# 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 ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union

import requests
from slack_sdk import WebClient


client = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])

NON_MODEL_TEST_MODULES = [
    "benchmark",
    "deepspeed",
    "extended",
    "fixtures",
    "generation",
    "onnx",
    "optimization",
    "pipelines",
    "sagemaker",
    "trainer",
    "utils",
]


def handle_test_results(test_results):
    expressions = test_results.split(" ")

    failed = 0
    success = 0

    # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
    # When it is too long, those signs are not present.
    time_spent = expressions[-2] if "=" in expressions[-1] else expressions[-1]

    for i, expression in enumerate(expressions):
        if "failed" in expression:
            failed += int(expressions[i - 1])
        if "passed" in expression:
            success += int(expressions[i - 1])

    return failed, success, time_spent


def handle_stacktraces(test_results):
    # These files should follow the following architecture:
    # === FAILURES ===
    # <path>:<line>: Error ...
    # <path>:<line>: Error ...
    # <empty line>

    total_stacktraces = test_results.split("\n")[1:-1]
    stacktraces = []
    for stacktrace in total_stacktraces:
        try:
            line = stacktrace[: stacktrace.index(" ")].split(":")[-2]
            error_message = stacktrace[stacktrace.index(" ") :]

            stacktraces.append(f"(line {line}) {error_message}")
        except Exception:
            stacktraces.append("Cannot retrieve error message.")

    return stacktraces


def dicts_to_sum(objects: Union[Dict[str, Dict], List[dict]]):
    if isinstance(objects, dict):
        lists = objects.values()
    else:
        lists = objects

    # Convert each dictionary to counter
    counters = map(collections.Counter, lists)
    # Sum all the counters
    return functools.reduce(operator.add, counters)


class Message:
    def __init__(self, title: str, model_results: Dict, additional_results: Dict):
        self.title = title

        # Failures and success of the modeling tests
        self.n_model_success = sum(r["success"] for r in model_results.values())
        self.n_model_single_gpu_failures = sum(dicts_to_sum(r["failed"])["single"] for r in model_results.values())
        self.n_model_multi_gpu_failures = sum(dicts_to_sum(r["failed"])["multi"] for r in model_results.values())

        # Some suites do not have a distinction between single and multi GPU.
        self.n_model_unknown_failures = sum(dicts_to_sum(r["failed"])["unclassified"] for r in model_results.values())
        self.n_model_failures = (
            self.n_model_single_gpu_failures + self.n_model_multi_gpu_failures + self.n_model_unknown_failures
        )

        # Failures and success of the additional tests
        self.n_additional_success = sum(r["success"] for r in additional_results.values())

        all_additional_failures = dicts_to_sum([r["failed"] for r in additional_results.values()])
        self.n_additional_single_gpu_failures = all_additional_failures["single"]
        self.n_additional_multi_gpu_failures = all_additional_failures["multi"]
        self.n_additional_unknown_gpu_failures = all_additional_failures["unclassified"]
        self.n_additional_failures = (
            self.n_additional_single_gpu_failures
            + self.n_additional_multi_gpu_failures
            + self.n_additional_unknown_gpu_failures
        )

        # Results
        self.n_failures = self.n_model_failures + self.n_additional_failures
        self.n_success = self.n_model_success + self.n_additional_success
        self.n_tests = self.n_failures + self.n_success

        self.model_results = model_results
        self.additional_results = additional_results

        self.thread_ts = None

    @property
    def time(self) -> str:
        all_results = [*self.model_results.values(), *self.additional_results.values()]
        time_spent = [r["time_spent"].split(", ")[0] for r in all_results if len(r["time_spent"])]
        total_secs = 0

        for time in time_spent:
            time_parts = time.split(":")

            # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
            if len(time_parts) == 1:
                time_parts = [0, 0, time_parts[0]]

            hours, minutes, seconds = int(time_parts[0]), int(time_parts[1]), float(time_parts[2])
            total_secs += hours * 3600 + minutes * 60 + seconds

        hours, minutes, seconds = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
        return f"{int(hours)}h{int(minutes)}m{int(seconds)}s"

    @property
    def header(self) -> Dict:
        return {"type": "header", "text": {"type": "plain_text", "text": self.title}}

