# Copyright 2021 AlQuraishi Laboratory
#
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
import numpy as np
import unittest
from openfold.model.template import (
    TemplatePointwiseAttention,
    TemplatePairStack,
)
from openfold.utils.tensor_utils import tree_map
import tests.compare_utils as compare_utils
from tests.config import consts
from tests.data_utils import random_template_feats

if(compare_utils.alphafold_is_installed()):
    alphafold = compare_utils.import_alphafold()
    import jax
    import haiku as hk


class TestTemplatePointwiseAttention(unittest.TestCase):
    def test_shape(self): 
        batch_size = consts.batch_size
        n_seq = consts.n_seq
        c_t = consts.c_t
        c_z = consts.c_z
        c = 26
        no_heads = 13
        n_res = consts.n_res
        inf = 1e7
        
        tpa = TemplatePointwiseAttention(
            c_t, c_z, c, no_heads, chunk_size=4, inf=inf
        )

        t = torch.rand((batch_size, n_seq, n_res, n_res, c_t))
        z = torch.rand((batch_size, n_res, n_res, c_z))

        z_update = tpa(t, z)

        self.assertTrue(z_update.shape == z.shape)


class TestTemplatePairStack(unittest.TestCase):
    def test_shape(self):
        batch_size = consts.batch_size
        c_t = consts.c_t
        c_hidden_tri_att = 7
        c_hidden_tri_mul = 7
        no_blocks = 2
        no_heads = 4
        pt_inner_dim = 15
        dropout = 0.25
        n_templ = consts.n_templ
        n_res = consts.n_res
        blocks_per_ckpt = None
        chunk_size = 4
        inf=1e7
        eps=1e-7

        tpe = TemplatePairStack(
            c_t,
            c_hidden_tri_att=c_hidden_tri_att,
            c_hidden_tri_mul=c_hidden_tri_mul,
            no_blocks=no_blocks,
            no_heads=no_heads,
            pair_transition_n=pt_inner_dim,
            dropout_rate=dropout,
            blocks_per_ckpt=None,
            chunk_size=chunk_size,
            inf=inf,
            eps=eps,
        )

        t = torch.rand((batch_size, n_templ, n_res, n_res, c_t))
        mask = torch.randint(0, 2, (batch_size, n_templ, n_res, n_res))
        shape_before = t.shape
        t = tpe(t, mask)
        shape_after = t.shape

        self.assertTrue(shape_before == shape_after)

    @compare_utils.skip_unless_alphafold_installed()
    def test_compare(self):
        def run_template_pair_stack(pair_act, pair_mask):
            config = compare_utils.get_alphafold_config()
            c_ee = config.model.embeddings_and_evoformer
            tps = alphafold.model.modules.TemplatePairStack(
                c_ee.template.template_pair_stack, 
                config.model.global_config,
                name="template_pair_stack"
            )
            act = tps(pair_act, pair_mask, is_training=False)
            ln = hk.LayerNorm([-1], True, True, name="output_layer_norm")
            act = ln(act)
            return act
        
        f = hk.transform(run_template_pair_stack)
    
        n_res = consts.n_res
    
        pair_act = np.random.rand(n_res, n_res, consts.c_t).astype(np.float32)
        pair_mask = np.random.randint(
            low=0, high=2, size=(n_res, n_res)
        ).astype(np.float32)
       
        params = compare_utils.fetch_alphafold_module_weights(
            "alphafold/alphafold_iteration/evoformer/template_embedding/" +
            "single_template_embedding/template_pair_stack"
        )
        params.update(compare_utils.fetch_alphafold_module_weights(
            "alphafold/alphafold_iteration/evoformer/template_embedding/" +
            "single_template_embedding/output_layer_norm"
        ))
    
        out_gt = f.apply(
            params, jax.random.PRNGKey(42), pair_act, pair_mask
        ).block_until_ready()
        out_gt = torch.as_tensor(np.array(out_gt))
   
        model = compare_utils.get_global_pretrained_openfold()
        out_repro = model.template_pair_stack(
            torch.as_tensor(pair_act).cuda(),
            torch.as_tensor(pair_mask).cuda(),
            _mask_trans=False,
        ).cpu()
    
        self.assertTrue(torch.max(torch.abs(out_gt - out_repro)) < consts.eps)


class Template(unittest.TestCase):
    @compare_utils.skip_unless_alphafold_installed()
    def test_compare(self):
        def test_template_embedding(pair, batch, mask_2d):
            config = compare_utils.get_alphafold_config()
            te = alphafold.model.modules.TemplateEmbedding(
                config.model.embeddings_and_evoformer.template, 
                config.model.global_config
            )
            act = te(pair, batch, mask_2d, is_training=False)
            return act
    
        f = hk.transform(test_template_embedding)
    
        n_res = consts.n_res
        n_templ = consts.n_templ
    
        pair_act = np.random.rand(n_res, n_res, consts.c_z).astype(np.float32)
        batch = random_template_feats(n_templ, n_res)
        pair_mask = np.random.randint(0, 2, (n_res, n_res)).astype(np.float32)
        
        # Fetch pretrained parameters (but only from one block)]
        params = compare_utils.fetch_alphafold_module_weights(
            "alphafold/alphafold_iteration/evoformer/template_embedding"
        )
    
        out_gt = f.apply(
            params, jax.random.PRNGKey(42), pair_act, batch, pair_mask
        ).block_until_ready()
        out_gt = torch.as_tensor(np.array(out_gt))
    
        inds = np.random.randint(0, 21, (n_res,))
        batch["target_feat"] = np.eye(22)[inds]
   
        model = compare_utils.get_global_pretrained_openfold()
        out_repro = model.embed_templates(
            {k:torch.as_tensor(v).cuda() for k,v in batch.items()},
            torch.as_tensor(pair_act).cuda(),
            torch.as_tensor(pair_mask).cuda(),
            templ_dim=0,
        )
        out_repro = out_repro["template_pair_embedding"]
        out_repro = out_repro.cpu()
    
        self.assertTrue(torch.max(torch.abs(out_gt - out_repro) < consts.eps))


if __name__ == "__main__":
    unittest.main() 
