# coding=utf-8
# 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 copy
import random
import unittest

from transformers import TransfoXLConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
    from transformers.models.transfo_xl.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST


class TransfoXLModelTester:
    def __init__(
        self,
        parent,
        batch_size=14,
        seq_length=7,
        mem_len=30,
        clamp_len=15,
        is_training=False,
        use_labels=True,
        vocab_size=99,
        cutoffs=[10, 50, 80],
        hidden_size=32,
        d_embed=32,
        num_attention_heads=4,
        d_head=8,
        d_inner=128,
        div_val=2,
        num_hidden_layers=5,
        scope=None,
        seed=1,
        eos_token_id=0,
        num_labels=3,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.mem_len = mem_len
        self.key_length = self.seq_length + self.mem_len
        self.clamp_len = clamp_len
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.cutoffs = cutoffs
        self.hidden_size = hidden_size
        self.d_embed = d_embed
        self.num_attention_heads = num_attention_heads
        self.d_head = d_head
        self.d_inner = d_inner
        self.div_val = div_val
        self.num_hidden_layers = num_hidden_layers
        self.scope = scope
        self.seed = seed
        self.eos_token_id = eos_token_id
        self.num_labels = num_labels
        self.pad_token_id = self.vocab_size - 1

    def prepare_config_and_inputs(self):
        input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        lm_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        config = self.get_config()

        return (config, input_ids_1, input_ids_2, lm_labels)

    def get_config(self):
        return TransfoXLConfig(
            vocab_size=self.vocab_size,
            mem_len=self.mem_len,
            clamp_len=self.clamp_len,
            cutoffs=self.cutoffs,
            d_model=self.hidden_size,
            d_embed=self.d_embed,
            n_head=self.num_attention_heads,
            d_head=self.d_head,
            d_inner=self.d_inner,
            div_val=self.div_val,
            n_layer=self.num_hidden_layers,
            eos_token_id=self.eos_token_id,
            pad_token_id=self.pad_token_id,
        )

    def set_seed(self):
        random.seed(self.seed)
        torch.manual_seed(self.seed)

    def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
        model = TransfoXLModel(config)
        model.to(torch_device)
        model.eval()

        outputs1 = model(input_ids_1)
        outputs2 = model(input_ids_2, outputs1["mems"])
        outputs = {
            "hidden_states_1": outputs1["last_hidden_state"],
            "mems_1": outputs1["mems"],
            "hidden_states_2": outputs2["last_hidden_state"],
            "mems_2": outputs2["mems"],
        }
        return outputs

    def check_transfo_xl_model_output(self, result):
        self.parent.assertEqual(result["hidden_states_1"].shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result["hidden_states_2"].shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result["mems_1"]],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )
        self.parent.assertListEqual(
            [mem.shape for mem in result["mems_2"]],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
        model = TransfoXLLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        lm_logits_1 = model(input_ids_1)["prediction_scores"]
        outputs1 = model(input_ids_1, labels=lm_labels)
        lm_logits_2 = model(input_ids_2, mems=outputs1["mems"])["prediction_scores"]
        outputs2 = model(input_ids_2, labels=lm_labels, mems=outputs1["mems"])

        outputs = {
            "loss_1": outputs1["loss"],
            "losses_1": outputs1["losses"],
            "mems_1": outputs1["mems"],
            "lm_logits_1": lm_logits_1,
            "loss_2": outputs2["loss"],
            "losses_2": outputs2["losses"],
            "mems_2": outputs2["mems"],
            "lm_logits_2": lm_logits_2,
        }
        return outputs

    def check_transfo_xl_lm_head_output(self, result):
        self.parent.assertEqual(result["loss_1"].shape, ())
        self.parent.assertEqual(result["losses_1"].shape, (self.batch_size, self.seq_length - 1))
        self.parent.assertEqual(result["lm_logits_1"].shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result["mems_1"]],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

        self.parent.assertEqual(result["loss_2"].shape, ())
        self.parent.assertEqual(result["losses_2"].shape, (self.batch_size, self.seq_length - 1))
        self.parent.assertEqual(result["lm_logits_2"].shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result["mems_2"]],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_transfo_xl_lm_head_trainer_compatible_tuple(self, config, input_ids_1, input_ids_2, lm_labels):
        config.trainer_compatible = True
        model = TransfoXLLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        lm_logits_1 = model(input_ids_1, return_dict=False)[0]
        outputs1 = model(input_ids_1, labels=lm_labels, return_dict=False)
        loss_1, _, losses_1, mems_1 = outputs1[:4]
        lm_logits_2 = model(input_ids_2, mems=mems_1, return_dict=False)[0]
        outputs2 = model(input_ids_2, labels=lm_labels, mems=mems_1, return_dict=False)
        loss_2, _, losses_2, mems_2 = outputs2[:4]

