




# 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


_lowerCamelCase      :  Tuple      						=						{
    "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        __snake_case  (_a		):
				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       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    Optional[int]:





								'''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."      )
								_lowerCAmelCase    :    Dict										=      self.lang_to_code[src_lang]
								_lowerCAmelCase    :    List[str]										=      self.lang_to_code[tgt_lang]
								return self.pre_processor._build_translation_inputs(
								    _UpperCAmelCase		,	return_tensors="""pt"""		,	src_lang=_UpperCAmelCase		,	tgt_lang=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		List[str]      )  ->    str:





								'''simple docstring'''




								return self.model.generate(**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Any      )  ->    int:





								'''simple docstring'''




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







import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_lowerCamelCase      :  int      						=						"bert-base-cased"
_lowerCamelCase      :  Union[str, Any]      						=						"google/pegasus-xsum"
_lowerCamelCase      :  Optional[int]      						=						[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCamelCase      :  Dict      						=						["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCamelCase      :  Any      						=						"patrickvonplaten/t5-tiny-random"
_lowerCamelCase      :  Dict      						=						"sshleifer/bart-tiny-random"
_lowerCamelCase      :  int      						=						"sshleifer/tiny-mbart"
_lowerCamelCase      :  List[Any]      						=						"sshleifer/tiny-marian-en-de"




def 							_UpperCAmelCase  (UpperCamelCase_ : Path      ,    UpperCamelCase_ : list       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      """\n""".join(UpperCamelCase_       )
				Path(UpperCamelCase_       ).open("""w"""       ).writelines(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.source"       )      ,    UpperCamelCase_       )
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.target"       )      ,    UpperCamelCase_       )
				return tmp_dir





class        __snake_case  (_a		):



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Any										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    Dict										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    List[str]										=      4
								_lowerCAmelCase    :    List[Any]										=      8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
								_lowerCAmelCase    :    Tuple										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	src_lang=_UpperCAmelCase		,	tgt_lang=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    int										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_src_len
												# show that targets are the same len
												assert batch["labels"].shape[1] == max_tgt_len
												if tok_name != MBART_TINY:
																continue
												# check language codes in correct place
												_lowerCAmelCase    :    Dict										=      shift_tokens_right(batch["""labels"""]		,	tokenizer.pad_token_id      )
												assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
												assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

												break  # No need to test every batch



				@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Optional[int]										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    str										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    Union[str, Any]										=      4
								_lowerCAmelCase    :    Dict										=      LegacySeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=20		,	max_target_length=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_len_source
												assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
												# show that targets were truncated
												assert batch["labels"].shape[1] == trunc_target  # Truncated
												assert max_len_target > trunc_target  # Truncated
												break  # No need to test every batch



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""      )

								_lowerCAmelCase    :    List[str]										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								_lowerCAmelCase    :    List[Any]										=      tmp_dir.joinpath("""train.source"""      ).open().readlines()
								_lowerCAmelCase    :    Tuple										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								pack_data_dir(_UpperCAmelCase		,	_UpperCAmelCase		,	128		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      {x.name for x in tmp_dir.iterdir()}
								_lowerCAmelCase    :    List[Any]										=      {x.name for x in save_dir.iterdir()}
								_lowerCAmelCase    :    Optional[Any]										=      save_dir.joinpath("""train.source"""      ).open().readlines()
								# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
								# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
								assert len(_UpperCAmelCase      ) < len(_UpperCAmelCase      )
								assert len(_UpperCAmelCase      ) == 1
								assert len(packed_examples[0]      ) == sum(len(_UpperCAmelCase      ) for x in orig_examples      )
								assert orig_paths == new_paths



				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE		,	reason="""This test requires fairseq"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    str:





								'''simple docstring'''




								if not FAIRSEQ_AVAILABLE:
												return
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    int										=      self._get_dataset(max_len=64      )
								_lowerCAmelCase    :    Optional[Any]										=      64
								_lowerCAmelCase    :    Union[str, Any]										=      ds.make_dynamic_sampler(_UpperCAmelCase		,	required_batch_size_multiple=_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      [len(_UpperCAmelCase      ) for x in batch_sampler]
								assert len(set(_UpperCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(_UpperCAmelCase      ) == len(_UpperCAmelCase      )  # no dropped or added examples
								_lowerCAmelCase    :    List[Any]										=      DataLoader(_UpperCAmelCase		,	batch_sampler=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    List[Any]										=      []
								_lowerCAmelCase    :    Tuple										=      []
								for batch in data_loader:
												_lowerCAmelCase    :    Dict										=      batch["""input_ids"""].shape
												_lowerCAmelCase    :    Optional[int]										=      src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												_lowerCAmelCase    :    Dict										=      np.product(batch["""input_ids"""].shape      )
												num_src_per_batch.append(_UpperCAmelCase      )
												if num_src_tokens > (max_tokens * 1.1):
																failures.append(_UpperCAmelCase      )
								assert num_src_per_batch[0] == max(_UpperCAmelCase      )
								if failures:
												raise AssertionError(f"too many tokens in {len(_UpperCAmelCase      )} batches"      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      self._get_dataset(max_len=512      )
								_lowerCAmelCase    :    List[Any]										=      2
								_lowerCAmelCase    :    Tuple										=      ds.make_sortish_sampler(_UpperCAmelCase		,	shuffle=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2		,	sampler=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      tokenizer.pad_token_id

								def count_pad_tokens(_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]="input_ids"      ):
												return [batch[k].eq(_UpperCAmelCase      ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      ) < sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      )
								assert sum(count_pad_tokens(_UpperCAmelCase      )      ) < sum(count_pad_tokens(_UpperCAmelCase      )      )
								assert len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str=1000		,	_UpperCAmelCase      :		Dict=128      )  ->    Dict:





								'''simple docstring'''




								if os.getenv("""USE_REAL_DATA"""		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    Optional[int]										=      """examples/seq2seq/wmt_en_ro"""
												_lowerCAmelCase    :    Optional[int]										=      max_len * 2 * 64
												if not Path(_UpperCAmelCase      ).joinpath("""train.len"""      ).exists():
																save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    int										=      """examples/seq2seq/test_data/wmt_en_ro"""
												_lowerCAmelCase    :    int										=      max_len * 4
												save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	n_obs=_UpperCAmelCase		,	)
								return ds, max_tokens, tokenizer



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self._get_dataset()
								_lowerCAmelCase    :    Optional[Any]										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=0		,	add_extra_examples=_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Any										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=1		,	add_extra_examples=_UpperCAmelCase      )      )
								assert idsa.intersection(_UpperCAmelCase      ) == set()



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase		,	use_fast=_UpperCAmelCase      )
								if tok_name == MBART_TINY:
												_lowerCAmelCase    :    Optional[Any]										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )		,	type_path="""train"""		,	max_source_length=4		,	max_target_length=8		,	src_lang="""EN"""		,	tgt_lang="""FR"""		,	)
												_lowerCAmelCase    :    Tuple										=      train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )		,	type_path="""train"""		,	max_source_length=4		,	max_target_length=8		,	)
												_lowerCAmelCase    :    Optional[Any]										=      train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_UpperCAmelCase      ) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase      ) == 0

