





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_SCREAMING_SNAKE_CASE	 =							16
_SCREAMING_SNAKE_CASE	 =							32


def 	__a(SCREAMING_SNAKE_CASE_  :					Accelerator		,       SCREAMING_SNAKE_CASE_  :					int = 16		,       SCREAMING_SNAKE_CASE_  :					str = "bert-base-cased"    ):



    '''simple docstring'''





    _lowerCAmelCase				=      AutoTokenizer.from_pretrained(A__    )
    _lowerCAmelCase				=      load_dataset("glue"		,       "mrpc"    )

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    _lowerCAmelCase				=      datasets.map(
        A__		,       batched=A__		,       remove_columns=["idx", "sentence1", "sentence2"]		,       load_from_cache_file=A__    )

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

    def collate_fn(SCREAMING_SNAKE_CASE_  :					List[str]    ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(A__		,       padding="max_length"		,       max_length=128		,       return_tensors="pt"    )
        return tokenizer.pad(A__		,       padding="longest"		,       return_tensors="pt"    )

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

    return train_dataloader, eval_dataloader


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					Any		,       SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    model.eval()
    _lowerCAmelCase				=      0
    for step, batch in enumerate(A__    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        with torch.no_grad():
            _lowerCAmelCase				=      model(**A__    )
        _lowerCAmelCase				=      outputs.logits.argmax(dim=-1    )
        # It is slightly faster to call this once, than multiple times
        _lowerCAmelCase			,			_lowerCAmelCase				=      accelerator.gather(
            (predictions, batch["labels"])    )  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(A__    ) - 1:
                _lowerCAmelCase				=      predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                _lowerCAmelCase				=      references[: len(eval_dataloader.dataset    ) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=A__		,       references=A__		,       )

    _lowerCAmelCase				=      metric.compute()
    return eval_metric["accuracy"]


def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    _lowerCAmelCase				=      Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowerCAmelCase				=      config["lr"]
    _lowerCAmelCase				=      int(config["num_epochs"]    )
    _lowerCAmelCase				=      int(config["seed"]    )
    _lowerCAmelCase				=      int(config["batch_size"]    )
    _lowerCAmelCase				=      args.model_name_or_path

    set_seed(A__    )
    _lowerCAmelCase			,			_lowerCAmelCase				=      get_dataloaders(A__		,       A__		,       A__    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCAmelCase				=      AutoModelForSequenceClassification.from_pretrained(A__		,       return_dict=A__    )

    # Instantiate optimizer
    _lowerCAmelCase				=      (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _lowerCAmelCase				=      optimizer_cls(params=model.parameters()		,       lr=A__    )

    if accelerator.state.deepspeed_plugin is not None:
        _lowerCAmelCase				=      accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        _lowerCAmelCase				=      1
    _lowerCAmelCase				=      (len(A__    ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _lowerCAmelCase				=      get_linear_schedule_with_warmup(
            optimizer=A__		,       num_warmup_steps=0		,       num_training_steps=A__		,       )
    else:
        _lowerCAmelCase				=      DummyScheduler(A__		,       total_num_steps=A__		,       warmup_num_steps=0    )

    # 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.
    _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      accelerator.prepare(
        A__		,       A__		,       A__		,       A__		,       A__    )

    # We need to keep track of how many total steps we have iterated over
    _lowerCAmelCase				=      0
    # We also need to keep track of the stating epoch so files are named properly
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      evaluate.load("glue"		,       "mrpc"    )
    _lowerCAmelCase				=      num_epochs