    @property
    def no_failures(self) -> Dict:
        return {
            "type": "section",
            "text": {
                "type": "plain_text",
                "text": f"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.",
                "emoji": True,
            },
            "accessory": {
                "type": "button",
                "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
            },
        }

    @property
    def failures(self) -> Dict:
        return {
            "type": "section",
            "text": {
                "type": "plain_text",
                "text": f"There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in {self.time}.",
                "emoji": True,
            },
            "accessory": {
                "type": "button",
                "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
            },
        }

    @staticmethod
    def get_device_report(report, rjust=6):
        if "single" in report and "multi" in report:
            return f"{str(report['single']).rjust(rjust)} | {str(report['multi']).rjust(rjust)} | "
        elif "single" in report:
            return f"{str(report['single']).rjust(rjust)} | {'0'.rjust(rjust)} | "
        elif "multi" in report:
            return f"{'0'.rjust(rjust)} | {str(report['multi']).rjust(rjust)} | "

    @property
    def category_failures(self) -> Dict:
        model_failures = [v["failed"] for v in self.model_results.values()]

        category_failures = {}

        for model_failure in model_failures:
            for key, value in model_failure.items():
                if key not in category_failures:
                    category_failures[key] = dict(value)
                else:
                    category_failures[key]["unclassified"] += value["unclassified"]
                    category_failures[key]["single"] += value["single"]
                    category_failures[key]["multi"] += value["multi"]

        individual_reports = []
        for key, value in category_failures.items():
            device_report = self.get_device_report(value)

            if sum(value.values()):
                if device_report:
                    individual_reports.append(f"{device_report}{key}")
                else:
                    individual_reports.append(key)

        header = "Single |  Multi | Category\n"
        category_failures_report = header + "\n".join(sorted(individual_reports))

        return {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": f"The following modeling categories had failures:\n\n```\n{category_failures_report}\n```",
            },
        }

    @property
    def model_failures(self) -> Dict:
        # Obtain per-model failures
        def per_model_sum(model_category_dict):
            return dicts_to_sum(model_category_dict["failed"].values())

        failures = {}
        non_model_failures = {
            k: per_model_sum(v) for k, v in self.model_results.items() if sum(per_model_sum(v).values())
        }

        for k, v in self.model_results.items():
            if k in NON_MODEL_TEST_MODULES:
                pass

            if sum(per_model_sum(v).values()):
                dict_failed = dict(v["failed"])
                pytorch_specific_failures = dict_failed.pop("PyTorch")
                tensorflow_specific_failures = dict_failed.pop("TensorFlow")
                other_failures = dicts_to_sum(dict_failed.values())

                failures[k] = {
                    "PyTorch": pytorch_specific_failures,
                    "TensorFlow": tensorflow_specific_failures,
                    "other": other_failures,
                }

        model_reports = []
        other_module_reports = []

        for key, value in non_model_failures.items():
            if key in NON_MODEL_TEST_MODULES:
                device_report = self.get_device_report(value)

                if sum(value.values()):
                    if device_report:
                        report = f"{device_report}{key}"
                    else:
                        report = key

                    other_module_reports.append(report)

        for key, value in failures.items():
            device_report_values = [
                value["PyTorch"]["single"],
                value["PyTorch"]["multi"],
                value["TensorFlow"]["single"],
                value["TensorFlow"]["multi"],
                sum(value["other"].values()),
            ]

            if sum(device_report_values):
                device_report = " | ".join([str(x).rjust(9) for x in device_report_values]) + " | "
                report = f"{device_report}{key}"

                model_reports.append(report)

        model_header = "Single PT |  Multi PT | Single TF |  Multi TF |     Other | Category\n"
        sorted_model_reports = sorted(model_reports, key=lambda s: s.split("] ")[-1])
        model_failures_report = model_header + "\n".join(sorted_model_reports)

        module_header = "Single |  Multi | Category\n"
        sorted_module_reports = sorted(other_module_reports, key=lambda s: s.split("] ")[-1])
        module_failures_report = module_header + "\n".join(sorted_module_reports)

        report = ""

        if len(model_reports):
            report += f"These following model modules had failures:\n```\n{model_failures_report}\n```\n\n"

        if len(other_module_reports):
            report += f"The following non-model modules had failures:\n```\n{module_failures_report}\n```\n\n"

