

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           =					16
lowerCamelCase           =					32




def 			UpperCAmelCase__				(			_A      : Accelerator	,      _A      : int = 16	,      _A      : str = "bert-base-cased"							):





  '''simple docstring'''







  a__  =AutoTokenizer.from_pretrained(lowerCAmelCase_							)
  a__  =load_dataset('''glue'''	,      '''mrpc'''							)

  def tokenize_function(_A      : List[str]							):
    # max_length=None => use the model max length (it's actually the default)
    a__  =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
  a__  =datasets.map(
      lowerCAmelCase_	,      batched=lowerCAmelCase_	,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,      load_from_cache_file=lowerCAmelCase_							)

  # 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'''							)

  def collate_fn(_A      : 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(lowerCAmelCase_	,      padding='''max_length'''	,      max_length=1_28	,      return_tensors='''pt'''							)
    return tokenizer.pad(lowerCAmelCase_	,      padding='''longest'''	,      return_tensors='''pt'''							)

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

  return train_dataloader, eval_dataloader




def 			UpperCAmelCase__				(			_A      : List[Any]	,      _A      : Optional[int]							):





  '''simple docstring'''







  a__  =Accelerator()

  # 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''']							)
  a__  =args.model_name_or_path

  set_seed(lowerCAmelCase_							)
  a__  =get_dataloaders(lowerCAmelCase_	,      lowerCAmelCase_	,      lowerCAmelCase_							)

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

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

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

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

  # 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

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

      overall_step += 1

    model.eval()
    a__  =0
    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():
        a__  =model(**lowerCAmelCase_							)
      a__  =outputs.logits.argmax(dim=-1							)
      # It is slightly faster to call this once, than multiple times
      a__  =accelerator.gather(
          (predictions, batch['''labels'''])							)  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(lowerCAmelCase_							) - 1:
          a__  =predictions[: len(eval_dataloader.dataset							) - samples_seen]
          a__  =references[: len(eval_dataloader.dataset							) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=lowerCAmelCase_	,      references=lowerCAmelCase_	,      )

    a__  =metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}:"""	,      lowerCAmelCase_							)
    a__  =eval_metric["""accuracy"""]

    if best_performance < eval_metric["accuracy"]:
      a__  =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(lowerCAmelCase_	,      lowerCAmelCase_							)




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







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


if __name__ == "__main__":
       main()








# Copyright 2023 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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
	from PIL import Image

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					_A  :	Any     =					(
					    '''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
					    '''should be the document containing the information, as well as a `question` that is the question about the '''
					    '''document. It returns a text that contains the answer to the question.'''
					)
					_A  :	Tuple     =					'''document_qa'''
					_A  :	Dict     =					AutoProcessor
					_A  :	Tuple     =					VisionEncoderDecoderModel

					_A  :	Optional[int]     =					['''image''', '''text''']
					_A  :	Optional[int]     =					['''text''']



					def __init__(     self :		Any						,   *__a :		List[str]						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												if not is_vision_available():
																			raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""							)

												super().__init__(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		"Image"						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							int			=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
												__lowercase					:							str			=   task_prompt.replace("""{user_input}"""						,   __a							)
												__lowercase					:							Union[str, Any]			=   self.pre_processor.tokenizer(
												    __a						,   add_special_tokens=__a						,   return_tensors="""pt"""							).input_ids
												__lowercase					:							int			=   self.pre_processor(__a						,   return_tensors="""pt"""							).pixel_values

												return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		int							)	->     int:


												"""simple docstring"""

												return self.model.generate(
												    inputs["""pixel_values"""].to(self.device							)						,   decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device							)						,   max_length=self.model.decoder.config.max_position_embeddings						,   early_stopping=__a						,   pad_token_id=self.pre_processor.tokenizer.pad_token_id						,   eos_token_id=self.pre_processor.tokenizer.eos_token_id						,   use_cache=__a						,   num_beams=1						,   bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]						,   return_dict_in_generate=__a						,   ).sequences



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.pre_processor.batch_decode(__a							)[0]
												__lowercase					:							int			=   sequence.replace(self.pre_processor.tokenizer.eos_token						,   """"""							)
												__lowercase					:							Union[str, Any]			=   sequence.replace(self.pre_processor.tokenizer.pad_token						,   """"""							)
												__lowercase					:							Optional[Any]			=   re.sub(r"""<.*?>"""						,   """"""						,   __a						,   count=1							).strip()  # remove first task start token
												__lowercase					:							Dict			=   self.pre_processor.tokenajson(__a							)

												return sequence["answer"]