




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


lowerCamelCase__ :   Optional[Any]					=							16
lowerCamelCase__ :   Optional[Any]					=							32

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Accelerator			,			__UpperCAmelCase						:	int = 16			,			__UpperCAmelCase						:	str = "bert-base-cased"							)					-> Optional[int]:
      SCREAMING_SNAKE_CASE_							=			AutoTokenizer.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			load_dataset('glue'			,			'mrpc'							)

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

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

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

      def collate_fn(__UpperCAmelCase						:	Dict							):
            # 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(__UpperCAmelCase			,			padding='max_length'			,			max_length=1_28			,			return_tensors='pt'							)
            return tokenizer.pad(__UpperCAmelCase			,			padding='longest'			,			return_tensors='pt'							)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_							=			DataLoader(
          tokenized_datasets['train']			,			shuffle=__UpperCAmelCase			,			collate_fn=__UpperCAmelCase			,			batch_size=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			DataLoader(
          tokenized_datasets['validation']			,			shuffle=__UpperCAmelCase			,			collate_fn=__UpperCAmelCase			,			batch_size=__UpperCAmelCase							)

      return train_dataloader, eval_dataloader

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Union[str, Any]							)					-> Optional[int]:
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_							=			Accelerator()

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

      set_seed(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			get_dataloaders(__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							)

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

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

      if accelerator.state.deepspeed_plugin is not None:
            SCREAMING_SNAKE_CASE_							=			accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            SCREAMING_SNAKE_CASE_							=			1
      SCREAMING_SNAKE_CASE_							=			(len(__UpperCAmelCase							) * num_epochs) // gradient_accumulation_steps

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

      # We need to keep track of how many total steps we have iterated over
      SCREAMING_SNAKE_CASE_							=			0
      # We also need to keep track of the stating epoch so files are named properly
      SCREAMING_SNAKE_CASE_							=			0

      # Now we train the model
      SCREAMING_SNAKE_CASE_							=			evaluate.load('glue'			,			'mrpc'							)
      SCREAMING_SNAKE_CASE_							=			0
      SCREAMING_SNAKE_CASE_							=			{}
      for epoch in range(__UpperCAmelCase			,			__UpperCAmelCase							):
            model.train()
            for step, batch in enumerate(__UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_							=			model(**__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_							=			outputs.loss
                  SCREAMING_SNAKE_CASE_							=			loss / gradient_accumulation_steps
                  accelerator.backward(__UpperCAmelCase							)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

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

            SCREAMING_SNAKE_CASE_							=			metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"			,			__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			eval_metric['accuracy']

            if best_performance < eval_metric["accuracy"]:
                  SCREAMING_SNAKE_CASE_							=			eval_metric['accuracy']

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir			,			'all_results.json'							)			,			'w'							) as f:
                  json.dump(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      )					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
      parser.add_argument(
          '--model_name_or_path'			,			type=__UpperCAmelCase			,			default='bert-base-cased'			,			help='Path to pretrained model or model identifier from huggingface.co/models.'			,			required=__UpperCAmelCase			,			)
      parser.add_argument(
          '--output_dir'			,			type=__UpperCAmelCase			,			default='.'			,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,			)
      parser.add_argument(
          '--performance_lower_bound'			,			type=__UpperCAmelCase			,			default=__UpperCAmelCase			,			help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'			,			)
      parser.add_argument(
          '--num_epochs'			,			type=__UpperCAmelCase			,			default=3			,			help='Number of train epochs.'			,			)
      SCREAMING_SNAKE_CASE_							=			parser.parse_args()
      SCREAMING_SNAKE_CASE_							=			{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
      training_function(__UpperCAmelCase			,			__UpperCAmelCase							)


if __name__ == "__main__":
   main()





# Copyright 2021 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 packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowerCamelCase__ :   str					=							'pytorch_model.bin'
lowerCamelCase__ :   int					=							'pytorch_model.bin.index.json'
lowerCamelCase__ :   List[Any]					=							'adapter_config.json'
lowerCamelCase__ :   Optional[int]					=							'adapter_model.bin'
lowerCamelCase__ :   Dict					=							'adapter_model.safetensors'
lowerCamelCase__ :   List[str]					=							'tf_model.h5'
lowerCamelCase__ :   Tuple					=							'tf_model.h5.index.json'
lowerCamelCase__ :   Optional[int]					=							'model.ckpt'
lowerCamelCase__ :   Any					=							'flax_model.msgpack'
lowerCamelCase__ :   List[Any]					=							'flax_model.msgpack.index.json'
lowerCamelCase__ :   int					=							'model.safetensors'
lowerCamelCase__ :   str					=							'model.safetensors.index.json'
lowerCamelCase__ :   Union[str, Any]					=							'config.json'
lowerCamelCase__ :   List[Any]					=							'preprocessor_config.json'
lowerCamelCase__ :   Optional[int]					=							FEATURE_EXTRACTOR_NAME
lowerCamelCase__ :   Any					=							'generation_config.json'
lowerCamelCase__ :   Any					=							'modelcard.json'

lowerCamelCase__ :   List[str]					=							'▁'
lowerCamelCase__ :   Union[str, Any]					=							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowerCamelCase__ :   Optional[int]					=							[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase__ :   str					=							[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase__ :   Dict					=							[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]							)					-> int:
      if version.parse(__UpperCAmelCase							) < version.parse(__UpperCAmelCase							):
            if "dev" in min_version:
                  SCREAMING_SNAKE_CASE_							=			(
                      'This example requires a source install from HuggingFace Transformers (see '
                      '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                  )
            else:
                  SCREAMING_SNAKE_CASE_							=			f"This example requires a minimum version of {min_version},"
            error_message += f" but the version found is {__version__}.\n"
            raise ImportError(
                error_message
                + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                'versions of HuggingFace Transformers.'							)