# coding=utf-8

# Copyright 2019 HuggingFace Inc.

#

# 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.

from __future__ import absolute_import, division, print_function



import os

import copy

import json

import logging

import importlib

import random

import shutil

import unittest

import uuid

import tempfile



import sys



from transformers import is_tf_available, is_torch_available



from .utils import require_tf, slow



if is_tf_available():

    import tensorflow as tf

    import numpy as np

    from transformers import TFPreTrainedModel

    # from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP



if sys.version_info[0] == 2:

    import cPickle as pickle



    class TemporaryDirectory(object):

        """Context manager for tempfile.mkdtemp() so it's usable with "with" statement."""

        def __enter__(self):

            self.name = tempfile.mkdtemp()

            return self.name

        def __exit__(self, exc_type, exc_value, traceback):

            shutil.rmtree(self.name)

else:

    import pickle

    TemporaryDirectory = tempfile.TemporaryDirectory

    unicode = str



def _config_zero_init(config):

    configs_no_init = copy.deepcopy(config)

    for key in configs_no_init.__dict__.keys():

        if '_range' in key or '_std' in key:

            setattr(configs_no_init, key, 0.0)

    return configs_no_init



class TFCommonTestCases:



    @require_tf

    class TFCommonModelTester(unittest.TestCase):



        model_tester = None

        all_model_classes = ()

        test_torchscript = True

        test_pruning = True

        test_resize_embeddings = True



        def test_initialization(self):

            pass

            # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            # configs_no_init = _config_zero_init(config)

            # for model_class in self.all_model_classes:

            #     model = model_class(config=configs_no_init)

            #     for name, param in model.named_parameters():

            #         if param.requires_grad:

            #             self.assertIn(param.data.mean().item(), [0.0, 1.0],

            #             msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))



        def test_save_load(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                model = model_class(config)

                outputs = model(inputs_dict)



                with TemporaryDirectory() as tmpdirname:

                    model.save_pretrained(tmpdirname)

                    model = model_class.from_pretrained(tmpdirname)

                    after_outputs = model(inputs_dict)



                    # Make sure we don't have nans

                    out_1 = after_outputs[0].numpy()

                    out_2 = outputs[0].numpy()

                    out_1 = out_1[~np.isnan(out_1)]

                    out_2 = out_2[~np.isnan(out_2)]

                    max_diff = np.amax(np.abs(out_1 - out_2))

                    self.assertLessEqual(max_diff, 1e-5)



        def test_pt_tf_model_equivalence(self):

            if not is_torch_available():

                return



            import torch

            import transformers



            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beggining

                pt_model_class = getattr(transformers, pt_model_class_name)



                config.output_hidden_states = True

                tf_model = model_class(config)

                pt_model = pt_model_class(config)



                # Check we can load pt model in tf and vice-versa with model => model functions

                tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)

                pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)



                # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences

                pt_model.eval()

                pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))

                                      for name, key in inputs_dict.items())

                with torch.no_grad():

                    pto = pt_model(**pt_inputs_dict)

                tfo = tf_model(inputs_dict)

                max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))

                self.assertLessEqual(max_diff, 2e-2)



                # Check we can load pt model in tf and vice-versa with checkpoint => model functions

                with TemporaryDirectory() as tmpdirname:

                    pt_checkpoint_path = os.path.join(tmpdirname, 'pt_model.bin')

                    torch.save(pt_model.state_dict(), pt_checkpoint_path)

                    tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)



                    tf_checkpoint_path = os.path.join(tmpdirname, 'tf_model.h5')

                    tf_model.save_weights(tf_checkpoint_path)

                    pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)



                # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences

                pt_model.eval()

                pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))

                                      for name, key in inputs_dict.items())

                with torch.no_grad():

                    pto = pt_model(**pt_inputs_dict)

                tfo = tf_model(inputs_dict)

                max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))

                self.assertLessEqual(max_diff, 2e-2)



        def test_compile_tf_model(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')

            optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)

            loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

            metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')



            for model_class in self.all_model_classes:

                # Prepare our model

                model = model_class(config)



