


'''simple docstring'''




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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    						=16
__snake_case    						=32





def 	a_					( lowerCamelCase	:     Accelerator	, lowerCamelCase	:     DatasetDict	, lowerCamelCase	:     List[int]	, lowerCamelCase	:     List[int]	, lowerCamelCase	:     int = 16				):
   lowerCAmelCase     =			AutoTokenizer.from_pretrained('bert-base-cased'				)
   lowerCAmelCase     =			DatasetDict(
       {
           'train': dataset['train'].select(lowerCamelCase				),
           'validation': dataset['train'].select(lowerCamelCase				),
           'test': dataset['validation'],
       }				)

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

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

      return tokenizer.pad(
          lowerCamelCase	, padding='longest'	, max_length=lowerCamelCase	, pad_to_multiple_of=lowerCamelCase	, return_tensors='pt'	, )

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

   lowerCAmelCase     =			DataLoader(
       tokenized_datasets['test']	, shuffle=lowerCamelCase	, collate_fn=lowerCamelCase	, batch_size=lowerCamelCase				)

   return train_dataloader, eval_dataloader, test_dataloader





def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     List[Any]				):
   # New Code #
   lowerCAmelCase     =			[]
   # Download the dataset
   lowerCAmelCase     =			load_dataset('glue'	, 'mrpc'				)
   # Create our splits
   lowerCAmelCase     =			StratifiedKFold(n_splits=int(args.num_folds				)				)
   # Initialize accelerator
   lowerCAmelCase     =			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase     =			config['lr']
   lowerCAmelCase     =			int(config['num_epochs']				)
   lowerCAmelCase     =			int(config['seed']				)
   lowerCAmelCase     =			int(config['batch_size']				)

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

   # If the batch size is too big we use gradient accumulation
   lowerCAmelCase     =			1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowerCAmelCase     =			batch_size // MAX_GPU_BATCH_SIZE
      lowerCAmelCase     =			MAX_GPU_BATCH_SIZE

   set_seed(lowerCamelCase				)

   # New Code #
   # Create our folds:
   lowerCAmelCase     =			kfold.split(np.zeros(datasets['train'].num_rows				)	, datasets['train']['label']				)
   lowerCAmelCase     =			[]
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(lowerCamelCase				):
      lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			get_fold_dataloaders(
          lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase     =			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).
      lowerCAmelCase     =			model.to(accelerator.device				)

      # Instantiate optimizer
      lowerCAmelCase     =			AdamW(params=model.parameters()	, lr=lowerCamelCase				)

      # Instantiate scheduler
      lowerCAmelCase     =			get_linear_schedule_with_warmup(
          optimizer=lowerCamelCase	, num_warmup_steps=100	, num_training_steps=(len(lowerCamelCase				) * num_epochs) // gradient_accumulation_steps	, )

      # 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.
      lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			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				)
            lowerCAmelCase     =			model(**lowerCamelCase				)
            lowerCAmelCase     =			outputs.loss
            lowerCAmelCase     =			loss / gradient_accumulation_steps
            accelerator.backward(lowerCamelCase				)
            if step % gradient_accumulation_steps == 0:
               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():
               lowerCAmelCase     =			model(**lowerCamelCase				)
            lowerCAmelCase     =			outputs.logits.argmax(dim=-1				)
            lowerCAmelCase     ,  lowerCAmelCase     =			accelerator.gather_for_metrics((predictions, batch['labels'])				)
            metric.add_batch(
                predictions=lowerCamelCase	, references=lowerCamelCase	, )

         lowerCAmelCase     =			metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(f'''epoch {epoch}:'''	, lowerCamelCase				)

