# Copyright 2020 The HuggingFace 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.
# See the License for the specific language governing permissions and
# limitations under the License.

import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, snapshot_download

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import (
    TOKEN,
    CaptureLogger,
    TemporaryHubRepo,
    is_staging_test,
    require_flax,
    require_safetensors,
)
from transformers.utils import FLAX_WEIGHTS_NAME, SAFE_WEIGHTS_NAME, logging


if is_flax_available():
    import os

    from flax.core.frozen_dict import unfreeze
    from flax.traverse_util import flatten_dict

    from transformers import FlaxBertModel

    os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12"  # assumed parallelism: 8


@require_flax
@is_staging_test
class FlaxModelPushToHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._token = TOKEN
        HfFolder.save_token(TOKEN)

    def test_push_to_hub(self):
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            config = BertConfig(
                vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
            )
            model = FlaxBertModel(config)
            model.push_to_hub(tmp_repo.repo_id, token=self._token)

            new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)

            base_params = flatten_dict(unfreeze(model.params))
            new_params = flatten_dict(unfreeze(new_model.params))

            for key in base_params.keys():
                max_diff = (base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    def test_push_to_hub_via_save_pretrained(self):
        with TemporaryHubRepo(token=self._token) as tmp_repo:
            config = BertConfig(
                vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
            )
            model = FlaxBertModel(config)
            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token)

            new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)

            base_params = flatten_dict(unfreeze(model.params))
            new_params = flatten_dict(unfreeze(new_model.params))

            for key in base_params.keys():
                max_diff = (base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    def test_push_to_hub_in_organization(self):
        with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
            config = BertConfig(
                vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
            )
            model = FlaxBertModel(config)
            model.push_to_hub(tmp_repo.repo_id, token=self._token)

            new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)

            base_params = flatten_dict(unfreeze(model.params))
            new_params = flatten_dict(unfreeze(new_model.params))

            for key in base_params.keys():
                max_diff = (base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

    def test_push_to_hub_in_organization_via_save_pretrained(self):
        with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
            config = BertConfig(
                vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
            )
            model = FlaxBertModel(config)
            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token)

            new_model = FlaxBertModel.from_pretrained(tmp_repo.repo_id)

            base_params = flatten_dict(unfreeze(model.params))
            new_params = flatten_dict(unfreeze(new_model.params))

            for key in base_params.keys():
                max_diff = (base_params[key] - new_params[key]).sum().item()
                self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")


def check_models_equal(model1, model2):
    models_are_equal = True
    flat_params_1 = flatten_dict(model1.params)
    flat_params_2 = flatten_dict(model2.params)
    for key in flat_params_1.keys():
        if np.sum(np.abs(flat_params_1[key] - flat_params_2[key])) > 1e-4:
            models_are_equal = False

    return models_are_equal


@require_flax
class FlaxModelUtilsTest(unittest.TestCase):
    def test_model_from_pretrained_subfolder(self):
        config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
        model = FlaxBertModel(config)

        subfolder = "bert"
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(os.path.join(tmp_dir, subfolder))

            with self.assertRaises(OSError):
                _ = FlaxBertModel.from_pretrained(tmp_dir)

            model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)

        self.assertTrue(check_models_equal(model, model_loaded))

    def test_model_from_pretrained_subfolder_sharded(self):
        config = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
        model = FlaxBertModel(config)

        subfolder = "bert"
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(os.path.join(tmp_dir, subfolder), max_shard_size="10KB")

            with self.assertRaises(OSError):
                _ = FlaxBertModel.from_pretrained(tmp_dir)

            model_loaded = FlaxBertModel.from_pretrained(tmp_dir, subfolder=subfolder)

        self.assertTrue(check_models_equal(model, model_loaded))

    def test_model_from_pretrained_hub_subfolder(self):
        subfolder = "bert"
        model_id = "hf-internal-testing/tiny-random-bert-subfolder"

        with self.assertRaises(OSError):
            _ = FlaxBertModel.from_pretrained(model_id)

        model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)

        self.assertIsNotNone(model)

    def test_model_from_pretrained_hub_subfolder_sharded(self):
        subfolder = "bert"
        model_id = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
        with self.assertRaises(OSError):
            _ = FlaxBertModel.from_pretrained(model_id)

        model = FlaxBertModel.from_pretrained(model_id, subfolder=subfolder)

        self.assertIsNotNone(model)

