





'''simple docstring'''



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


__magic_name__   :						List[Any]    =			16
__magic_name__   :						Tuple    =			32




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 16					,	SCREAMING_SNAKE_CASE__ = "bert-base-cased"       ):







  '''simple docstring'''



  _snake_case     		=				AutoTokenizer.from_pretrained(__snake_case       )
  _snake_case     		=				load_dataset("glue"					,	"mrpc"       )

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

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

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

  def collate_fn(SCREAMING_SNAKE_CASE__       ):
    # 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(__snake_case					,	padding="max_length"					,	max_length=1_28					,	return_tensors="pt"       )
    return tokenizer.pad(__snake_case					,	padding="longest"					,	return_tensors="pt"       )

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

  return train_dataloader, eval_dataloader




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				Accelerator()

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

  set_seed(__snake_case       )
  _snake_case		,		_snake_case     		=				get_dataloaders(__snake_case					,	__snake_case					,	__snake_case       )

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

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

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

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

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

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

      overall_step += 1

    model.eval()
    _snake_case     		=				0
    for step, batch in enumerate(__snake_case       ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device       )
      with torch.no_grad():
        _snake_case     		=				model(**__snake_case       )
      _snake_case     		=				outputs.logits.argmax(dim=-1       )
      # It is slightly faster to call this once, than multiple times
      _snake_case		,		_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(__snake_case       ) - 1:
          _snake_case     		=				predictions[: len(eval_dataloader.dataset       ) - samples_seen]
          _snake_case     		=				references[: len(eval_dataloader.dataset       ) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=__snake_case					,	references=__snake_case					,	)

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

    if best_performance < eval_metric["accuracy"]:
      _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(__snake_case					,	__snake_case       )




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."       )
  parser.add_argument(
      "--model_name_or_path"					,	type=__snake_case					,	default="bert-base-cased"					,	help="Path to pretrained model or model identifier from huggingface.co/models."					,	required=__snake_case					,	)
  parser.add_argument(
      "--output_dir"					,	type=__snake_case					,	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=__snake_case					,	default=__snake_case					,	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=__snake_case					,	default=3					,	help="Number of train epochs."					,	)
  _snake_case     		=				parser.parse_args()
  _snake_case     		=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(__snake_case					,	__snake_case       )


if __name__ == "__main__":
 main()









'''simple docstring'''



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)


@add_end_docstrings(__UpperCamelCase			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       super().__init__(*lowerCamelCase						,   **lowerCamelCase	)
       requires_backends(self						,   "vision"	)
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING	)

     def 	UpperCamelCase(					self						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}
       _snake_case     		=				{}

       if prompt is not None:
         _snake_case     		=				prompt

       if generate_kwargs is not None:
         _snake_case     		=				generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _snake_case     		=				{}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
               " please use only one"	)
         _snake_case     		=				max_new_tokens
       return preprocess_params, forward_kwargs, {}

     def __call__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       return super().__call__(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       _snake_case     		=				load_image(lowerCamelCase	)

       if prompt is not None:
         if not isinstance(lowerCamelCase						,   lowerCamelCase	):
           raise ValueError(
               F'''Received an invalid text input, got - {type(lowerCamelCase	)} - but expected a single string. '''
               "Note also that one single text can be provided for conditional image to text generation."	)

         _snake_case     		=				self.model.config.model_type

         if model_type == "git":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(text=lowerCamelCase						,   add_special_tokens=lowerCamelCase	).input_ids
           _snake_case     		=				[self.tokenizer.cls_token_id] + input_ids
           _snake_case     		=				torch.tensor(lowerCamelCase	).unsqueeze(0	)
           model_inputs.update({"input_ids": input_ids}	)

         elif model_type == "pix2struct":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   header_text=lowerCamelCase						,   return_tensors=self.framework	)

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(lowerCamelCase						,   return_tensors=self.framework	)
           model_inputs.update(lowerCamelCase	)

         else:
           raise ValueError(F'''Model type {model_type} does not support conditional text generation'''	)

       else:
         _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)

       if self.model.config.model_type == "git" and prompt is None:
         _snake_case     		=				None

       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["input_ids"]						,   lowerCamelCase	)
           and all(x is None for x in model_inputs["input_ids"]	)
       ):
         _snake_case     		=				None

       if generate_kwargs is None:
         _snake_case     		=				{}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _snake_case     		=				model_inputs.pop(self.model.main_input_name	)
       _snake_case     		=				self.model.generate(lowerCamelCase						,   **lowerCamelCase						,   **lowerCamelCase	)
       return model_outputs






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for output_ids in model_outputs:
         _snake_case     		=				{
             "generated_text": self.tokenizer.decode(
                 lowerCamelCase						,   skip_special_tokens=lowerCamelCase						,   )
         }
         records.append(lowerCamelCase	)
       return records