        outputs = {
            "losses_1": losses_1,
            "mems_1": mems_1,
            "lm_logits_1": lm_logits_1,
            "loss_1": loss_1,
            "losses_2": losses_2,
            "mems_2": mems_2,
            "lm_logits_2": lm_logits_2,
            "loss_2": loss_2,
        }

        config.trainer_compatible = None
        return outputs

    def create_transfo_xl_lm_head_trainer_incompatible_tuple(self, config, input_ids_1, input_ids_2, lm_labels):
        config.trainer_compatible = False
        model = TransfoXLLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        lm_logits_1 = model(input_ids_1, return_dict=False)[0]
        outputs1 = model(input_ids_1, labels=lm_labels, return_dict=False)
        losses_1, _, mems_1 = outputs1[:3]
        loss_1 = outputs1[-1]
        lm_logits_2 = model(input_ids_2, mems=mems_1, return_dict=False)[0]
        outputs2 = model(input_ids_2, labels=lm_labels, mems=mems_1)
        losses_2, _, mems_2 = outputs2[:3]
        loss_2 = outputs2[-1]

        outputs = {
            "losses_1": losses_1,
            "mems_1": mems_1,
            "lm_logits_1": lm_logits_1,
            "loss_1": loss_1,
            "losses_2": losses_2,
            "mems_2": mems_2,
            "lm_logits_2": lm_logits_2,
            "loss_2": loss_2,
        }

        config.trainer_compatible = None
        return outputs

    def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels):
        config.num_labels = self.num_labels
        model = TransfoXLForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids_1)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
        inputs_dict = {"input_ids": input_ids_1}
        return config, inputs_dict


@require_torch
class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (TransfoXLModel, TransfoXLLMHeadModel, TransfoXLForSequenceClassification) if is_torch_available() else ()
    )
    all_generative_model_classes = (TransfoXLLMHeadModel,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": TransfoXLModel,
            "text-classification": TransfoXLForSequenceClassification,
            "text-generation": TransfoXLLMHeadModel,
            "zero-shot": TransfoXLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_pruning = False
    test_resize_embeddings = True
    test_mismatched_shapes = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name == "TextGenerationPipelineTests":
            # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
            # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
            # tokenizer.
            return True

        return False

    def check_cutoffs_and_n_token(
        self, copied_cutoffs, layer, model_embed, model, model_class, resized_value, vocab_size
    ):
        # Check that the cutoffs were modified accordingly
        for i in range(len(copied_cutoffs)):
            if i < layer:
                self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i])
                if model_class == TransfoXLLMHeadModel:
                    self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i])
                if i < len(model.config.cutoffs):
                    self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i])
            else:
                self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i] + resized_value)
                if model_class == TransfoXLLMHeadModel:
                    self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i] + resized_value)
                if i < len(model.config.cutoffs):
                    self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i] + resized_value)

        self.assertEqual(model_embed.n_token, vocab_size + resized_value)
        if model_class == TransfoXLLMHeadModel:
            self.assertEqual(model.crit.n_token, vocab_size + resized_value)

    def setUp(self):
        self.model_tester = TransfoXLModelTester(self)
        self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_transfo_xl_model(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs)
        self.model_tester.check_transfo_xl_model_output(output_result)

    def test_transfo_xl_lm_head(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()

        output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
        self.model_tester.check_transfo_xl_lm_head_output(output_result)

        output_result = self.model_tester.create_transfo_xl_lm_head_trainer_compatible_tuple(*config_and_inputs)
        self.model_tester.check_transfo_xl_lm_head_output(output_result)

        output_result = self.model_tester.create_transfo_xl_lm_head_trainer_incompatible_tuple(*config_and_inputs)
        self.model_tester.check_transfo_xl_lm_head_output(output_result)

    def test_transfo_xl_sequence_classification_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs)

    def test_retain_grad_hidden_states_attentions(self):
        # xlnet cannot keep gradients in attentions or hidden states
        return

    @require_torch_multi_gpu
    def test_multi_gpu_data_parallel_forward(self):
        # Opt-out of this test.
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TransfoXLModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_resize_tokens_embeddings(self):
        (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.test_resize_embeddings:
            return

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config)
            model.to(torch_device)

            if self.model_tester.is_training is False:
                model.eval()

            model_vocab_size = config.vocab_size
            # Retrieve the embeddings and clone theme
            model_embed = model.resize_token_embeddings(model_vocab_size)
            cloned_embeddings = [emb.weight.clone() for emb in model_embed.emb_layers]
            # Retrieve the cutoffs and copy them
            copied_cutoffs = copy.copy(model_embed.cutoffs)

            test_layers = list(range(config.div_val))
            for layer in test_layers:
                # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
                model_embed = model.resize_token_embeddings(model_vocab_size + 10, layer)
                self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
                # Check that it actually resizes the embeddings matrix
                self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] + 10)
                # Check that the cutoffs were modified accordingly
                self.check_cutoffs_and_n_token(
                    copied_cutoffs, layer, model_embed, model, model_class, 10, model_vocab_size
                )

