






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

snake_case__   :						Union[str, Any]													=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__     )
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Any:
     super().__init__(*_UpperCAmelCase		,		**_UpperCAmelCase			)
     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		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None			)      ->			List[str]:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  {}

     if prompt is not None:
        UpperCamelCase_						=  prompt

     if generate_kwargs is not None:
        UpperCamelCase_						=  generate_kwargs
     if max_new_tokens is not None:
        if "generate_kwargs" not in forward_kwargs:
           UpperCamelCase_						=  {}
        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'			)
        UpperCamelCase_						=  max_new_tokens
     return preprocess_params, forward_kwargs, {}




  def __call__(  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			int:
     return super().__call__(_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			str:
     UpperCamelCase_						=  load_image(_UpperCAmelCase			)

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

        UpperCamelCase_						=  self.model.config.model_type

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

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

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

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

     else:
        UpperCamelCase_						=  self.image_processor(images=_UpperCAmelCase		,		return_tensors=self.framework			)

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

     return model_inputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			Any:
     # 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']		,		_UpperCAmelCase			)
         and all(x is None for x in model_inputs['input_ids']			)
     ):
        UpperCamelCase_						=  None

     if generate_kwargs is None:
        UpperCamelCase_						=  {}
     # 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.
     UpperCamelCase_						=  model_inputs.pop(self.model.main_input_name			)
     UpperCamelCase_						=  self.model.generate(_UpperCAmelCase		,		**_UpperCAmelCase		,		**_UpperCAmelCase			)
     return model_outputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  []
     for output_ids in model_outputs:
        UpperCamelCase_						=  {
            'generated_text': self.tokenizer.decode(
                _UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase		,		)
        }
        records.append(_UpperCAmelCase			)
     return records













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
#
########################################################################


snake_case__   :						Dict													=   1_6
snake_case__   :						List[str]													=   3_2






def      _snake_case     (__lowercase     ,   __lowercase = 16):
   UpperCamelCase_						=  AutoTokenizer.from_pretrained('bert-base-cased')
   UpperCamelCase_						=  load_dataset('glue'     ,   'mrpc')

   def tokenize_function(__lowercase):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase_						=  tokenizer(examples['sentence1']     ,   examples['sentence2']     ,   truncation=__lowercase     ,   max_length=__lowercase)
      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(
          __lowercase     ,   batched=__lowercase     ,   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(__lowercase):
      # 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(
          __lowercase     ,   padding='longest'     ,   max_length=__lowercase     ,   pad_to_multiple_of=__lowercase     ,   return_tensors='pt'     ,   )

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

   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

 snake_case__   :						List[str]													=   mocked_dataloaders  # noqa: F811






def      _snake_case     (__lowercase     ,   __lowercase):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,   __lowercase) == "1":
      UpperCamelCase_						=  2
   # New Code #
   UpperCamelCase_						=  int(args.gradient_accumulation_steps)
   # Initialize accelerator
   UpperCamelCase_						=  Accelerator(
       cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=__lowercase)
   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
   UpperCamelCase_						=  config['lr']
   UpperCamelCase_						=  int(config['num_epochs'])
   UpperCamelCase_						=  int(config['seed'])
   UpperCamelCase_						=  int(config['batch_size'])

   UpperCamelCase_						=  evaluate.load('glue'     ,   'mrpc')

   set_seed(__lowercase)
   UpperCamelCase_		,   UpperCamelCase_						=  get_dataloaders(__lowercase     ,   __lowercase)
   # 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=__lowercase)

   # 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=__lowercase)

   # Instantiate scheduler
   UpperCamelCase_						=  get_linear_schedule_with_warmup(
       optimizer=__lowercase     ,   num_warmup_steps=100     ,   num_training_steps=(len(__lowercase) * 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(
       __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase)

   # Now we train the model
   for epoch in range(__lowercase):
      model.train()
      for step, batch in enumerate(__lowercase):
         # 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(__lowercase):
            UpperCamelCase_						=  model(**__lowercase)
            UpperCamelCase_						=  output.loss
            accelerator.backward(__lowercase)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(__lowercase):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         with torch.no_grad():
            UpperCamelCase_						=  model(**__lowercase)
         UpperCamelCase_						=  outputs.logits.argmax(dim=-1)
         UpperCamelCase_		,   UpperCamelCase_						=  accelerator.gather_for_metrics((predictions, batch['labels']))
         metric.add_batch(
             predictions=__lowercase     ,   references=__lowercase     ,   )

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






def      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Simple example of training script.')
   parser.add_argument(
       '--mixed_precision'     ,   type=__lowercase     ,   default=__lowercase     ,   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=__lowercase     ,   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.')
   UpperCamelCase_						=  parser.parse_args()
   UpperCamelCase_						=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(__lowercase     ,   __lowercase)


if __name__ == "__main__":
 main()





