import torch
import transformers

from ..registry import ModelAttribute, model_zoo

# ===============================
# Register single-sentence ALBERT
# ===============================
BATCH_SIZE = 2
SEQ_LENGTH = 16


def data_gen_fn():
    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
    return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)


def data_gen_for_pretrain():
    inputs = data_gen_fn()
    inputs['labels'] = inputs['input_ids'].clone()
    inputs['sentence_order_label'] = torch.zeros(BATCH_SIZE, dtype=torch.int64)
    return inputs


output_transform_fn = lambda x: x

config = transformers.AlbertConfig(embedding_size=128,
                                   hidden_size=128,
                                   num_hidden_layers=2,
                                   num_attention_heads=4,
                                   intermediate_size=256)

model_zoo.register(name='transformers_albert',
                   model_fn=lambda: transformers.AlbertModel(config, add_pooling_layer=False),
                   data_gen_fn=data_gen_fn,
                   output_transform_fn=output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_pretraining',
                   model_fn=lambda: transformers.AlbertForPreTraining(config),
                   data_gen_fn=data_gen_for_pretrain,
                   output_transform_fn=lambda x: dict(loss=x.loss),
                   model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_masked_lm',
                   model_fn=lambda: transformers.AlbertForMaskedLM(config),
                   data_gen_fn=data_gen_fn,
                   output_transform_fn=output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_sequence_classification',
                   model_fn=lambda: transformers.AlbertForSequenceClassification(config),
                   data_gen_fn=data_gen_fn,
                   output_transform_fn=output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_token_classification',
                   model_fn=lambda: transformers.AlbertForTokenClassification(config),
                   data_gen_fn=data_gen_fn,
                   output_transform_fn=output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))

# ===============================
# Register multi-sentence ALBERT
# ===============================


def data_gen_for_qa():
    question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
    inputs = tokenizer(question, text, return_tensors="pt")
    return inputs


def data_gen_for_mcq():
    prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
    choice0 = "It is eaten with a fork and a knife."
    choice1 = "It is eaten while held in the hand."
    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
    encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
    encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
    return encoding


model_zoo.register(name='transformers_albert_for_question_answering',
                   model_fn=lambda: transformers.AlbertForQuestionAnswering(config),
                   data_gen_fn=data_gen_for_qa,
                   output_transform_fn=output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_albert_for_multiple_choice',
                   model_fn=lambda: transformers.AlbertForMultipleChoice(config),
                   data_gen_fn=data_gen_for_mcq,
                   output_transform_fn=output_transform_fn,
                   model_attribute=ModelAttribute(has_control_flow=True))