# 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,
# 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 Cohere2 model."""

import unittest

import pytest
from parameterized import parameterized
from pytest import mark

from transformers import AutoModelForCausalLM, AutoTokenizer, Cohere2Config, is_torch_available, pipeline
from transformers.generation.configuration_utils import GenerationConfig
from transformers.testing_utils import (
    Expectations,
    cleanup,
    is_flash_attn_2_available,
    is_kernels_available,
    is_torch_xpu_available,
    require_flash_attn,
    require_torch,
    require_torch_large_accelerator,
    slow,
    torch_device,
)

from ...models.cohere.test_modeling_cohere import CohereModelTester


if is_torch_available():
    import torch

    from transformers import (
        Cohere2ForCausalLM,
        Cohere2Model,
    )

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


class Cohere2ModelTester(CohereModelTester):
    config_class = Cohere2Config
    if is_torch_available():
        model_class = Cohere2Model
        for_causal_lm_class = Cohere2ForCausalLM


@require_torch
class Cohere2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (Cohere2Model, Cohere2ForCausalLM) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Cohere2Model,
            "text-generation": Cohere2ForCausalLM,
        }
        if is_torch_available()
        else {}
    )
    _is_stateful = True

    # 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 = Cohere2ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Cohere2Config, 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)


@slow
@require_torch_large_accelerator
class Cohere2IntegrationTest(unittest.TestCase):
    input_text = ["Hello I am doing", "Hi today"]

    def tearDown(self):
        cleanup(torch_device, gc_collect=True)

    def test_model_bf16(self):
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_TEXTS = [
            "<BOS_TOKEN>Hello I am doing a project for a school assignment and I need to create a website for a fictional company. I have",
            "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n",
        ]

        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, attn_implementation="eager").to(
            torch_device
        )

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_fp16(self):
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        # fmt: off
        EXPECTED_TEXTS = Expectations(
            {
                ("xpu", 3): ["<BOS_TOKEN>Hello I am doing a project for my school and I need to create a website for a fictional company. I have the", "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n"],
                (None, None): ["<BOS_TOKEN>Hello I am doing a project for a school assignment and I need to create a website for a fictional company. I have", "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n"],
                ("cuda", 8): ['<BOS_TOKEN>Hello I am doing a project for my school and I need to create a website for a fictional company. I have the', "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n"],
            }
        )
        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
        # fmt: on

        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16, attn_implementation="eager").to(
            torch_device
        )

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXT)

    def test_model_pipeline_bf16(self):
        # See https://github.com/huggingface/transformers/pull/31747 -- pipeline was broken for Cohere2 before this PR
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        # EXPECTED_TEXTS should match the same non-pipeline test, minus the special tokens
        EXPECTED_TEXTS = [
            "Hello I am doing a project for a school assignment and I need to create a website for a fictional company. I have",
            "Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n",
        ]

        model = AutoModelForCausalLM.from_pretrained(
            model_id, dtype=torch.bfloat16, attn_implementation="flex_attention"
        ).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

        output = pipe(self.input_text, max_new_tokens=20, do_sample=False, padding=True)

        self.assertEqual(output[0][0]["generated_text"], EXPECTED_TEXTS[0])
        self.assertEqual(output[1][0]["generated_text"], EXPECTED_TEXTS[1])

    @require_flash_attn
    @mark.flash_attn_test
    def test_model_flash_attn(self):
        # See https://github.com/huggingface/transformers/issues/31953 --- flash attn was generating garbage for Gemma2, especially in long context
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_TEXTS = [
            '<BOS_TOKEN>Hello I am doing a project for my school and I need to create a website for a fictional company. I have the logo and the name of the company. I need a website that is simple and easy to navigate. I need a home page, about us, services, contact us, and a gallery. I need the website to be responsive and I need it to be able to be hosted on a server. I need the website to be done in a week. I need the website to be done in HTML,',
            "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n\nThis recipe is very simple and easy to make.\n\nYou will need:\n\n* 2 cups of flour\n* 1 cup of sugar\n* 1/2 cup of cocoa powder\n* 1 teaspoon of baking powder\n* 1 teaspoon of baking soda\n* 1/2 teaspoon of salt\n* 2 eggs\n* 1 cup of milk\n",
        ]  # fmt: skip

        model = AutoModelForCausalLM.from_pretrained(
            model_id, attn_implementation="flash_attention_2", dtype="float16"
        ).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    @pytest.mark.torch_export_test
    def test_export_static_cache(self):
        from transformers.integrations.executorch import (
            TorchExportableModuleWithStaticCache,
            convert_and_export_with_cache,
        )