      # New Code #
      # We also run predictions on the test set at the very end
      lowerCAmelCase     =			[]
      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():
            lowerCAmelCase     =			model(**lowerCamelCase				)
         lowerCAmelCase     =			outputs.logits
         lowerCAmelCase     ,  lowerCAmelCase     =			accelerator.gather_for_metrics((predictions, batch['labels'])				)
         fold_predictions.append(predictions.cpu()				)
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu()				)
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(lowerCamelCase	, dim=0				)				)
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   lowerCAmelCase     =			torch.cat(lowerCamelCase	, dim=0				)
   lowerCAmelCase     =			torch.stack(lowerCamelCase	, dim=0				).sum(dim=0				).div(int(args.num_folds				)				).argmax(dim=-1				)
   lowerCAmelCase     =			metric.compute(predictions=lowerCamelCase	, references=lowerCamelCase				)
   accelerator.print('Average test metrics from all folds:'	, lowerCamelCase				)





def 	a_					( ):
   lowerCAmelCase     =			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.'	, )
   parser.add_argument('--cpu'	, action='store_true'	, help='If passed, will train on the CPU.'				)
   # New Code #
   parser.add_argument('--num_folds'	, type=lowerCamelCase	, default=3	, help='The number of splits to perform across the dataset'				)
   lowerCAmelCase     =			parser.parse_args()
   lowerCAmelCase     =			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCamelCase	, lowerCamelCase				)


if __name__ == "__main__":
       main()









'''simple docstring'''




import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case    						=logging.get_logger(__name__)





def 	a_					( lowerCamelCase	:     Any				):
   lowerCAmelCase     =			OrderedDict()
   for key, value in state_dict.items():
      if key.startswith('module.encoder'				):
         lowerCAmelCase     =			key.replace('module.encoder'	, 'glpn.encoder'				)
      if key.startswith('module.decoder'				):
         lowerCAmelCase     =			key.replace('module.decoder'	, 'decoder.stages'				)
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         lowerCAmelCase     =			key[key.find('patch_embed'				) + len('patch_embed'				)]
         lowerCAmelCase     =			key.replace(f'''patch_embed{idx}'''	, f'''patch_embeddings.{int(lowerCamelCase				)-1}'''				)
      if "norm" in key:
         lowerCAmelCase     =			key.replace('norm'	, 'layer_norm'				)
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         lowerCAmelCase     =			key[key.find('glpn.encoder.layer_norm'				) + len('glpn.encoder.layer_norm'				)]
         lowerCAmelCase     =			key.replace(f'''layer_norm{idx}'''	, f'''layer_norm.{int(lowerCamelCase				)-1}'''				)
      if "layer_norm1" in key:
         lowerCAmelCase     =			key.replace('layer_norm1'	, 'layer_norm_1'				)
      if "layer_norm2" in key:
         lowerCAmelCase     =			key.replace('layer_norm2'	, 'layer_norm_2'				)
      if "block" in key:
         # replace for example block1 by block.0
         lowerCAmelCase     =			key[key.find('block'				) + len('block'				)]
         lowerCAmelCase     =			key.replace(f'''block{idx}'''	, f'''block.{int(lowerCamelCase				)-1}'''				)
      if "attn.q" in key:
         lowerCAmelCase     =			key.replace('attn.q'	, 'attention.self.query'				)
      if "attn.proj" in key:
         lowerCAmelCase     =			key.replace('attn.proj'	, 'attention.output.dense'				)
      if "attn" in key:
         lowerCAmelCase     =			key.replace('attn'	, 'attention.self'				)
      if "fc1" in key:
         lowerCAmelCase     =			key.replace('fc1'	, 'dense1'				)
      if "fc2" in key:
         lowerCAmelCase     =			key.replace('fc2'	, 'dense2'				)
      if "linear_pred" in key:
         lowerCAmelCase     =			key.replace('linear_pred'	, 'classifier'				)
      if "linear_fuse" in key:
         lowerCAmelCase     =			key.replace('linear_fuse.conv'	, 'linear_fuse'				)
         lowerCAmelCase     =			key.replace('linear_fuse.bn'	, 'batch_norm'				)
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         lowerCAmelCase     =			key[key.find('linear_c'				) + len('linear_c'				)]
         lowerCAmelCase     =			key.replace(f'''linear_c{idx}'''	, f'''linear_c.{int(lowerCamelCase				)-1}'''				)
      if "bot_conv" in key:
         lowerCAmelCase     =			key.replace('bot_conv'	, '0.convolution'				)
      if "skip_conv1" in key:
         lowerCAmelCase     =			key.replace('skip_conv1'	, '1.convolution'				)
      if "skip_conv2" in key:
         lowerCAmelCase     =			key.replace('skip_conv2'	, '2.convolution'				)
      if "fusion1" in key:
         lowerCAmelCase     =			key.replace('fusion1'	, '1.fusion'				)
      if "fusion2" in key:
         lowerCAmelCase     =			key.replace('fusion2'	, '2.fusion'				)
      if "fusion3" in key:
         lowerCAmelCase     =			key.replace('fusion3'	, '3.fusion'				)
      if "fusion" in key and "conv" in key:
         lowerCAmelCase     =			key.replace('conv'	, 'convolutional_layer'				)
      if key.startswith('module.last_layer_depth'				):
         lowerCAmelCase     =			key.replace('module.last_layer_depth'	, 'head.head'				)
      lowerCAmelCase     =			value

