# 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 os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class TFBenchmarkTest(unittest.TestCase):
    def check_results_dict_not_empty(self, results):
        for model_result in results.values():
            for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
                result = model_result["result"][batch_size][sequence_length]
                self.assertIsNotNone(result)

    def test_inference_no_configs_eager(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            eager_mode=True,
            multi_process=False,
        )
        benchmark = TensorFlowBenchmark(benchmark_args)
        results = benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result)
        self.check_results_dict_not_empty(results.memory_inference_result)

    def test_inference_no_configs_only_pretrain(self):
        MODEL_ID = "sgugger/tiny-distilbert-classification"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            multi_process=False,
            only_pretrain_model=True,
        )
        benchmark = TensorFlowBenchmark(benchmark_args)
        results = benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result)
        self.check_results_dict_not_empty(results.memory_inference_result)

    def test_inference_no_configs_graph(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,