# coding=utf-8
# Copyright 2023 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 Persimmon model. """


import gc
import unittest

from parameterized import parameterized

from transformers import PersimmonConfig, is_torch_available, set_seed
from transformers.testing_utils import (
    backend_empty_cache,
    require_bitsandbytes,
    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 (
        AutoTokenizer,
        PersimmonForCausalLM,
        PersimmonForSequenceClassification,
        PersimmonModel,
    )
    from transformers.models.persimmon.modeling_persimmon import (
        PersimmonDynamicNTKScalingRotaryEmbedding,
        PersimmonLinearScalingRotaryEmbedding,
        PersimmonRotaryEmbedding,
    )


# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester with Llama->Persimmon
class PersimmonModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=False,
        use_labels=True,
        vocab_size=99,
        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_labels=3,
        num_choices=4,
        pad_token_id=0,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_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.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_labels = num_labels
        self.num_choices = num_choices
        self.pad_token_id = pad_token_id
        self.scope = scope

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

        input_mask = None
        if self.use_input_mask:
            input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device)

        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)

        sequence_labels = None
        token_labels = None
        choice_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        return PersimmonConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_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,
            is_decoder=False,
            initializer_range=self.initializer_range,
            pad_token_id=self.pad_token_id,
        )

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = PersimmonModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_model_as_decoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = PersimmonModel(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
        )
        result = model(input_ids, attention_mask=input_mask)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = PersimmonForCausalLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = PersimmonForCausalLM(config=config)
        model.to(torch_device)
        model.eval()

        # first forward pass
        outputs = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

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

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

        output_from_no_past = model(
            next_input_ids,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["hidden_states"][0]

        # 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))

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


@require_torch
class PersimmonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (PersimmonModel, PersimmonForCausalLM, PersimmonForSequenceClassification) if is_torch_available() else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": PersimmonModel,
            "text-classification": PersimmonForSequenceClassification,
            # TODO (ydshieh): check why these two fail. Fix them or skip them in a better way.
            # "text-generation": PersimmonForCausalLM,
            # "zero-shot": PersimmonForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    all_generative_model_classes = (PersimmonForCausalLM,) if is_torch_available() else ()
    test_headmasking = False
    test_pruning = False

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.setUp with Llama->Persimmon
    def setUp(self):
        self.model_tester = PersimmonModelTester(self)
        self.config_tester = ConfigTester(self, config_class=PersimmonConfig, hidden_size=37)

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_config
    def test_config(self):
        self.config_tester.run_common_tests()

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_various_embeddings
    def test_model_various_embeddings(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config_and_inputs[0].position_embedding_type = type
            self.model_tester.create_and_check_model(*config_and_inputs)

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model with Llama->Persimmon,llama->persimmon
    def test_persimmon_sequence_classification_model(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        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 = PersimmonForSequenceClassification(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))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_single_label with Llama->Persimmon,llama->persimmon
    def test_persimmon_sequence_classification_model_for_single_label(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.num_labels = 3
        config.problem_type = "single_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], self.model_tester.type_sequence_label_size)
        model = PersimmonForSequenceClassification(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))

    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_multi_label with Llama->Persimmon,llama->persimmon
    def test_persimmon_sequence_classification_model_for_multi_label(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
        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 = PersimmonForSequenceClassification(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("Persimmon buffers include complex numbers, which breaks this test")
    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_save_load_fast_init_from_base
    def test_save_load_fast_init_from_base(self):
        pass

    @parameterized.expand([("linear",), ("dynamic",)])
    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->Persimmon
    def test_model_rope_scaling_from_config(self, scaling_type):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        short_input = ids_tensor([1, 10], config.vocab_size)
        long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        original_model = PersimmonModel(config)
        original_model.to(torch_device)
        original_model.eval()
        original_short_output = original_model(short_input).last_hidden_state
        original_long_output = original_model(long_input).last_hidden_state

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
        scaled_model = PersimmonModel(config)
        scaled_model.to(torch_device)
        scaled_model.eval()
        scaled_short_output = scaled_model(short_input).last_hidden_state
        scaled_long_output = scaled_model(long_input).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
        else:
            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))

