



'''simple docstring'''





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a  			=						"src/transformers"
__a  			=						"docs/source/en/tasks"





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Any:
      with open(_lowerCAmelCase	,  """r"""	,  encoding="""utf-8"""	,  newline="""\n"""						) as f:
            snake_case__    :       List[str]       		= f.readlines()
      # Find the start prompt.
      snake_case__    :       Union[str, Any]       		= 0
      while not lines[start_index].startswith(_lowerCAmelCase						):
            start_index += 1
      start_index += 1

      snake_case__    :       Tuple       		= start_index
      while not lines[end_index].startswith(_lowerCAmelCase						):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]						) <= 1:
            start_index += 1
      while len(lines[end_index]						) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a  			=						direct_transformers_import(TRANSFORMERS_PATH)

__a  			=						{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a  			=						{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 		__snake_case(							_lowerCAmelCase						)						->					Optional[int]:
      snake_case__    :       List[str]       		= TASK_GUIDE_TO_MODELS[task_guide]
      snake_case__    :       Union[str, Any]       		= SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase	,  set()						)
      snake_case__    :       List[str]       		= {
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]						) + "\n"





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=False						)						->					Tuple:
      snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       int       		= _find_text_in_file(
          filename=os.path.join(_lowerCAmelCase	,  _lowerCAmelCase						)	,  start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""	,  end_prompt="""<!--End of the generated tip-->"""	,  )

      snake_case__    :       List[Any]       		= get_model_list_for_task(_lowerCAmelCase						)

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(_lowerCAmelCase	,  _lowerCAmelCase						)	,  """w"""	,  encoding="""utf-8"""	,  newline="""\n"""						) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]						)
            else:
                  raise ValueError(
                      f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
                      """ to fix this."""						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       __a  			=						parser.parse_args()

       for task_guide in TASK_GUIDE_TO_MODELS.keys():
              check_model_list_for_task(task_guide, args.fix_and_overwrite)





'''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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a  			=						16
__a  			=						32





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16						)						->					Optional[Any]:
      snake_case__    :       Optional[int]       		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      snake_case__    :       Optional[int]       		= load_dataset("""glue"""	,  """mrpc"""						)

      def tokenize_function(_lowerCAmelCase						):
            # max_length=None => use the model max length (it's actually the default)
            snake_case__    :       Union[str, Any]       		= 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():
            snake_case__    :       List[str]       		= 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
      snake_case__    :       Any       		= tokenized_datasets.rename_column("""label"""	,  """labels"""						)

      def collate_fn(_lowerCAmelCase						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case__    :       List[str]       		= 128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  snake_case__    :       Optional[Any]       		= 16
            elif accelerator.mixed_precision != "no":
                  snake_case__    :       Tuple       		= 8
            else:
                  snake_case__    :       int       		= None

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

      # Instantiate dataloaders.
      snake_case__    :       List[Any]       		= DataLoader(
          tokenized_datasets["""train"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)
      snake_case__    :       Dict       		= DataLoader(
          tokenized_datasets["""validation"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       __a  			=						mocked_dataloaders  # noqa: F811





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,  _lowerCAmelCase						) == "1":
            snake_case__    :       int       		= 2
      # New Code #
      snake_case__    :       Any       		= int(args.gradient_accumulation_steps						)
      # Initialize accelerator
      snake_case__    :       Any       		= Accelerator(
          cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  gradient_accumulation_steps=_lowerCAmelCase						)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__    :       List[Any]       		= config["""lr"""]
      snake_case__    :       Optional[Any]       		= int(config["""num_epochs"""]						)
      snake_case__    :       Union[str, Any]       		= int(config["""seed"""]						)
      snake_case__    :       List[str]       		= int(config["""batch_size"""]						)

      snake_case__    :       Union[str, Any]       		= evaluate.load("""glue"""	,  """mrpc"""						)

      set_seed(_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       Tuple       		= get_dataloaders(_lowerCAmelCase	,  _lowerCAmelCase						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__    :       Optional[int]       		= 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).
      snake_case__    :       Tuple       		= model.to(accelerator.device						)

      # Instantiate optimizer
      snake_case__    :       Any       		= AdamW(params=model.parameters()	,  lr=_lowerCAmelCase						)

      # Instantiate scheduler
      snake_case__    :       List[Any]       		= get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase	,  num_warmup_steps=100	,  num_training_steps=(len(_lowerCAmelCase						) * num_epochs)	,  )

      # 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.
      snake_case__		,							snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       List[Any]       		= 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						)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(_lowerCAmelCase						):
                        snake_case__    :       Any       		= model(**_lowerCAmelCase						)
                        snake_case__    :       str       		= output.loss
                        accelerator.backward(_lowerCAmelCase						)
                        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():
                        snake_case__    :       str       		= model(**_lowerCAmelCase						)
                  snake_case__    :       Optional[int]       		= outputs.logits.argmax(dim=-1						)
                  snake_case__		,							snake_case__    :       List[Any]       		= accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
                  metric.add_batch(
                      predictions=_lowerCAmelCase	,  references=_lowerCAmelCase	,  )

            snake_case__    :       str       		= metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"	,  _lowerCAmelCase						)





def 		__snake_case(							)						->					List[str]:
      snake_case__    :       List[str]       		= 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."""	,  )
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,  type=_lowerCAmelCase	,  default=1	,  help="""The number of minibatches to be ran before gradients are accumulated."""	,  )
      parser.add_argument("""--cpu"""	,  action="""store_true"""	,  help="""If passed, will train on the CPU."""						)
      snake_case__    :       Tuple       		= parser.parse_args()
      snake_case__    :       Dict       		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()