        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        # fmt: off
        EXPECTED_TEXT_COMPLETIONS = Expectations(
            {
                ("xpu", 3): ["Hello I am doing a project for a friend and I am stuck on a few things. I have a 2004 Ford F-"],
                (None, None): ["Hello I am doing a project on the effects of social media on mental health. I have a few questions. 1. What is the relationship"],
                ("cuda", 8): ['Hello I am doing a project for a friend and I am stuck on a few things. I have a 2004 Ford F-'],
            }
        )
        EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation()
        # fmt: on

        tokenizer = AutoTokenizer.from_pretrained(model_id, pad_token="<PAD>", padding_side="right")
        # Load model
        device = "cpu"  # TODO (joao / export experts): should be on `torch_device`, but causes GPU OOM
        dtype = torch.bfloat16
        cache_implementation = "static"
        attn_implementation = "sdpa"
        batch_size = 1
        model = AutoModelForCausalLM.from_pretrained(
            "CohereForAI/c4ai-command-r7b-12-2024",
            device_map=device,
            dtype=dtype,
            attn_implementation=attn_implementation,
            generation_config=GenerationConfig(
                use_cache=True,
                cache_implementation=cache_implementation,
                max_length=30,
                cache_config={
                    "batch_size": batch_size,
                    "max_cache_len": 30,
                },
            ),
        )

        prompts = ["Hello I am doing"]
        prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
        prompt_token_ids = prompt_tokens["input_ids"]
        max_new_tokens = 30 - prompt_token_ids.shape[-1]

        # Static Cache + export
        exported_program = convert_and_export_with_cache(model)
        ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
            exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
        )
        ep_generated_text = tokenizer.batch_decode(ep_generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, ep_generated_text)

    @parameterized.expand([("flash_attention_2",), ("sdpa",), ("flex_attention",), ("eager",)])
    def test_generation_beyond_sliding_window(self, attn_implementation: str):
        """Test that we can correctly generate beyond the sliding window. This is non trivial as
        we need to correctly slice the attention mask in all cases (because we use a hybrid cache).
        Outputs for every attention functions should be coherent and identical.
        """
        # Impossible to test it with this model (even with < 100 tokens), probably due to the compilation of a large model.
        if attn_implementation == "flex_attention":
            self.skipTest(
                reason="`flex_attention` gives `torch._inductor.exc.InductorError: RuntimeError: No valid triton configs. OutOfMemoryError: out of resource: triton_tem_fused_0 Required: 147456 Hardware limit:101376 Reducing block sizes or `num_stages` may help.`"
            )

        if (
            attn_implementation == "flash_attention_2"
            and not is_flash_attn_2_available()
            and not (is_torch_xpu_available() and is_kernels_available())
        ):
            self.skipTest("FlashAttention2 is required for this test.")

        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_COMPLETIONS = [
            " the mountains, the lakes, the rivers, the forests, the trees, the birds, the animals",
            ", green, yellow, orange, purple, pink, brown, black, white, grey, silver",
        ]

        input_text = [
            "This is a nice place. " * 200 + "I really enjoy the scenery,",  # This is larger than 1024 tokens
            "A list of colors: red, blue",  # This will almost all be padding tokens
        ]
        tokenizer = AutoTokenizer.from_pretrained(model_id, padding="left")
        inputs = tokenizer(input_text, padding=True, return_tensors="pt").to(torch_device)

        # We use `sliding_window=1024` instead of the origin value `4096` in the config to avoid GPU OOM
        model = AutoModelForCausalLM.from_pretrained(
            model_id, attn_implementation=attn_implementation, dtype=torch.float16, sliding_window=1024
        ).to(torch_device)

        # Make sure prefill is larger than sliding window
        input_size = inputs.input_ids.shape[-1]
        self.assertTrue(input_size > model.config.sliding_window)

        out = model.generate(**inputs, max_new_tokens=20)[:, input_size:]
        output_text = tokenizer.batch_decode(out)

        self.assertEqual(output_text, EXPECTED_COMPLETIONS)