        return {"type": "section", "text": {"type": "mrkdwn", "text": report}}

    @property
    def additional_failures(self) -> Dict:
        failures = {k: v["failed"] for k, v in self.additional_results.items()}
        errors = {k: v["error"] for k, v in self.additional_results.items()}

        individual_reports = []
        for key, value in failures.items():
            device_report = self.get_device_report(value)

            if sum(value.values()) or errors[key]:
                report = f"{key}"
                if errors[key]:
                    report = f"[Errored out] {report}"
                if device_report:
                    report = f"{device_report}{report}"

                individual_reports.append(report)

        header = "Single |  Multi | Category\n"
        failures_report = header + "\n".join(sorted(individual_reports))

        return {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": f"The following non-modeling tests had failures:\n```\n{failures_report}\n```",
            },
        }

    @property
    def payload(self) -> str:
        blocks = [self.header]

        if self.n_model_failures > 0 or self.n_additional_failures > 0:
            blocks.append(self.failures)

        if self.n_model_failures > 0:
            blocks.extend([self.category_failures, self.model_failures])

        if self.n_additional_failures > 0:
            blocks.append(self.additional_failures)

        if self.n_model_failures == 0 and self.n_additional_failures == 0:
            blocks.append(self.no_failures)

        return json.dumps(blocks)

    @staticmethod
    def error_out():
        payload = [
            {
                "type": "section",
                "text": {
                    "type": "plain_text",
                    "text": "There was an issue running the tests.",
                },
                "accessory": {
                    "type": "button",
                    "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                    "url": f"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
                },
            }
        ]

        print("Sending the following payload")
        print(json.dumps({"blocks": json.loads(payload)}))

        client.chat_postMessage(
            channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"],
            text="There was an issue running the tests.",
            blocks=payload,
        )

    def post(self):
        print("Sending the following payload")
        print(json.dumps({"blocks": json.loads(self.payload)}))

        text = f"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else "All tests passed."

        self.thread_ts = client.chat_postMessage(
            channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"],
            blocks=self.payload,
            text=text,
        )

    def get_reply_blocks(self, job_name, job_result, failures, device, text):
        if len(failures) > 2500:
            failures = "\n".join(failures.split("\n")[:20]) + "\n\n[Truncated]"

        title = job_name
        if device is not None:
            title += f" ({device}-gpu)"

        content = {"type": "section", "text": {"type": "mrkdwn", "text": text}}

        if job_result["job_link"] is not None:
            content["accessory"] = {
                "type": "button",
                "text": {"type": "plain_text", "text": "GitHub Action job", "emoji": True},
                "url": job_result["job_link"],
            }

        return [
            {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
            content,
            {"type": "section", "text": {"type": "mrkdwn", "text": failures}},
        ]

    def post_reply(self):
        if self.thread_ts is None:
            raise ValueError("Can only post reply if a post has been made.")

        sorted_dict = sorted(self.model_results.items(), key=lambda t: t[0])
        for job, job_result in sorted_dict:
            if len(job_result["failures"]):
                for device, failures in job_result["failures"].items():
                    text = "\n".join(
                        sorted([f"*{k}*: {v[device]}" for k, v in job_result["failed"].items() if v[device]])
                    )

                    blocks = self.get_reply_blocks(job, job_result, failures, device, text=text)

                    print("Sending the following reply")
                    print(json.dumps({"blocks": blocks}))

                    client.chat_postMessage(
                        channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"],
                        text=f"Results for {job}",
                        blocks=blocks,
                        thread_ts=self.thread_ts["ts"],
                    )

                    time.sleep(1)

        for job, job_result in self.additional_results.items():
            if len(job_result["failures"]):
                for device, failures in job_result["failures"].items():
                    blocks = self.get_reply_blocks(
                        job,
                        job_result,
                        failures,
                        device,
                        text=f"Number of failures: {sum(job_result['failed'].values())}",
                    )

                    print("Sending the following reply")
                    print(json.dumps({"blocks": blocks}))

                    client.chat_postMessage(
                        channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"],
                        text=f"Results for {job}",
                        blocks=blocks,
                        thread_ts=self.thread_ts["ts"],
                    )

                    time.sleep(1)


def get_job_links():
    run_id = os.environ["GITHUB_RUN_ID"]
    url = f"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
    result = requests.get(url).json()
    jobs = {}

