import torch
import transformers

from ..registry import ModelAttribute, model_zoo

try:
    from transformers import LlamaConfig, LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
    HAS_LLAMA = True
except ImportError:
    HAS_LLAMA = False

if HAS_LLAMA:
    # ===============================
    # Register LLaMA
    # ===============================

    def data_gen():
        # the input ids are corresponding to the sentence
        # 'Hello, my dog is cute'
        #
        # the code is give below:
        # -----------------------------------
        # from transformers import LlamaTokenizerFast
        # tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
        # input = 'Hello, my dog is cute'
        # tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
        # -----------------------------------

        input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long()
        attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long()
        return dict(input_ids=input_ids, attention_mask=attention_mask)

    # label is needed for casual lm
    def data_gen_for_casual_lm():
        data = data_gen()
        labels = data['input_ids'].clone()
        data['labels'] = labels
        return data

    # transform the output to a dict
    output_transform_fn = lambda x: x

    # function to get the loss
    loss_fn = lambda output: output.last_hidden_state.mean()
    loss_fn_for_casual_lm = lambda output: output.loss
    loss_fn_for_seq_classification = lambda output: output.logits.mean()

    config = LlamaConfig(num_hidden_layers=4,
                         hidden_size=128,
                         intermediate_size=256,
                         num_attention_heads=4,
                         max_position_embeddings=128,
                         num_labels=16)

    # register the following models
    # transformers.LlamaModel,
    # transformers.LlamaForCausalLM,
    # transformers.LlamaForSequenceClassification,
    model_zoo.register(name='transformers_llama',
                       model_fn=lambda: transformers.LlamaModel(config),
                       data_gen_fn=data_gen,
                       output_transform_fn=output_transform_fn,
                       loss_fn=loss_fn,
                       model_attribute=ModelAttribute(has_control_flow=True))
    model_zoo.register(name='transformers_llama_for_casual_lm',
                       model_fn=lambda: transformers.LlamaForCausalLM(config),
                       data_gen_fn=data_gen_for_casual_lm,
                       output_transform_fn=output_transform_fn,
                       loss_fn=loss_fn_for_casual_lm,
                       model_attribute=ModelAttribute(has_control_flow=True))
    model_zoo.register(name='transformers_llama_for_sequence_classification',
                       model_fn=lambda: transformers.LlamaForSequenceClassification(config),
                       data_gen_fn=data_gen,
                       output_transform_fn=output_transform_fn,
                       loss_fn=loss_fn_for_seq_classification,
                       model_attribute=ModelAttribute(has_control_flow=True))
