# Copyright 2021, The HuggingFace Inc. 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.
"""Testing suite for the PyTorch OPT model."""

import copy
import tempfile
import unittest

import timeout_decorator  # noqa

from transformers import OPTConfig, is_torch_available
from transformers.testing_utils import (
    require_torch,
    require_torch_accelerator,
    require_torch_fp16,
    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 transformers import (
        GPT2Tokenizer,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
    )


def prepare_opt_inputs_dict(
    config,
    input_ids,
    decoder_input_ids=None,
    attention_mask=None,
    decoder_attention_mask=None,
):
    if attention_mask is None:
        attention_mask = input_ids.ne(config.pad_token_id)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }


class OPTModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=50,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        embed_dim=16,
        num_labels=3,
        word_embed_proj_dim=16,
        type_sequence_label_size=2,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_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.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.embed_dim = embed_dim
        self.num_labels = num_labels
        self.type_sequence_label_size = type_sequence_label_size
        self.word_embed_proj_dim = word_embed_proj_dim
        self.is_encoder_decoder = False

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
            3,
        )
        input_ids[:, -1] = self.eos_token_id  # Eos Token

        decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        config = self.get_config()
        inputs_dict = prepare_opt_inputs_dict(config, input_ids, decoder_input_ids)
        return config, inputs_dict

    def get_config(self):
        return OPTConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            embed_dim=self.embed_dim,
            is_encoder_decoder=False,
            word_embed_proj_dim=self.word_embed_proj_dim,
        )

    def get_pipeline_config(self):
        config = self.get_config()
        config.max_position_embeddings = 100
        return config

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict

    def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
        model = OPTModel(config=config).to(torch_device).eval()

        input_ids = inputs_dict["input_ids"]
        attention_mask = inputs_dict["attention_mask"]

        # first forward pass
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)

        output, past_key_values = outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_attn_mask = ids_tensor((self.batch_size, 3), 2)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)

        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
            "last_hidden_state"
        ]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

        # test no attention_mask works
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
        _, past_key_values = outputs.to_tuple()
        output_from_no_past = model(next_input_ids)["last_hidden_state"]

        output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]

        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))


@require_torch
class OPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (OPTModel, OPTForCausalLM, OPTForSequenceClassification, OPTForQuestionAnswering)
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": OPTModel,
            "text-classification": OPTForSequenceClassification,
            "text-generation": OPTForCausalLM,
            "zero-shot": OPTForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    is_encoder_decoder = False

    test_missing_keys = False

    # TODO: Fix the failed tests
    def is_pipeline_test_to_skip(
        self,
        pipeline_test_case_name,
        config_class,
        model_architecture,
        tokenizer_name,
        image_processor_name,
        feature_extractor_name,
        processor_name,
    ):
        if (
            pipeline_test_case_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast")
        ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

        return False

    def setUp(self):
        self.model_tester = OPTModelTester(self)
        self.config_tester = ConfigTester(self, config_class=OPTConfig)

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

    def test_save_load_strict(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
            self.assertEqual(info["missing_keys"], set())

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (OPTModel,):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))

            if not self.is_encoder_decoder:
                input_ids = inputs["input_ids"]
                del inputs["input_ids"]
            else:
                encoder_input_ids = inputs["input_ids"]
                decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids", None)

            wte = model.get_input_embeddings()
            if not self.is_encoder_decoder:
                inputs["inputs_embeds"] = wte(input_ids)
            else:
                inputs["inputs_embeds"] = wte(encoder_input_ids)
                inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)

            with torch.no_grad():
                model(**inputs)[0]

    @require_torch_fp16
    def test_generate_fp16(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs()
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        model = OPTForCausalLM(config).eval().to(torch_device)
        model.half()
        model.generate(input_ids, attention_mask=attention_mask)
        model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)

    def test_opt_sequence_classification_model(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs()
        config.num_labels = 3
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
        model = OPTForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))

    def test_opt_sequence_classification_model_for_multi_label(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs()
        config.num_labels = 3
        config.problem_type = "multi_label_classification"
        input_ids = input_dict["input_ids"]
        attention_mask = input_ids.ne(1).to(torch_device)
        sequence_labels = ids_tensor(
            [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
        ).to(torch.float)
        model = OPTForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))

    @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
    def test_model_parallelism(self):
        super().test_model_parallelism()

    @unittest.skip(reason="Position ids follow custom logic")
    def attention_mask_padding_matches_padding_free_with_position_ids(
        self, attn_implementation: str, fa_kwargs: bool = False
    ):
        pass


def assert_tensors_close(a, b, atol=1e-12, prefix=""):
    """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
    if a is None and b is None:
        return True
    try:
        if torch.allclose(a, b, atol=atol):
            return True
        raise Exception
    except Exception:
        pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
        if a.numel() > 100:
            msg = f"tensor values are {pct_different:.1%} percent different."
        else:
            msg = f"{a} != {b}"
        if prefix:
            msg = prefix + ": " + msg
        raise AssertionError(msg)


def _long_tensor(tok_lst):
    return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)