    try:
        jobs.update({job["name"]: job["html_url"] for job in result["jobs"]})
        pages_to_iterate_over = math.ceil((result["total_count"] - 100) / 100)

        for i in range(pages_to_iterate_over):
            result = requests.get(url + f"&page={i + 2}").json()
            jobs.update({job["name"]: job["html_url"] for job in result["jobs"]})

        return jobs
    except Exception as e:
        print("Unknown error, could not fetch links.", e)

    return {}


def retrieve_artifact(name: str, gpu: Optional[str]):
    if gpu not in [None, "single", "multi"]:
        raise ValueError(f"Invalid GPU for artifact. Passed GPU: `{gpu}`.")

    if gpu is not None:
        name = f"{gpu}-gpu-docker_{name}"

    _artifact = {}

    if os.path.exists(name):
        files = os.listdir(name)
        for file in files:
            try:
                with open(os.path.join(name, file)) as f:
                    _artifact[file.split(".")[0]] = f.read()
            except UnicodeDecodeError as e:
                raise ValueError(f"Could not open {os.path.join(name, file)}.") from e

    return _artifact


def retrieve_available_artifacts():
    class Artifact:
        def __init__(self, name: str, single_gpu: bool = False, multi_gpu: bool = False):
            self.name = name
            self.single_gpu = single_gpu
            self.multi_gpu = multi_gpu
            self.paths = []

        def __str__(self):
            return self.name

        def add_path(self, path: str, gpu: str = None):
            self.paths.append({"name": self.name, "path": path, "gpu": gpu})

    _available_artifacts: Dict[str, Artifact] = {}

    directories = filter(os.path.isdir, os.listdir())
    for directory in directories:
        if directory.startswith("single-gpu-docker"):
            artifact_name = directory[len("single-gpu-docker") + 1 :]

            if artifact_name in _available_artifacts:
                _available_artifacts[artifact_name].single_gpu = True
            else:
                _available_artifacts[artifact_name] = Artifact(artifact_name, single_gpu=True)

            _available_artifacts[artifact_name].add_path(directory, gpu="single")

        elif directory.startswith("multi-gpu-docker"):
            artifact_name = directory[len("multi-gpu-docker") + 1 :]

            if artifact_name in _available_artifacts:
                _available_artifacts[artifact_name].multi_gpu = True
            else:
                _available_artifacts[artifact_name] = Artifact(artifact_name, multi_gpu=True)

            _available_artifacts[artifact_name].add_path(directory, gpu="multi")
        else:
            artifact_name = directory
            if artifact_name not in _available_artifacts:
                _available_artifacts[artifact_name] = Artifact(artifact_name)

            _available_artifacts[artifact_name].add_path(directory)

    return _available_artifacts


if __name__ == "__main__":
    arguments = sys.argv[1:][0]
    try:
        models = ast.literal_eval(arguments)
    except SyntaxError:
        Message.error_out()
        raise ValueError("Errored out.")

    github_actions_job_links = get_job_links()
    available_artifacts = retrieve_available_artifacts()

    modeling_categories = [
        "PyTorch",
        "TensorFlow",
        "Flax",
        "Tokenizers",
        "Pipelines",
        "Trainer",
        "ONNX",
        "Auto",
        "Unclassified",
    ]

    # This dict will contain all the information relative to each model:
    # - Failures: the total, as well as the number of failures per-category defined above
    # - Success: total
    # - Time spent: as a comma-separated list of elapsed time
    # - Failures: as a line-break separated list of errors
    model_results = {
        model: {
            "failed": {m: {"unclassified": 0, "single": 0, "multi": 0} for m in modeling_categories},
            "success": 0,
            "time_spent": "",
            "failures": {},
        }
        for model in models
        if f"run_all_tests_gpu_{model}_test_reports" in available_artifacts
    }

    unclassified_model_failures = []

    for model in model_results.keys():
        for artifact_path in available_artifacts[f"run_all_tests_gpu_{model}_test_reports"].paths:
            artifact = retrieve_artifact(artifact_path["name"], artifact_path["gpu"])
            if "stats" in artifact:
                # Link to the GitHub Action job
                model_results[model]["job_link"] = github_actions_job_links.get(
                    f"Model tests ({model}, {artifact_path['gpu']}-gpu-docker)"
                )

                failed, success, time_spent = handle_test_results(artifact["stats"])
                model_results[model]["success"] += success
                model_results[model]["time_spent"] += time_spent[1:-1] + ", "

                stacktraces = handle_stacktraces(artifact["failures_line"])

                for line in artifact["summary_short"].split("\n"):
                    if re.search("FAILED", line):