    @require_safetensors
    def test_safetensors_save_and_load(self):
        model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(tmp_dir, safe_serialization=True)

            # No msgpack file, only a model.safetensors
            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME)))
            self.assertFalse(os.path.isfile(os.path.join(tmp_dir, FLAX_WEIGHTS_NAME)))

            new_model = FlaxBertModel.from_pretrained(tmp_dir)

        self.assertTrue(check_models_equal(model, new_model))

    @require_safetensors
    def test_safetensors_load_from_hub(self):
        """
        This test checks that we can load safetensors from a checkpoint that only has those on the Hub
        """
        flax_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

        # Can load from the Flax-formatted checkpoint
        safetensors_model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-safetensors-only")
        self.assertTrue(check_models_equal(flax_model, safetensors_model))

    @require_safetensors
    def test_safetensors_load_from_local(self):
        """
        This test checks that we can load safetensors from a checkpoint that only has those on the Hub
        """
        with tempfile.TemporaryDirectory() as tmp:
            location = snapshot_download("hf-internal-testing/tiny-bert-flax-only", cache_dir=tmp)
            flax_model = FlaxBertModel.from_pretrained(location)

        with tempfile.TemporaryDirectory() as tmp:
            location = snapshot_download("hf-internal-testing/tiny-bert-flax-safetensors-only", cache_dir=tmp)
            safetensors_model = FlaxBertModel.from_pretrained(location)

        self.assertTrue(check_models_equal(flax_model, safetensors_model))

    @require_safetensors
    def test_safetensors_load_from_hub_msgpack_before_safetensors(self):
        """
        This test checks that we'll first download msgpack weights before safetensors
        The safetensors file on that repo is a pt safetensors and therefore cannot be loaded without PyTorch
        """
        FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-msgpack")

    @require_safetensors
    def test_safetensors_load_from_local_msgpack_before_safetensors(self):
        """
        This test checks that we'll first download msgpack weights before safetensors
        The safetensors file on that repo is a pt safetensors and therefore cannot be loaded without PyTorch
        """
        with tempfile.TemporaryDirectory() as tmp:
            location = snapshot_download("hf-internal-testing/tiny-bert-pt-safetensors-msgpack", cache_dir=tmp)
            FlaxBertModel.from_pretrained(location)

    @require_safetensors
    def test_safetensors_flax_from_flax(self):
        model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")

        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(tmp_dir, safe_serialization=True)
            new_model = FlaxBertModel.from_pretrained(tmp_dir)

        self.assertTrue(check_models_equal(model, new_model))

    @require_safetensors
    def test_safetensors_flax_from_sharded_msgpack_with_sharded_safetensors_local(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            path = snapshot_download(
                "hf-internal-testing/tiny-bert-flax-safetensors-msgpack-sharded", cache_dir=tmp_dir
            )

            # This should not raise even if there are two types of sharded weights
            FlaxBertModel.from_pretrained(path)

    @require_safetensors
    def test_safetensors_flax_from_sharded_msgpack_with_sharded_safetensors_hub(self):
        # This should not raise even if there are two types of sharded weights
        # This should discard the safetensors weights in favor of the msgpack sharded weights
        FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-safetensors-msgpack-sharded")

    @require_safetensors
    def test_safetensors_from_pt_bf16(self):
        # This should not raise; should be able to load bf16-serialized torch safetensors without issue
        # and without torch.
        logger = logging.get_logger("transformers.modeling_flax_utils")

        with CaptureLogger(logger) as cl:
            FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-safetensors-bf16")

        self.assertTrue(
            "Some of the weights of FlaxBertModel were initialized in bfloat16 precision from the model checkpoint"
            in cl.out
        )