                # Check that the model can still do a forward pass successfully (every parameter should be resized)
                model(**inputs_dict)

                # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
                model_embed = model.resize_token_embeddings(model_vocab_size - 5, layer)
                self.assertEqual(model.config.vocab_size, model_vocab_size - 5)
                # Check that it actually resizes the embeddings matrix
                self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] - 5)
                # Check that the cutoffs were modified accordingly
                self.check_cutoffs_and_n_token(
                    copied_cutoffs, layer, model_embed, model, model_class, -5, model_vocab_size
                )

                # Check that the model can still do a forward pass successfully (every parameter should be resized)
                # Input ids should be clamped to the maximum size of the vocabulary
                inputs_dict["input_ids"].clamp_(max=model_vocab_size - 5 - 1)
                model(**inputs_dict)

                # Check that adding and removing tokens has not modified the first part of the embedding matrix.
                models_equal = True
                for p1, p2 in zip(cloned_embeddings[layer], model_embed.emb_layers[layer].weight):
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False

                self.assertTrue(models_equal)

                # Reset model embeddings to original size
                model.resize_token_embeddings(model_vocab_size, layer)
                self.assertEqual(model_vocab_size, model.config.vocab_size)
                self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0])

    def test_resize_embeddings_untied(self):
        # transfo-xl requires special resize for lm-head
        return

    def _check_attentions_for_generate(
        self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(attentions, tuple)
        self.assertListEqual(
            [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
        )
        self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

        for idx, iter_attentions in enumerate(attentions):
            tgt_len = min_length if idx == 0 else (min_length - 2)
            src_len = (min_length + config.mem_len) if idx == 0 else (min_length + config.mem_len - 2)

            expected_shape = (
                batch_size * num_beam_groups,
                config.num_attention_heads,
                tgt_len,
                src_len,
            )

            # check attn size
            self.assertListEqual(
                [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
            )

    def _check_hidden_states_for_generate(
        self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(hidden_states, tuple)
        self.assertListEqual(
            [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
            [True] * len(hidden_states),
        )
        self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

        for idx, iter_hidden_states in enumerate(hidden_states):
            seq_len = min_length if idx == 0 else min_length - 2
            expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
            # check hidden size
            self.assertListEqual(
                [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
                [expected_shape] * len(iter_hidden_states),
            )

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "cluster_weight") and module.cluster_weight is not None:
            module.cluster_weight.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)
        if hasattr(module, "cluster_bias") and module.cluster_bias is not None:
            module.cluster_bias.data.fill_(3)

        if hasattr(module, "emb_projs"):
            for i in range(len(module.emb_projs)):
                if module.emb_projs[i] is not None:
                    nn.init.constant_(module.emb_projs[i], 0.0003)
        if hasattr(module, "out_projs"):
            for i in range(len(module.out_projs)):
                if module.out_projs[i] is not None:
                    nn.init.constant_(module.out_projs[i], 0.0003)

        for param in ["r_emb", "r_w_bias", "r_r_bias", "r_bias"]:
            if hasattr(module, param) and getattr(module, param) is not None:
                weight = getattr(module, param)
                weight.data.fill_(3)


@require_torch
class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_transfo_xl_wt103(self):
        model = TransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
        model.to(torch_device)

        # fmt: off
        input_ids = torch.tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]],dtype=torch.long,device=torch_device)  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        expected_output_ids = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,142,1298,188,2,29546,113,8,3654,4,1,1109,7136,833,3,13,1645,4,29546,11,104,7,1,1109,532,7129,2,10,83507,2,1162,1123,2,6,7245,10,2,5,11,104,7,1,1109,532,7129,2,10,24,24,10,22,10,13,770,5863,4,7245,10]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family ( except for
        #  Alexei and Maria ) are discovered. The voice of young son, Tsarevich Alexei
        #  Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young
        #  Grigori Rasputin is asked by his father and a group of men to perform magic.
        #  Rasputin has a vision and denounces one of the men as a horse thief. Although
        #  his father initially slaps him for making such an accusation, Rasputin watches
        #  as the man is chased outside and beaten. Twenty years later, Rasputin sees a
        #  vision of the Virgin Mary, prompting him to become a priest. Rasputin quickly
        #  becomes famous, with people, even a bishop, begging for his blessing. In the
        #  early 20th century, Rasputin became a symbol of the Russian Orthodox Church.
        #  The image of Rasputin was used in the Russian national anthem, " Nearer, My God,
        #  to Heaven ", and was used in the Russian national anthem, " " ( " The Great Spirit
        #  of Heaven "

        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