   return new_state_dict





def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     str				):
   # for each of the encoder blocks:
   for i in range(config.num_encoder_blocks				):
      for j in range(config.depths[i]				):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         lowerCAmelCase     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
         lowerCAmelCase     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''				)
         # next, add keys and values (in that order) to the state dict
         lowerCAmelCase     =			kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCAmelCase     =			kv_bias[: config.hidden_sizes[i]]
         lowerCAmelCase     =			kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCAmelCase     =			kv_bias[config.hidden_sizes[i] :]





def 	a_					( ):
   lowerCAmelCase     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)

   return image





@torch.no_grad()
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]=False	, lowerCamelCase	:     List[str]=None				):
   lowerCAmelCase     =			GLPNConfig(hidden_sizes=[64, 128, 320, 512]	, decoder_hidden_size=64	, depths=[3, 8, 27, 3]				)

   # load image processor (only resize + rescale)
   lowerCAmelCase     =			GLPNImageProcessor()

   # prepare image
   lowerCAmelCase     =			prepare_img()
   lowerCAmelCase     =			image_processor(images=lowerCamelCase	, return_tensors='pt'				).pixel_values

   logger.info('Converting model...'				)

   # load original state dict
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location=torch.device('cpu'				)				)

   # rename keys
   lowerCAmelCase     =			rename_keys(lowerCamelCase				)

   # key and value matrices need special treatment
   read_in_k_v(lowerCamelCase	, lowerCamelCase				)

   # create HuggingFace model and load state dict
   lowerCAmelCase     =			GLPNForDepthEstimation(lowerCamelCase				)
   model.load_state_dict(lowerCamelCase				)
   model.eval()

   # forward pass
   lowerCAmelCase     =			model(lowerCamelCase				)
   lowerCAmelCase     =			outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCAmelCase     =			torch.tensor(
             [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]				)
      elif "kitti" in model_name:
         lowerCAmelCase     =			torch.tensor(
             [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]				)
      else:
         raise ValueError(f'''Unknown model name: {model_name}'''				)

      lowerCAmelCase     =			torch.Size([1, 480, 640]				)

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3]	, lowerCamelCase	, atol=1e-4				)
      print('Looks ok!'				)

   # finally, push to hub if required
   if push_to_hub:
      logger.info('Pushing model and image processor to the hub...'				)
      model.push_to_hub(
          repo_path_or_name=Path(lowerCamelCase	, lowerCamelCase				)	, organization='nielsr'	, commit_message='Add model'	, use_temp_dir=lowerCamelCase	, )
      image_processor.push_to_hub(
          repo_path_or_name=Path(lowerCamelCase	, lowerCamelCase				)	, organization='nielsr'	, commit_message='Add image processor'	, use_temp_dir=lowerCamelCase	, )


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()

       parser.add_argument(
           """--checkpoint_path""",
           default=None,
           type=str,
           help="""Path to the original PyTorch checkpoint (.pth file).""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
       )
       parser.add_argument(
           """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
       )
       parser.add_argument(
           """--model_name""",
           default="""glpn-kitti""",
           type=str,
           help="""Name of the model in case you're pushing to the hub.""",
       )
       __snake_case    						=parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





