# Copyright 2024 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,
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"""Testing suite for the PyTorch OLMoE model."""

import unittest

from transformers import OlmoeConfig, is_torch_available
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers.models.gpt_neox.tokenization_gpt_neox import GPTNeoXTokenizer as GPTNeoXTokenizerFast
from transformers.testing_utils import (
    require_tokenizers,
    require_torch,
    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
from ...test_tensor_parallel_mixin import TensorParallelTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        OlmoeForCausalLM,
        OlmoeModel,
    )


class OlmoeModelTester:
    if is_torch_available():
        causal_lm_class = OlmoeForCausalLM

    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,
        hidden_act="silu",
        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,
        num_experts_per_tok=2,
        num_experts=8,
        norm_topk_prob=False,
        output_router_logits=False,
        router_aux_loss_coef=0.001,
        intermediate_size=16,
    ):
        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
        self.num_experts_per_tok = num_experts_per_tok
        self.num_experts = num_experts
        self.norm_topk_prob = norm_topk_prob
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef

    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_like(input_ids).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 OlmoeConfig(
            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,
            num_experts_per_tok=self.num_experts_per_tok,
            num_experts=self.num_experts,
            norm_topk_prob=self.norm_topk_prob,
            output_router_logits=self.output_router_logits,
            router_aux_loss_coef=self.router_aux_loss_coef,
        )

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = OlmoeModel(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 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 OlmoeModelTest(
    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, TensorParallelTesterMixin, unittest.TestCase
):
    all_model_classes = (OlmoeModel, OlmoeForCausalLM) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": OlmoeModel,
            "text-generation": OlmoeForCausalLM,
        }
        if is_torch_available()
        else {}
    )

    # Need to use `0.8` instead of `0.9` for `test_cpu_offload`
    # This is because we are hitting edge cases with the causal_mask buffer
    model_split_percents = [0.5, 0.7, 0.8]

    def setUp(self):
        self.model_tester = OlmoeModelTester(self)
        self.config_tester = ConfigTester(self, config_class=OlmoeConfig, hidden_size=32)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)


@require_torch
class OlmoeIntegrationTest(unittest.TestCase):
    @slow
    def test_model_7b_logits(self):
        input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]]
        model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
        out = model(torch.tensor(input_ids, device=model.device)).logits.float()
        # Expected mean on dim = -1
        EXPECTED_MEAN = torch.tensor([[-1.3814, -3.4450, -2.2990, -1.9542, -2.4387, -2.7941, -2.9312, -2.8309]])
        torch.testing.assert_close(out.mean(-1).cpu(), EXPECTED_MEAN, rtol=1e-2, atol=1e-2)
        # slicing logits[0, 0, 0:30]
        EXPECTED_SLICE = torch.tensor([-2.3874, -2.4076, -2.4995, 4.2278, 1.4004, -0.0252, 0.4189, -2.7560, 0.3531, 1.6678, -0.7941, -1.1818, -0.2920, 0.7131, -1.4173, 1.6723, 0.5406, 0.1345, -0.1800, 0.2304, 1.2791, 0.7489, 0.6341, -0.0151, -1.3693, -1.2532, -2.3921, 0.7376, 1.6876, 0.5483])  # fmt: skip
        torch.testing.assert_close(out[0, 0, :30].cpu(), EXPECTED_SLICE, rtol=1e-2, atol=1e-2)

    @slow
    def test_model_7b_greedy_generation(self):
        EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that \nthe speed of light is the same for all observers, no matter \nhow fast they are moving.  This is a very counter-intuitive \nconcept, and it took Einstein a long time to come up with \nthe theory.  The theory of relativity is based on two \npostulates"""
        prompt = "Simply put, the theory of relativity states that "
        tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
        model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924", device_map="auto")
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)

        # greedy generation outputs
        generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

    @require_tokenizers
    def test_fast_special_tokens(self):
        fast_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924")

        original_add_eos_token = fast_tokenizer.add_eos_token

        fast_tokenizer.add_eos_token = False
        fast = fast_tokenizer.encode("A sample test")
        self.assertEqual(fast, [34, 3410, 1071])

        fast_tokenizer.add_eos_token = True
        fast = fast_tokenizer.encode("A sample test")
        self.assertEqual(fast, [34, 3410, 1071, 50279])

        fast_tokenizer.add_eos_token = original_add_eos_token

    @require_tokenizers
    def test_simple_encode_decode(self):
        rust_tokenizer = GPTNeoXTokenizerFast.from_pretrained("allenai/OLMoE-1B-7B-0924")

        self.assertEqual(rust_tokenizer.encode("This is a test"), [1552, 310, 247, 1071])
        self.assertEqual(rust_tokenizer.decode([1552, 310, 247, 1071], skip_special_tokens=True), "This is a test")

        # bytefallback showcase
        self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [20025, 46549, 5225, 48561, 33656, 238, 12105])  # fmt: skip
        self.assertEqual(
            rust_tokenizer.decode([20025, 46549, 5225, 48561, 33656, 238, 12105], skip_special_tokens=True),
            "生活的真谛是",
        )

        # Inner spaces showcase
        self.assertEqual(rust_tokenizer.encode("Hi  Hello"), [12764, 50276, 12092])
        self.assertEqual(rust_tokenizer.decode([12764, 50276, 12092], skip_special_tokens=False), "Hi  Hello")

        self.assertEqual(rust_tokenizer.encode("Hi   Hello"), [12764, 50275, 12092])
        self.assertEqual(rust_tokenizer.decode([12764, 50275, 12092], skip_special_tokens=False), "Hi   Hello")

        self.assertEqual(rust_tokenizer.encode(""), [])

        self.assertEqual(rust_tokenizer.encode(" "), [209])

        self.assertEqual(rust_tokenizer.encode("  "), [50276])

        self.assertEqual(rust_tokenizer.encode(" Hello"), [24387])