@require_torch
class OPTModelIntegrationTests(unittest.TestCase):
    @slow
    def test_inference_no_head(self):
        model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device)
        input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])

        with torch.no_grad():
            output = model(input_ids=input_ids).last_hidden_state

        expected_shape = torch.Size((1, 11, 512))
        self.assertEqual(output.shape, expected_shape)
        # expected value works for CPU, as well as GPU (with TF32 disabled)
        expected_slice = torch.tensor(
            [
                [-0.28726277, -1.9241608, -0.3058734],
                [-1.2737825, -0.13332152, -0.18766522],
                [0.41159445, 0.1191957, -1.3107123],
            ],
            device=torch_device,
        )
        assert_tensors_close(output[0, :3, :3], expected_slice, atol=5e-5)


@require_torch
@slow
class OPTEmbeddingsTest(unittest.TestCase):
    def setUp(self):
        super().setUp()
        self.path_model = "facebook/opt-350m"

    def test_load_model(self):
        try:
            _ = OPTForCausalLM.from_pretrained(self.path_model)
        except BaseException:
            self.fail("Failed loading model")

    def test_logits(self):
        model = OPTForCausalLM.from_pretrained(self.path_model)
        model = model.eval()
        tokenizer = GPT2Tokenizer.from_pretrained(self.path_model)

        prompts = [
            "Today is a beautiful day and I want to",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        inputs = tokenizer(prompts, return_tensors="pt", padding=True, add_special_tokens=False)
        logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(dim=-1)
        logits_meta = torch.Tensor(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]
        )
        assert torch.allclose(logits, logits_meta, atol=1e-4)


@slow
class OPTGenerationTest(unittest.TestCase):
    @property
    def prompts(self):
        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]

    def test_generation_pre_attn_layer_norm(self):
        model_id = "facebook/opt-125m"

        EXPECTED_OUTPUTS = [
            "Today is a beautiful day and I want to",
            "In the city of New York, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        predicted_outputs = []
        tokenizer = GPT2Tokenizer.from_pretrained(model_id)
        model = OPTForCausalLM.from_pretrained(model_id)

        for prompt in self.prompts:
            input_ids = tokenizer(prompt, return_tensors="pt").input_ids

            generated_ids = model.generate(input_ids, max_length=10)

            generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
            predicted_outputs += generated_string

        self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)

    def test_batch_generation(self):
        model_id = "facebook/opt-350m"

        tokenizer = GPT2Tokenizer.from_pretrained(model_id)
        model = OPTForCausalLM.from_pretrained(model_id)
        model.to(torch_device)

        tokenizer.padding_side = "left"

        # use different length sentences to test batching
        sentences = [
            "Hello, my dog is a little",
            "Today, I",
        ]

        inputs = tokenizer(sentences, return_tensors="pt", padding=True)
        input_ids = inputs["input_ids"].to(torch_device)

        outputs = model.generate(
            input_ids=input_ids,
            attention_mask=inputs["attention_mask"].to(torch_device),
            max_new_tokens=10,
        )

        inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
        output_non_padded = model.generate(input_ids=inputs_non_padded, max_new_tokens=10)

        inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
        output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=10)

        batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
        non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
        padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)

        expected_output_sentence = [
            "Hello, my dog is a little bit of a dork.\nI'm a",
            "Today, I was in the middle of a conversation with a friend",
        ]
        self.assertListEqual(expected_output_sentence, batch_out_sentence)
        self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])

    def test_generation_post_attn_layer_norm(self):
        model_id = "facebook/opt-350m"

        EXPECTED_OUTPUTS = [
            "Today is a beautiful day and I want to",
            "In the city of San Francisco, the city",
            "Paris is the capital of France and the capital",
            "Computers and mobile phones have taken over the",
        ]

        predicted_outputs = []
        tokenizer = GPT2Tokenizer.from_pretrained(model_id)
        model = OPTForCausalLM.from_pretrained(model_id)

        for prompt in self.prompts:
            input_ids = tokenizer(prompt, return_tensors="pt").input_ids

            generated_ids = model.generate(input_ids, max_length=10)

            generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
            predicted_outputs += generated_string

        self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)

    @require_torch_accelerator
    @require_torch_fp16
    def test_batched_nan_fp16(self):
        # a bug manifested starting at models facebook/opt-1.3 and larger when running batched generations,
        # therefore not using a tiny model, but the smallest model the problem was seen with which is opt-1.3b.
        # please refer to this github thread: https://github.com/huggingface/transformers/pull/17437 for more details
        model_name = "facebook/opt-1.3b"
        tokenizer = GPT2Tokenizer.from_pretrained(model_name, use_fast=False, padding_side="left")

        model = OPTForCausalLM.from_pretrained(model_name, dtype=torch.float16, use_cache=True).to(torch_device)
        model = model.eval()

        batch = tokenizer(["Who are you?", "Joe Biden is the president of"], padding=True, return_tensors="pt")

        input_ids = batch["input_ids"].to(torch_device)
        attention_mask = batch["attention_mask"].to(torch_device)

        with torch.no_grad():
            outputs = model(input_ids, attention_mask=attention_mask)
            self.assertFalse(
                torch.isnan(outputs.logits[0]).any().item()
            )  # the first logits could contain NaNs if it fails