    if args.partial_train_epoch is not None:
        _lowerCAmelCase				=      args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint    )
        _lowerCAmelCase				=      args.resume_from_checkpoint.split("epoch_"    )[1]
        _lowerCAmelCase				=      ""
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        _lowerCAmelCase				=      int(A__    ) + 1
        _lowerCAmelCase				=      evaluation_loop(A__		,       A__		,       A__		,       A__    )
        accelerator.print("resumed checkpoint performance:"		,       A__    )
        accelerator.print("resumed checkpoint's scheduler's lr:"		,       lr_scheduler.get_lr()[0]    )
        accelerator.print("resumed optimizers's lr:"		,       optimizer.param_groups[0]["lr"]    )
        with open(os.path.join(args.output_dir		,       F'''state_{starting_epoch-1}.json'''    )		,       "r"    ) as f:
            _lowerCAmelCase				=      json.load(A__    )
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    _lowerCAmelCase				=      {}
    for epoch in range(A__		,       A__    ):
        model.train()
        for step, batch in enumerate(A__    ):
            _lowerCAmelCase				=      model(**A__    )
            _lowerCAmelCase				=      outputs.loss
            _lowerCAmelCase				=      loss / gradient_accumulation_steps
            accelerator.backward(A__    )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        _lowerCAmelCase				=      F'''epoch_{epoch}'''
        _lowerCAmelCase				=      os.path.join(args.output_dir		,       A__    )
        accelerator.save_state(A__    )
        _lowerCAmelCase				=      evaluation_loop(A__		,       A__		,       A__		,       A__    )
        _lowerCAmelCase				=      accuracy
        _lowerCAmelCase				=      lr_scheduler.get_lr()[0]
        _lowerCAmelCase				=      optimizer.param_groups[0]["lr"]
        _lowerCAmelCase				=      epoch
        _lowerCAmelCase				=      overall_step
        accelerator.print(F'''epoch {epoch}:'''		,       A__    )

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir		,       F'''state_{epoch}.json'''    )		,       "w"    ) as f:
                json.dump(A__		,       A__    )


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."    )
    parser.add_argument(
        "--model_name_or_path"		,       type=A__		,       default="bert-base-cased"		,       help="Path to pretrained model or model identifier from huggingface.co/models."		,       required=A__		,       )
    parser.add_argument(
        "--output_dir"		,       type=A__		,       default="."		,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,       )
    parser.add_argument(
        "--resume_from_checkpoint"		,       type=A__		,       default=A__		,       help="If the training should continue from a checkpoint folder."		,       )
    parser.add_argument(
        "--partial_train_epoch"		,       type=A__		,       default=A__		,       help="If passed, the training will stop after this number of epochs."		,       )
    parser.add_argument(
        "--num_epochs"		,       type=A__		,       default=2		,       help="Number of train epochs."		,       )
    _lowerCAmelCase				=      parser.parse_args()
    _lowerCAmelCase				=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

    training_function(A__		,       A__    )


if __name__ == "__main__":
       main()









'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_SCREAMING_SNAKE_CASE	 =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							Optional[Any]         =					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __lowerCamelCase						:							str         =					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  __lowerCamelCase						:							Optional[int]         =					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  __lowerCamelCase						:							List[Any]         =					{
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }




 @require_torch
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "This is bad"]			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=1		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     # Legacy behavior
     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             {"label": "LABEL_0", "score": 0.504},
             {"label": "LABEL_0", "score": 0.504},
         ]			,     )




 @require_torch
 def 							_snake_case   (	self		)    ->							Tuple:
     import torch

     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"			,     device=torch.device("cpu"		)			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @slow
 @require_torch
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      pipeline("text-classification"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 @slow
 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline("text-classification"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      TextClassificationPipeline(model=_lowerCAmelCase			,     tokenizer=_lowerCAmelCase		)
     return text_classifier, ["HuggingFace is in", "This is another test"]





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     _lowerCAmelCase				=      "HuggingFace is in"
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)

     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]		)
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)

     _lowerCAmelCase				=      ["HuggingFace is in ", "Paris is in France"]
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}, {"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]			,     )
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)
     self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()		)

     # Forcing to get all results with `top_k=None`
     # This is NOT the legacy format
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase			,     top_k=_lowerCAmelCase		)
     _lowerCAmelCase				=      len(model.config.idalabel.values()		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N, [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N]			,     )

     _lowerCAmelCase				=      {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     {"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}			,     )
     self.assertTrue(outputs["label"] in model.config.idalabel.values()		)

     # This might be used a text pair, but tokenizer + pipe interaction
     # makes it hard to understand that it's not using the pair properly
     # https://github.com/huggingface/transformers/issues/17305
     # We disabled this usage instead as it was outputting wrong outputs.
     _lowerCAmelCase				=      [["HuggingFace is in ", "Paris is in France"]]
     with self.assertRaises(_lowerCAmelCase		):
         text_classifier(_lowerCAmelCase		)

     # This used to be valid for doing text pairs
     # We're keeping it working because of backward compatibility
     _lowerCAmelCase				=      text_classifier([[["HuggingFace is in ", "Paris is in France"]]]		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]			,     )
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)