                # Let's load it from the disk to be sure we can use pretrained weights

                with TemporaryDirectory() as tmpdirname:

                    outputs = model(inputs_dict)  # build the model

                    model.save_pretrained(tmpdirname)

                    model = model_class.from_pretrained(tmpdirname)



                outputs_dict = model(input_ids)

                hidden_states = outputs_dict[0]



                # Add a dense layer on top to test intetgration with other keras modules

                outputs = tf.keras.layers.Dense(2, activation='softmax', name='outputs')(hidden_states)



                # Compile extended model

                extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])

                extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])



        def test_keyword_and_dict_args(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                model = model_class(config)

                outputs_dict = model(inputs_dict)



                inputs_keywords = copy.deepcopy(inputs_dict)

                input_ids = inputs_keywords.pop('input_ids')

                outputs_keywords = model(input_ids, **inputs_keywords)



                output_dict = outputs_dict[0].numpy()

                output_keywords = outputs_keywords[0].numpy()



                self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)



        def test_attention_outputs(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                config.output_attentions = True

                config.output_hidden_states = False

                model = model_class(config)

                outputs = model(inputs_dict)

                attentions = [t.numpy() for t in outputs[-1]]

                self.assertEqual(model.config.output_attentions, True)

                self.assertEqual(model.config.output_hidden_states, False)

                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

                self.assertListEqual(

                    list(attentions[0].shape[-3:]),

                    [self.model_tester.num_attention_heads,

                    self.model_tester.seq_length,

                    self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])

                out_len = len(outputs)



                # Check attention is always last and order is fine

                config.output_attentions = True

                config.output_hidden_states = True

                model = model_class(config)

                outputs = model(inputs_dict)

                self.assertEqual(out_len+1, len(outputs))

                self.assertEqual(model.config.output_attentions, True)

                self.assertEqual(model.config.output_hidden_states, True)



                attentions = [t.numpy() for t in outputs[-1]]

                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

                self.assertListEqual(

                    list(attentions[0].shape[-3:]),

                    [self.model_tester.num_attention_heads,

                    self.model_tester.seq_length,

                    self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])



        def test_hidden_states_output(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                config.output_hidden_states = True

                config.output_attentions = False

                model = model_class(config)

                outputs = model(inputs_dict)

                hidden_states = [t.numpy() for t in outputs[-1]]

                self.assertEqual(model.config.output_attentions, False)

                self.assertEqual(model.config.output_hidden_states, True)

                self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)

                self.assertListEqual(

                    list(hidden_states[0].shape[-2:]),

                    [self.model_tester.seq_length, self.model_tester.hidden_size])



        def test_model_common_attributes(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                model = model_class(config)

                assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)

                x = model.get_output_embeddings()

                assert x is None or isinstance(x, tf.keras.layers.Layer)



        def test_determinism(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()



            for model_class in self.all_model_classes:

                model = model_class(config)

                first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]

                self.assertTrue(tf.math.equal(first, second).numpy().all())



        def test_inputs_embeds(self):

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

            input_ids = inputs_dict["input_ids"]

            del inputs_dict["input_ids"]



            for model_class in self.all_model_classes:

                model = model_class(config)



                wte = model.get_input_embeddings()

                try:

                    x = wte(input_ids, mode="embedding")

                except:

                    try:

                        x = wte([input_ids], mode="embedding")

                    except:

                        try:

                            x = wte([input_ids, None, None, None], mode="embedding")

                        except:

                            if hasattr(self.model_tester, "embedding_size"):

                                x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)

                            else:

                                x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)

                # ^^ In our TF models, the input_embeddings can take slightly different forms,

                # so we try a few of them.

                # We used to fall back to just synthetically creating a dummy tensor of ones:

                #

                inputs_dict["inputs_embeds"] = x

                outputs = model(inputs_dict)





def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):

    """Creates a random int32 tensor of the shape within the vocab size."""

    if rng is None:

        rng = random.Random()



    total_dims = 1

    for dim in shape:

        total_dims *= dim



    values = []

    for _ in range(total_dims):

        values.append(rng.randint(0, vocab_size - 1))



    output = tf.constant(values,

                         shape=shape,

                         dtype=dtype if dtype is not None else tf.int32)



    return output





if __name__ == "__main__":

    unittest.main()

