

# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowercase_			=  {
    "Acehnese Arabic": "ace_Arab",
    "Acehnese Latin": "ace_Latn",
    "Mesopotamian Arabic": "acm_Arab",
    "Ta'izzi-Adeni Arabic": "acq_Arab",
    "Tunisian Arabic": "aeb_Arab",
    "Afrikaans": "afr_Latn",
    "South Levantine Arabic": "ajp_Arab",
    "Akan": "aka_Latn",
    "Amharic": "amh_Ethi",
    "North Levantine Arabic": "apc_Arab",
    "Modern Standard Arabic": "arb_Arab",
    "Modern Standard Arabic Romanized": "arb_Latn",
    "Najdi Arabic": "ars_Arab",
    "Moroccan Arabic": "ary_Arab",
    "Egyptian Arabic": "arz_Arab",
    "Assamese": "asm_Beng",
    "Asturian": "ast_Latn",
    "Awadhi": "awa_Deva",
    "Central Aymara": "ayr_Latn",
    "South Azerbaijani": "azb_Arab",
    "North Azerbaijani": "azj_Latn",
    "Bashkir": "bak_Cyrl",
    "Bambara": "bam_Latn",
    "Balinese": "ban_Latn",
    "Belarusian": "bel_Cyrl",
    "Bemba": "bem_Latn",
    "Bengali": "ben_Beng",
    "Bhojpuri": "bho_Deva",
    "Banjar Arabic": "bjn_Arab",
    "Banjar Latin": "bjn_Latn",
    "Standard Tibetan": "bod_Tibt",
    "Bosnian": "bos_Latn",
    "Buginese": "bug_Latn",
    "Bulgarian": "bul_Cyrl",
    "Catalan": "cat_Latn",
    "Cebuano": "ceb_Latn",
    "Czech": "ces_Latn",
    "Chokwe": "cjk_Latn",
    "Central Kurdish": "ckb_Arab",
    "Crimean Tatar": "crh_Latn",
    "Welsh": "cym_Latn",
    "Danish": "dan_Latn",
    "German": "deu_Latn",
    "Southwestern Dinka": "dik_Latn",
    "Dyula": "dyu_Latn",
    "Dzongkha": "dzo_Tibt",
    "Greek": "ell_Grek",
    "English": "eng_Latn",
    "Esperanto": "epo_Latn",
    "Estonian": "est_Latn",
    "Basque": "eus_Latn",
    "Ewe": "ewe_Latn",
    "Faroese": "fao_Latn",
    "Fijian": "fij_Latn",
    "Finnish": "fin_Latn",
    "Fon": "fon_Latn",
    "French": "fra_Latn",
    "Friulian": "fur_Latn",
    "Nigerian Fulfulde": "fuv_Latn",
    "Scottish Gaelic": "gla_Latn",
    "Irish": "gle_Latn",
    "Galician": "glg_Latn",
    "Guarani": "grn_Latn",
    "Gujarati": "guj_Gujr",
    "Haitian Creole": "hat_Latn",
    "Hausa": "hau_Latn",
    "Hebrew": "heb_Hebr",
    "Hindi": "hin_Deva",
    "Chhattisgarhi": "hne_Deva",
    "Croatian": "hrv_Latn",
    "Hungarian": "hun_Latn",
    "Armenian": "hye_Armn",
    "Igbo": "ibo_Latn",
    "Ilocano": "ilo_Latn",
    "Indonesian": "ind_Latn",
    "Icelandic": "isl_Latn",
    "Italian": "ita_Latn",
    "Javanese": "jav_Latn",
    "Japanese": "jpn_Jpan",
    "Kabyle": "kab_Latn",
    "Jingpho": "kac_Latn",
    "Kamba": "kam_Latn",
    "Kannada": "kan_Knda",
    "Kashmiri Arabic": "kas_Arab",
    "Kashmiri Devanagari": "kas_Deva",
    "Georgian": "kat_Geor",
    "Central Kanuri Arabic": "knc_Arab",
    "Central Kanuri Latin": "knc_Latn",
    "Kazakh": "kaz_Cyrl",
    "Kabiyè": "kbp_Latn",
    "Kabuverdianu": "kea_Latn",
    "Khmer": "khm_Khmr",
    "Kikuyu": "kik_Latn",
    "Kinyarwanda": "kin_Latn",
    "Kyrgyz": "kir_Cyrl",
    "Kimbundu": "kmb_Latn",
    "Northern Kurdish": "kmr_Latn",
    "Kikongo": "kon_Latn",
    "Korean": "kor_Hang",
    "Lao": "lao_Laoo",
    "Ligurian": "lij_Latn",
    "Limburgish": "lim_Latn",
    "Lingala": "lin_Latn",
    "Lithuanian": "lit_Latn",
    "Lombard": "lmo_Latn",
    "Latgalian": "ltg_Latn",
    "Luxembourgish": "ltz_Latn",
    "Luba-Kasai": "lua_Latn",
    "Ganda": "lug_Latn",
    "Luo": "luo_Latn",
    "Mizo": "lus_Latn",
    "Standard Latvian": "lvs_Latn",
    "Magahi": "mag_Deva",
    "Maithili": "mai_Deva",
    "Malayalam": "mal_Mlym",
    "Marathi": "mar_Deva",
    "Minangkabau Arabic ": "min_Arab",
    "Minangkabau Latin": "min_Latn",
    "Macedonian": "mkd_Cyrl",
    "Plateau Malagasy": "plt_Latn",
    "Maltese": "mlt_Latn",
    "Meitei Bengali": "mni_Beng",
    "Halh Mongolian": "khk_Cyrl",
    "Mossi": "mos_Latn",
    "Maori": "mri_Latn",
    "Burmese": "mya_Mymr",
    "Dutch": "nld_Latn",
    "Norwegian Nynorsk": "nno_Latn",
    "Norwegian Bokmål": "nob_Latn",
