






'''simple docstring'''

# 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        a_		(    lowerCamelCase       ):
  lowercase  	=   """naver-clova-ix/donut-base-finetuned-docvqa"""
  lowercase  	=   (
      """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."""
  )
  lowercase  	=   """document_qa"""
  lowercase  	=   AutoProcessor
  lowercase  	=   VisionEncoderDecoderModel

  lowercase  	=   ["""image""", """text"""]
  lowercase  	=   ["""text"""]

  def __init__(   self    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






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

        super().__init__(*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
        UpperCamelCase       						=      task_prompt.replace("""{user_input}"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.pre_processor.tokenizer(
            _SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).input_ids
        UpperCamelCase       						=      self.pre_processor(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[str]:



        """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=_SCREAMING_SNAKE_CASE    ,	pad_token_id=self.pre_processor.tokenizer.pad_token_id    ,	eos_token_id=self.pre_processor.tokenizer.eos_token_id    ,	use_cache=_SCREAMING_SNAKE_CASE    ,	num_beams=1    ,	bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]    ,	return_dict_in_generate=_SCREAMING_SNAKE_CASE    ,	).sequences


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE   )[0]
        UpperCamelCase       						=      sequence.replace(self.pre_processor.tokenizer.eos_token    ,	""""""   )
        UpperCamelCase       						=      sequence.replace(self.pre_processor.tokenizer.pad_token    ,	""""""   )
        UpperCamelCase       						=      re.sub(R"""<.*?>"""    ,	""""""    ,	_SCREAMING_SNAKE_CASE    ,	count=1   ).strip()  # remove first task start token
        UpperCamelCase       						=      self.pre_processor.tokenajson(_SCREAMING_SNAKE_CASE   )

        return sequence["answer"]








'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


SCREAMING_SNAKE_CASE__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = 16					)->				Dict:
      UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
      UpperCamelCase       						=      load_dataset("""glue"""	,		"""mrpc"""					)

      def tokenize_function(__UpperCamelCase					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase       						=      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():
            UpperCamelCase       						=      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
      UpperCamelCase       						=      tokenized_datasets.rename_column("""label"""	,		"""labels"""					)

      def collate_fn(__UpperCamelCase					):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCamelCase       						=      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":
                  UpperCamelCase       						=      16
            elif accelerator.mixed_precision != "no":
                  UpperCamelCase       						=      8
            else:
                  UpperCamelCase       						=      None

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

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

      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

     SCREAMING_SNAKE_CASE__											=     mocked_dataloaders  # noqa: F811







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,		__UpperCamelCase					) == "1":
            UpperCamelCase       						=      2
      # New Code #
      UpperCamelCase       						=      int(args.gradient_accumulation_steps					)
      UpperCamelCase       						=      int(args.local_sgd_steps					)
      # Initialize accelerator
      UpperCamelCase       						=      Accelerator(
          cpu=args.cpu	,		mixed_precision=args.mixed_precision	,		gradient_accumulation_steps=__UpperCamelCase					)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""					)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase       						=      config["""lr"""]
      UpperCamelCase       						=      int(config["""num_epochs"""]					)
      UpperCamelCase       						=      int(config["""seed"""]					)
      UpperCamelCase       						=      int(config["""batch_size"""]					)

      UpperCamelCase       						=      evaluate.load("""glue"""	,		"""mrpc"""					)

      set_seed(__UpperCamelCase					)
      UpperCamelCase						,UpperCamelCase       						=      get_dataloaders(__UpperCamelCase	,		__UpperCamelCase					)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase       						=      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).
      UpperCamelCase       						=      model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCamelCase       						=      AdamW(params=model.parameters()	,		lr=__UpperCamelCase					)

      # Instantiate scheduler
      UpperCamelCase       						=      get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase	,		num_warmup_steps=100	,		num_training_steps=(len(__UpperCamelCase					) * 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.
      UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      accelerator.prepare(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)

      # Now we train the model
      for epoch in range(__UpperCamelCase					):
            model.train()
            with LocalSGD(
                accelerator=__UpperCamelCase	,		model=__UpperCamelCase	,		local_sgd_steps=__UpperCamelCase	,		enabled=local_sgd_steps is not None					) as local_sgd:
                  for step, batch in enumerate(__UpperCamelCase					):
                        # 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(__UpperCamelCase					):
                              UpperCamelCase       						=      model(**__UpperCamelCase					)
                              UpperCamelCase       						=      output.loss
                              accelerator.backward(__UpperCamelCase					)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

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

            UpperCamelCase       						=      metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"epoch {epoch}:"	,		__UpperCamelCase					)







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      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."""	,		)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,		type=__UpperCamelCase	,		default=1	,		help="""The number of minibatches to be ran before gradients are accumulated."""	,		)
      parser.add_argument(
          """--local_sgd_steps"""	,		type=__UpperCamelCase	,		default=8	,		help="""Number of local SGD steps or None to disable local SGD"""					)
      parser.add_argument("""--cpu"""	,		action="""store_true"""	,		help="""If passed, will train on the CPU."""					)
      UpperCamelCase       						=      parser.parse_args()
      UpperCamelCase       						=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
     main()
