import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.electra.modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
    )


class FlaxElectraModelTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_attention_mask=True,
        use_token_type_ids=True,
        use_labels=True,
        vocab_size=99,
        embedding_size=24,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_choices=4,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_attention_mask = use_attention_mask
        self.use_token_type_ids = use_token_type_ids
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.embedding_size = embedding_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.num_choices = num_choices
        super().__init__()

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        attention_mask = None
        if self.use_attention_mask:
            attention_mask = random_attention_mask([self.batch_size, self.seq_length])

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = ElectraConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            embedding_size=self.embedding_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            type_vocab_size=self.type_vocab_size,
            initializer_range=self.initializer_range,
        )

        return config, input_ids, token_type_ids, attention_mask

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, token_type_ids, attention_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict


@require_flax
class FlaxElectraModelTest(FlaxModelTesterMixin, unittest.TestCase):
    test_head_masking = True

    all_model_classes = (
        (
            FlaxElectraModel,
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForPreTraining,
            FlaxElectraForTokenClassification,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForMultipleChoice,
            FlaxElectraForSequenceClassification,
        )
        if is_flax_available()
        else ()
    )

    def setUp(self):
        self.model_tester = FlaxElectraModelTester(self)

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            if model_class_name == FlaxElectraForMaskedLM:
                model = model_class_name.from_pretrained("google/electra-small-generator")
            else:
                model = model_class_name.from_pretrained("google/electra-small-discriminator")
            outputs = model(np.ones((1, 1)))
            self.assertIsNotNone(outputs)