    "Nepali": "npi_Deva",
    "Northern Sotho": "nso_Latn",
    "Nuer": "nus_Latn",
    "Nyanja": "nya_Latn",
    "Occitan": "oci_Latn",
    "West Central Oromo": "gaz_Latn",
    "Odia": "ory_Orya",
    "Pangasinan": "pag_Latn",
    "Eastern Panjabi": "pan_Guru",
    "Papiamento": "pap_Latn",
    "Western Persian": "pes_Arab",
    "Polish": "pol_Latn",
    "Portuguese": "por_Latn",
    "Dari": "prs_Arab",
    "Southern Pashto": "pbt_Arab",
    "Ayacucho Quechua": "quy_Latn",
    "Romanian": "ron_Latn",
    "Rundi": "run_Latn",
    "Russian": "rus_Cyrl",
    "Sango": "sag_Latn",
    "Sanskrit": "san_Deva",
    "Santali": "sat_Olck",
    "Sicilian": "scn_Latn",
    "Shan": "shn_Mymr",
    "Sinhala": "sin_Sinh",
    "Slovak": "slk_Latn",
    "Slovenian": "slv_Latn",
    "Samoan": "smo_Latn",
    "Shona": "sna_Latn",
    "Sindhi": "snd_Arab",
    "Somali": "som_Latn",
    "Southern Sotho": "sot_Latn",
    "Spanish": "spa_Latn",
    "Tosk Albanian": "als_Latn",
    "Sardinian": "srd_Latn",
    "Serbian": "srp_Cyrl",
    "Swati": "ssw_Latn",
    "Sundanese": "sun_Latn",
    "Swedish": "swe_Latn",
    "Swahili": "swh_Latn",
    "Silesian": "szl_Latn",
    "Tamil": "tam_Taml",
    "Tatar": "tat_Cyrl",
    "Telugu": "tel_Telu",
    "Tajik": "tgk_Cyrl",
    "Tagalog": "tgl_Latn",
    "Thai": "tha_Thai",
    "Tigrinya": "tir_Ethi",
    "Tamasheq Latin": "taq_Latn",
    "Tamasheq Tifinagh": "taq_Tfng",
    "Tok Pisin": "tpi_Latn",
    "Tswana": "tsn_Latn",
    "Tsonga": "tso_Latn",
    "Turkmen": "tuk_Latn",
    "Tumbuka": "tum_Latn",
    "Turkish": "tur_Latn",
    "Twi": "twi_Latn",
    "Central Atlas Tamazight": "tzm_Tfng",
    "Uyghur": "uig_Arab",
    "Ukrainian": "ukr_Cyrl",
    "Umbundu": "umb_Latn",
    "Urdu": "urd_Arab",
    "Northern Uzbek": "uzn_Latn",
    "Venetian": "vec_Latn",
    "Vietnamese": "vie_Latn",
    "Waray": "war_Latn",
    "Wolof": "wol_Latn",
    "Xhosa": "xho_Latn",
    "Eastern Yiddish": "ydd_Hebr",
    "Yoruba": "yor_Latn",
    "Yue Chinese": "yue_Hant",
    "Chinese Simplified": "zho_Hans",
    "Chinese Traditional": "zho_Hant",
    "Standard Malay": "zsm_Latn",
    "Zulu": "zul_Latn",
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'facebook/nllb-200-distilled-600M'
    lowerCamelCase		     =							(
        'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
        'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
        'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
        'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
    )
    lowerCamelCase		     =							'translator'
    lowerCamelCase		     =							AutoTokenizer
    lowerCamelCase		     =							AutoModelForSeqaSeqLM
    lowerCamelCase		     =							LANGUAGE_CODES

    lowerCamelCase		     =							['text', 'text', 'text']
    lowerCamelCase		     =							['text']


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	int,lowercase_			:	Optional[int],lowercase_			:	Dict		)->   Any:





        '''simple docstring'''







        if src_lang not in self.lang_to_code:
            raise ValueError(F'{src_lang} is not a supported language.'		)
        if tgt_lang not in self.lang_to_code:
            raise ValueError(F'{tgt_lang} is not a supported language.'		)
        A__   				=       self.lang_to_code[src_lang]
        A__   				=       self.lang_to_code[tgt_lang]
        return self.pre_processor._build_translation_inputs(
            lowercase_,return_tensors='pt',src_lang=lowercase_,tgt_lang=lowercase_		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	Dict		)->   List[Any]:





        '''simple docstring'''







        return self.model.generate(**lowercase_		)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Union[str, Any]		)->   Optional[int]:





        '''simple docstring'''







        return self.post_processor.decode(outputs[0].tolist(),skip_special_tokens=lowercase_		)




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

