






"""simple docstring"""


import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowercase_								=	16
lowercase_								=	32



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if args.with_tracking:
    __A		     =					Accelerator(
        cpu=args.cpu						,			mixed_precision=args.mixed_precision						,			log_with='''all'''						,			project_dir=args.project_dir					)
  else:
    __A		     =					Accelerator(cpu=args.cpu						,			mixed_precision=args.mixed_precision					)

  if hasattr(args.checkpointing_steps						,			'''isdigit'''					):
    if args.checkpointing_steps == "epoch":
      __A		     =					args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      __A		     =					int(args.checkpointing_steps					)
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'					)
  else:
    __A		     =					None
  # 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''']					)

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    __A		     =					os.path.split(__UpperCamelCase					)[-1].split('''.'''					)[0]
    accelerator.init_trackers(__UpperCamelCase						,			__UpperCamelCase					)

  __A		     =					AutoTokenizer.from_pretrained('''bert-base-cased'''					)
  __A		     =					load_dataset('''glue'''						,			'''mrpc'''					)
  __A		     =					evaluate.load('''glue'''						,			'''mrpc'''					)

  def tokenize_function(__UpperCamelCase					):
    # max_length=None => use the model max length (it's actually the default)
    __A		     =					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
  # starting with the main process first:
  with accelerator.main_process_first():
    __A		     =					datasets.map(
        __UpperCamelCase						,			batched=__UpperCamelCase						,			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'''					)

  # 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

  def collate_fn(__UpperCamelCase					):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __A		     =					1_2_8 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		     =					1_6
    elif accelerator.mixed_precision != "no":
      __A		     =					8
    else:
      __A		     =					None

    return tokenizer.pad(
        __UpperCamelCase						,			padding='''longest'''						,			max_length=__UpperCamelCase						,			pad_to_multiple_of=__UpperCamelCase						,			return_tensors='''pt'''						,			)

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

  set_seed(__UpperCamelCase					)

  # 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=__UpperCamelCase					)

  # 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=__UpperCamelCase					)

  # Instantiate scheduler
  __A		     =					get_linear_schedule_with_warmup(
      optimizer=__UpperCamelCase						,			num_warmup_steps=1_0_0						,			num_training_steps=(len(__UpperCamelCase					) * 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(
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

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

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'					)
      accelerator.load_state(args.resume_from_checkpoint					)
      __A		     =					os.path.basename(args.resume_from_checkpoint					)
    else:
      # Get the most recent checkpoint
      __A		     =					[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
      dirs.sort(key=os.path.getctime					)
      __A		     =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    __A		     =					os.path.splitext(__UpperCamelCase					)[0]

    if "epoch" in training_difference:
      __A		     =					int(training_difference.replace('''epoch_'''						,			''''''					)					) + 1
      __A		     =					None
    else:
      __A		     =					int(training_difference.replace('''step_'''						,			''''''					)					)
      __A		     =					resume_step // len(__UpperCamelCase					)
      resume_step -= starting_epoch * len(__UpperCamelCase					)

    # Now we train the model
  for epoch in range(__UpperCamelCase						,			__UpperCamelCase					):
    model.train()
    if args.with_tracking:
      __A		     =					0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      __A		     =					accelerator.skip_first_batches(__UpperCamelCase						,			__UpperCamelCase					)
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      __A		     =					train_dataloader
    for step, batch in enumerate(__UpperCamelCase					):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device					)
      __A		     =					model(**__UpperCamelCase					)
      __A		     =					outputs.loss
      __A		     =					loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(__UpperCamelCase					)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(__UpperCamelCase						,			__UpperCamelCase					):
        __A		     =					f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            __A		     =					os.path.join(args.output_dir						,			__UpperCamelCase					)
          accelerator.save_state(__UpperCamelCase					)

    model.eval()
    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():
        __A		     =					model(**__UpperCamelCase					)
      __A		     =					outputs.logits.argmax(dim=-1					)
      __A     ,       __A		     =					accelerator.gather_for_metrics((predictions, batch['''labels'''])					)
      metric.add_batch(
          predictions=__UpperCamelCase						,			references=__UpperCamelCase						,			)

    __A		     =					metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'						,			__UpperCamelCase					)
    if args.with_tracking:
      accelerator.log(
          {
              '''accuracy''': eval_metric['''accuracy'''],
              '''f1''': eval_metric['''f1'''],
              '''train_loss''': total_loss.item() / len(__UpperCamelCase					),
              '''epoch''': epoch,
          }						,			step=__UpperCamelCase						,			)

    if checkpointing_steps == "epoch":
      __A		     =					f'epoch_{epoch}'
      if args.output_dir is not None:
        __A		     =					os.path.join(args.output_dir						,			__UpperCamelCase					)
      accelerator.save_state(__UpperCamelCase					)

  if args.with_tracking:
    accelerator.end_training()



