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from __future__ import annotations

import os
from datetime import datetime

from google.cloud.speech_v1 import RecognitionAudio, RecognitionConfig

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.operators.text_to_speech import CloudTextToSpeechSynthesizeOperator
from airflow.providers.google.cloud.operators.translate_speech import CloudTranslateSpeechOperator
from airflow.utils.trigger_rule import TriggerRule

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")

DAG_ID = "gcp_translate_speech"

BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"

# [START howto_operator_translate_speech_gcp_filename]
FILE_NAME = f"test-translate-speech-file-{DAG_ID}-{ENV_ID}"
# [END howto_operator_translate_speech_gcp_filename]

# [START howto_operator_text_to_speech_api_arguments]
INPUT = {"text": "Sample text for demo purposes"}
VOICE = {"language_code": "en-US", "ssml_gender": "FEMALE"}
AUDIO_CONFIG = {"audio_encoding": "LINEAR16"}
# [END howto_operator_text_to_speech_api_arguments]

# [START howto_operator_translate_speech_arguments]
CONFIG = RecognitionConfig({"encoding": "LINEAR16", "language_code": "en_US"})
AUDIO = RecognitionAudio({"uri": f"gs://{BUCKET_NAME}/{FILE_NAME}"})
TARGET_LANGUAGE = "pl"
FORMAT = "text"
MODEL = "base"
SOURCE_LANGUAGE = None
# [END howto_operator_translate_speech_arguments]


with DAG(
    DAG_ID,
    schedule="@once",  # Override to match your needs
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "translate-speech"],
) as dag:
    create_bucket = GCSCreateBucketOperator(task_id="create_bucket", bucket_name=BUCKET_NAME)

    text_to_speech_synthesize_task = CloudTextToSpeechSynthesizeOperator(
        project_id=PROJECT_ID,
        input_data=INPUT,
        voice=VOICE,
        audio_config=AUDIO_CONFIG,
        target_bucket_name=BUCKET_NAME,
        target_filename=FILE_NAME,
        task_id="text_to_speech_synthesize_task",
    )
    # [START howto_operator_translate_speech]
    translate_speech_task = CloudTranslateSpeechOperator(
        project_id=PROJECT_ID,
        audio=AUDIO,
        config=CONFIG,
        target_language=TARGET_LANGUAGE,
        format_=FORMAT,
        source_language=SOURCE_LANGUAGE,
        model=MODEL,
        task_id="translate_speech_task",
    )
    translate_speech_task2 = CloudTranslateSpeechOperator(
        audio=AUDIO,
        config=CONFIG,
        target_language=TARGET_LANGUAGE,
        format_=FORMAT,
        source_language=SOURCE_LANGUAGE,
        model=MODEL,
        task_id="translate_speech_task2",
    )
    # [END howto_operator_translate_speech]

    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE
    )

    (
        # TEST SETUP
        create_bucket
        # TEST BODY
        >> text_to_speech_synthesize_task
        >> translate_speech_task
        >> translate_speech_task2
        # TEST TEARDOWN
        >> delete_bucket
    )

    from tests_common.test_utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()


from tests_common.test_utils.system_tests import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)
