





'''simple docstring'''

import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowerCAmelCase							:	Optional[int]			=16
__lowerCAmelCase							:	Tuple			=32





def 						UpperCamelCase				(							_lowerCamelCase					:					Accelerator						,       _lowerCamelCase					:					DatasetDict						,       _lowerCamelCase					:					List[int]						,       _lowerCamelCase					:					List[int]						,       _lowerCamelCase					:					int = 16       ):
    A__				=			AutoTokenizer.from_pretrained("bert-base-cased"       )
    A__				=			DatasetDict(
        {
            "train": dataset["train"].select(_lowerCamelCase       ),
            "validation": dataset["train"].select(_lowerCamelCase       ),
            "test": dataset["validation"],
        }       )

    def tokenize_function(_lowerCamelCase					:					Dict       ):
        # max_length=None => use the model max length (it's actually the default)
        A__				=			tokenizer(examples["sentence1"]						,       examples["sentence2"]						,       truncation=_lowerCamelCase						,       max_length=_lowerCamelCase       )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        A__				=			datasets.map(
            _lowerCamelCase						,       batched=_lowerCamelCase						,       remove_columns=["idx", "sentence1", "sentence2"]						,       )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    A__				=			tokenized_datasets.rename_column("label"						,       "labels"       )

    def collate_fn(_lowerCamelCase					:					Any       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__				=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            A__				=			16
        elif accelerator.mixed_precision != "no":
            A__				=			8
        else:
            A__				=			None

        return tokenizer.pad(
            _lowerCamelCase						,       padding="longest"						,       max_length=_lowerCamelCase						,       pad_to_multiple_of=_lowerCamelCase						,       return_tensors="pt"						,       )

    # Instantiate dataloaders.
    A__				=			DataLoader(
        tokenized_datasets["train"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )
    A__				=			DataLoader(
        tokenized_datasets["validation"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )

    A__				=			DataLoader(
        tokenized_datasets["test"]						,       shuffle=_lowerCamelCase						,       collate_fn=_lowerCamelCase						,       batch_size=_lowerCamelCase       )

    return train_dataloader, eval_dataloader, test_dataloader





def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					str       ):
    # New Code #
    A__				=			[]
    # Download the dataset
    A__				=			load_dataset("glue"						,       "mrpc"       )
    # Create our splits
    A__				=			StratifiedKFold(n_splits=int(args.num_folds       )       )
    # Initialize accelerator
    A__				=			Accelerator(cpu=args.cpu						,       mixed_precision=args.mixed_precision       )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__				=			config["lr"]
    A__				=			int(config["num_epochs"]       )
    A__				=			int(config["seed"]       )
    A__				=			int(config["batch_size"]       )

    A__				=			evaluate.load("glue"						,       "mrpc"       )

    # If the batch size is too big we use gradient accumulation
    A__				=			1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        A__				=			batch_size // MAX_GPU_BATCH_SIZE
        A__				=			MAX_GPU_BATCH_SIZE

    set_seed(_lowerCamelCase       )

    # New Code #
    # Create our folds:
    A__				=			kfold.split(np.zeros(datasets["train"].num_rows       )						,       datasets["train"]["label"]       )
    A__				=			[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(_lowerCamelCase       ):
        A__,	A__,	A__				=			get_fold_dataloaders(
            _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        A__				=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,       return_dict=_lowerCamelCase       )

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        A__				=			model.to(accelerator.device       )

        # Instantiate optimizer
        A__				=			AdamW(params=model.parameters()						,       lr=_lowerCamelCase       )

        # Instantiate scheduler
        A__				=			get_linear_schedule_with_warmup(
            optimizer=_lowerCamelCase						,       num_warmup_steps=1_00						,       num_training_steps=(len(_lowerCamelCase       ) * num_epochs) // gradient_accumulation_steps						,       )

        # Prepare everything
        # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
        # prepare method.
        A__,	A__,	A__,	A__,	A__				=			accelerator.prepare(
            _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

        # Now we train the model
        for epoch in range(_lowerCamelCase       ):
            model.train()
            for step, batch in enumerate(_lowerCamelCase       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                A__				=			model(**_lowerCamelCase       )
                A__				=			outputs.loss
                A__				=			loss / gradient_accumulation_steps
                accelerator.backward(_lowerCamelCase       )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(_lowerCamelCase       ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device       )
                with torch.no_grad():
                    A__				=			model(**_lowerCamelCase       )
                A__				=			outputs.logits.argmax(dim=-1       )
                A__,	A__				=			accelerator.gather_for_metrics((predictions, batch["labels"])       )
                metric.add_batch(
                    predictions=_lowerCamelCase						,       references=_lowerCamelCase						,       )

            A__				=			metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"epoch {epoch}:"						,       _lowerCamelCase       )

        # New Code #
        # We also run predictions on the test set at the very end
        A__				=			[]
        for step, batch in enumerate(_lowerCamelCase       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            with torch.no_grad():
                A__				=			model(**_lowerCamelCase       )
            A__				=			outputs.logits
            A__,	A__				=			accelerator.gather_for_metrics((predictions, batch["labels"])       )
            fold_predictions.append(predictions.cpu()       )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(_lowerCamelCase						,       dim=0       )       )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    A__				=			torch.cat(_lowerCamelCase						,       dim=0       )
    A__				=			torch.stack(_lowerCamelCase						,       dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    A__				=			metric.compute(predictions=_lowerCamelCase						,       references=_lowerCamelCase       )
    accelerator.print("Average test metrics from all folds:"						,       _lowerCamelCase       )





def 						UpperCamelCase				(							):
    A__				=			argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision"						,       type=_lowerCamelCase						,       default=_lowerCamelCase						,       choices=["no", "fp16", "bf16", "fp8"]						,       help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU."						,       )
    parser.add_argument("--cpu"						,       action="store_true"						,       help="If passed, will train on the CPU."       )
    # New Code #
    parser.add_argument("--num_folds"						,       type=_lowerCamelCase						,       default=3						,       help="The number of splits to perform across the dataset"       )
    A__				=			parser.parse_args()
    A__				=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase						,       _lowerCamelCase       )


if __name__ == "__main__":
  main()










'''simple docstring'''

# 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							:	Optional[Any]			={
    "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    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""facebook/nllb-200-distilled-600M"""
 __lowercase   		=				(
     """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`."""
 )
 __lowercase   		=				"""translator"""
 __lowercase   		=				AutoTokenizer
 __lowercase   		=				AutoModelForSeqaSeqLM
 __lowercase   		=				LANGUAGE_CODES

 __lowercase   		=				["""text""", """text""", """text"""]
 __lowercase   		=				["""text"""]
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :Any						,       lowercase_      :List[str]						,       lowercase_      :int							)->							str:
     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      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Any							)->							int:
     return self.model.generate(**lowercase_							)



 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Optional[Any]							)->							str:
     return self.post_processor.decode(outputs[0].tolist()						,       skip_special_tokens=lowercase_							)