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					argparse.ArgumentParser(description='''Simple example of training script.'''					)
  parser.add_argument(
      '''--mixed_precision'''						,			type=__UpperCamelCase						,			default=__UpperCamelCase						,			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.'''					)
  parser.add_argument(
      '''--checkpointing_steps'''						,			type=__UpperCamelCase						,			default=__UpperCamelCase						,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''						,			)
  parser.add_argument(
      '''--resume_from_checkpoint'''						,			type=__UpperCamelCase						,			default=__UpperCamelCase						,			help='''If the training should continue from a checkpoint folder.'''						,			)
  parser.add_argument(
      '''--with_tracking'''						,			action='''store_true'''						,			help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''						,			)
  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(
      '''--project_dir'''						,			type=__UpperCamelCase						,			default='''logs'''						,			help='''Location on where to store experiment tracking logs` and relevent project information'''						,			)
  __A		     =					parser.parse_args()
  __A		     =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
  training_function(__UpperCamelCase						,			__UpperCamelCase					)


if __name__ == "__main__":
       main()









"""simple docstring"""


import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

snake_case_								=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def 					_lowerCAmelCase (  lowercase_		):
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		UpperCAmelCase			 =					model_type_to_module_name(lowercase_		)

																		UpperCAmelCase			 =					importlib.import_module(F""".{module_name}"""						,      'transformers.models'		)
																		try:
																								return getattr(lowercase_						,      lowercase_		)
																		except AttributeError:
																								continue

						for processor in PROCESSOR_MAPPING._extra_content.values():
												if getattr(lowercase_						,      '__name__'						,      lowercase_		) == class_name:
																		return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						UpperCAmelCase			 =					importlib.import_module('transformers'		)
						if hasattr(lowercase_						,      lowercase_		):
												return getattr(lowercase_						,      lowercase_		)

						return None







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]   )							->     str:
												raise EnvironmentError(
												    'AutoProcessor is designed to be instantiated '
												    'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'   )






						@classmethod
						@replace_list_option_in_docstrings(lowercase_   )
						def       UpperCAmelCase__	(   cls					:Optional[int]  ,    lowercase_					:Tuple  ,    **lowercase_					:int   )							->     Optional[int]:
												UpperCAmelCase			 =					kwargs.pop('config'  ,    lowercase_   )
												UpperCAmelCase			 =					kwargs.pop('trust_remote_code'  ,    lowercase_   )
												UpperCAmelCase			 =					True

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

												# First, let's see if we have a preprocessor config.
												# Filter the kwargs for `get_file_from_repo`.
												UpperCAmelCase			 =					{
												    key: kwargs[key] for key in inspect.signature(lowercase_   ).parameters.keys() if key in kwargs
												}
												# Let's start by checking whether the processor class is saved in an image processor
												UpperCAmelCase			 =					get_file_from_repo(
												    lowercase_  ,    lowercase_  ,    **lowercase_   )
												if preprocessor_config_file is not None:
																		UpperCAmelCase ,       UpperCAmelCase			 =					ImageProcessingMixin.get_image_processor_dict(lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																		if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																								UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
												if preprocessor_config_file is not None and processor_class is None:
																		UpperCAmelCase ,       UpperCAmelCase			 =					FeatureExtractionMixin.get_feature_extractor_dict(lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																		if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																								UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

												if processor_class is None:
																		# Next, let's check whether the processor class is saved in a tokenizer
																		UpperCAmelCase			 =					get_file_from_repo(
																		    lowercase_  ,    lowercase_  ,    **lowercase_   )
																		if tokenizer_config_file is not None:
																								with open(lowercase_  ,    encoding='utf-8'   ) as reader:
																														UpperCAmelCase			 =					json.load(lowercase_   )

																								UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																								if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																														UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

												if processor_class is None:
																		# Otherwise, load config, if it can be loaded.
																		if not isinstance(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					AutoConfig.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )

																		# And check if the config contains the processor class.
																		UpperCAmelCase			 =					getattr(lowercase_  ,    'processor_class'  ,    lowercase_   )
																		if hasattr(lowercase_  ,    'auto_map'   ) and "AutoProcessor" in config.auto_map:
																								UpperCAmelCase			 =					config.auto_map['AutoProcessor']

												if processor_class is not None:
																		UpperCAmelCase			 =					processor_class_from_name(lowercase_   )

												UpperCAmelCase			 =					processor_auto_map is not None
												UpperCAmelCase			 =					processor_class is not None or type(lowercase_   ) in PROCESSOR_MAPPING
												UpperCAmelCase			 =					resolve_trust_remote_code(
												    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )

												if has_remote_code and trust_remote_code:
																		UpperCAmelCase			 =					get_class_from_dynamic_module(
																		    lowercase_  ,    lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					kwargs.pop('code_revision'  ,    lowercase_   )
																		if os.path.isdir(lowercase_   ):
																								processor_class.register_for_auto_class()
																		return processor_class.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												elif processor_class is not None:
																		return processor_class.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												# Last try: we use the PROCESSOR_MAPPING.
												elif type(lowercase_   ) in PROCESSOR_MAPPING:
																		return PROCESSOR_MAPPING[type(lowercase_   )].from_pretrained(lowercase_  ,    **lowercase_   )

												# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
												# tokenizer.
												try:
																		return AutoTokenizer.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												except Exception:
																		try:
																								return AutoImageProcessor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		except Exception:
																								pass

																		try:
																								return AutoFeatureExtractor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		except Exception:
																								pass

												raise ValueError(
												    f"""Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a """
												    'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
												    'the files of at least one of those processing classes.'   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:int  ,    lowercase_					:Union[str, Any]   )							->     Tuple:
												PROCESSOR_MAPPING.register(lowercase_  ,    lowercase_   )





