embedding
        self.interpolate_position = interpolate_position
        self.orig_patch_image_size = orig_patch_image_size

        self.share_attn_bias = share_attn_bias
        self.use_image_feature = use_image_feature
        self.disable_entangle = disable_entangle
        self.use_ofasys = use_ofasys
        self.vit_type = vit_type
        self.vit_drop_path_rate = vit_drop_path_rate

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            forced_eos_token_id=forced_eos_token_id,
            **kwargs,
        )

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get(
                'force_bos_token_to_be_generated', False):
            self.forced_bos_token_id = self.bos_token_id
            warnings.warn(
                f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                'The config can simply be saved and uploaded again to be fixed.'
            )