                        line = line.replace("FAILED ", "")
                        line = line.split()[0].replace("\n", "")

                        if artifact_path["gpu"] not in model_results[model]["failures"]:
                            model_results[model]["failures"][artifact_path["gpu"]] = ""

                        model_results[model]["failures"][
                            artifact_path["gpu"]
                        ] += f"*{line}*\n_{stacktraces.pop(0)}_\n\n"

                        if re.search("_tf_", line):
                            model_results[model]["failed"]["TensorFlow"][artifact_path["gpu"]] += 1

                        elif re.search("_flax_", line):
                            model_results[model]["failed"]["Flax"][artifact_path["gpu"]] += 1

                        elif re.search("test_modeling", line):
                            model_results[model]["failed"]["PyTorch"][artifact_path["gpu"]] += 1

                        elif re.search("test_tokenization", line):
                            model_results[model]["failed"]["Tokenizers"][artifact_path["gpu"]] += 1

                        elif re.search("test_pipelines", line):
                            model_results[model]["failed"]["Pipelines"][artifact_path["gpu"]] += 1

                        elif re.search("test_trainer", line):
                            model_results[model]["failed"]["Trainer"][artifact_path["gpu"]] += 1

                        elif re.search("onnx", line):
                            model_results[model]["failed"]["ONNX"][artifact_path["gpu"]] += 1

                        elif re.search("auto", line):
                            model_results[model]["failed"]["Auto"][artifact_path["gpu"]] += 1

                        else:
                            model_results[model]["failed"]["Unclassified"][artifact_path["gpu"]] += 1
                            unclassified_model_failures.append(line)

    # Additional runs
    additional_files = {
        "Examples directory": "run_examples_gpu",
        "PyTorch pipelines": "run_tests_torch_pipeline_gpu",
        "TensorFlow pipelines": "run_tests_tf_pipeline_gpu",
        "Torch CUDA extension tests": "run_tests_torch_cuda_extensions_gpu_test_reports",
    }

    additional_results = {
        key: {
            "failed": {"unclassified": 0, "single": 0, "multi": 0},
            "success": 0,
            "time_spent": "",
            "error": False,
            "failures": {},
            "job_link": github_actions_job_links.get(key),
        }
        for key in additional_files.keys()
    }

    for key in additional_results.keys():

        # If a whole suite of test fails, the artifact isn't available.
        if additional_files[key] not in available_artifacts:
            additional_results[key]["error"] = True
            continue

        for artifact_path in available_artifacts[additional_files[key]].paths:
            if artifact_path["gpu"] is not None:
                additional_results[key]["job_link"] = github_actions_job_links.get(
                    f"{key} ({artifact_path['gpu']}-gpu-docker)"
                )
            artifact = retrieve_artifact(artifact_path["name"], artifact_path["gpu"])
            stacktraces = handle_stacktraces(artifact["failures_line"])

            failed, success, time_spent = handle_test_results(artifact["stats"])
            additional_results[key]["failed"][artifact_path["gpu"] or "unclassified"] += failed
            additional_results[key]["success"] += success
            additional_results[key]["time_spent"] += time_spent[1:-1] + ", "

            if len(artifact["errors"]):
                additional_results[key]["error"] = True

            if failed:
                for line in artifact["summary_short"].split("\n"):
                    if re.search("FAILED", line):
                        line = line.replace("FAILED ", "")
                        line = line.split()[0].replace("\n", "")

                        if artifact_path["gpu"] not in additional_results[key]["failures"]:
                            additional_results[key]["failures"][artifact_path["gpu"]] = ""

                        additional_results[key]["failures"][
                            artifact_path["gpu"]
                        ] += f"*{line}*\n_{stacktraces.pop(0)}_\n\n"

    message = Message("🤗 Results of the scheduled tests.", model_results, additional_results)

    message.post()
    message.post_reply()