    # Copied from tests.models.falcon.test_modeling_falcon.FalconModelTest.test_model_rope_scaling with Falcon->Persimmon
    def test_model_rope_scaling(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        hidden_size = config.hidden_size
        num_heads = config.num_attention_heads
        head_dim = hidden_size // num_heads
        scaling_factor = 10
        short_input_length = 10
        long_input_length = int(config.max_position_embeddings * 1.5)

        # Inputs
        x = torch.randn(1, dtype=torch.float32, device=torch_device)  # used exlusively to get the dtype and the device

        # Sanity check original RoPE
        original_rope = PersimmonRotaryEmbedding(
            head_dim,
            max_position_embeddings=config.max_position_embeddings,
            base=config.rope_theta,
        ).to(torch_device)
        original_cos_short, original_sin_short = original_rope(x, short_input_length)
        original_cos_long, original_sin_long = original_rope(x, long_input_length)
        torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :])
        torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :])

        # Sanity check linear RoPE scaling
        # New position "x" should match original position with index "x/scaling_factor"
        linear_scaling_rope = PersimmonLinearScalingRotaryEmbedding(
            head_dim,
            max_position_embeddings=config.max_position_embeddings,
            base=config.rope_theta,
            scaling_factor=scaling_factor,
        ).to(torch_device)
        linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length)
        linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length)
        torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :])
        torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :])
        for new_position in range(0, long_input_length, scaling_factor):
            original_position = int(new_position // scaling_factor)
            torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :])
            torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :])

        # Sanity check Dynamic NTK RoPE scaling
        # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
        # with scaling_factor (or that `inv_freq` decreases)
        ntk_scaling_rope = PersimmonDynamicNTKScalingRotaryEmbedding(
            head_dim,
            max_position_embeddings=config.max_position_embeddings,
            base=config.rope_theta,
            scaling_factor=scaling_factor,
        ).to(torch_device)
        ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length)
        ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length)
        torch.testing.assert_close(ntk_cos_short, original_cos_short)
        torch.testing.assert_close(ntk_sin_short, original_sin_short)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(ntk_cos_long, original_cos_long)
        with self.assertRaises(AssertionError):
            torch.testing.assert_close(ntk_sin_long, original_sin_long)
        self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())


@require_torch
class PersimmonIntegrationTest(unittest.TestCase):
    @slow
    @require_torch_accelerator
    @require_bitsandbytes
    def test_model_8b_chat_logits(self):
        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
        model = PersimmonForCausalLM.from_pretrained(
            "adept/persimmon-8b-chat", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16
        )
        out = model(torch.tensor([input_ids], device=torch_device)).logits

        EXPECTED_MEAN = torch.tensor(
            [[-11.4726, -11.1495, -11.2694, -11.2223, -10.9452, -11.0663, -11.0031, -11.1028]]
        )
        # change dtype to `torch.float32` before calling `mean` to avoid `nan` values
        torch.testing.assert_close(out.cpu().to(torch.float32).mean(-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4)
        # fmt: off
        EXPECTED_SLICE = torch.tensor(
            [-16.9062, -16.9062, -16.9062, -16.9062, -16.8906, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9531, -16.9062, -16.9062],
            dtype=torch.float16
        )
        # fmt: on
        torch.testing.assert_close(out.cpu()[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)

        backend_empty_cache(torch_device)
        del model
        gc.collect()

    @slow
    @require_torch_accelerator
    @require_torch_fp16
    @require_bitsandbytes
    def test_model_8b_chat_greedy_generation(self):
        EXPECTED_TEXT_COMPLETION = """human: Simply put, the theory of relativity states that?\n\nadept: The theory of relativity states that the laws of physics are the same for all observers, regardless of their relative motion."""
        prompt = "human: Simply put, the theory of relativity states that?\n\nadept:"
        tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-chat", use_fast=False)
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch_device)
        model = PersimmonForCausalLM.from_pretrained(
            "adept/persimmon-8b-chat", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16
        )

        # greedy generation outputs
        generated_ids = model.generate(input_ids, max_new_tokens=64)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

        backend_empty_cache(torch_device)
        del model
        gc.collect()
