





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a__      :     str         =		logging.getLogger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:Optional[int]				)	->				Optional[Any]:
     '''simple docstring'''


     return (preds == labels).mean()





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}     ,		)





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
  __lowerCAmelCase   :						str							=   field(metadata={"""help""": """Should contain the data files for the task."""} )
  __lowerCAmelCase   :						int							=   field(
      default=128     ,		metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }     ,		)
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )




def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     A_   ,    A_   ,    A_          =       parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir				)
         and os.listdir(training_args.output_dir				)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
              ''' --overwrite_output_dir to overcome.'''				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,						datefmt='''%m/%d/%Y %H:%M:%S'''  ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,						)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,						training_args.local_rank  ,						training_args.device  ,						training_args.n_gpu  ,						bool(training_args.local_rank != -1				)  ,						training_args.fpaa  ,						)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank				):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s'''  ,						_UpperCAmelCase				)

     # Set seed
     set_seed(training_args.seed				)

     try:
          A_          =       processors[data_args.task_name]()
          A_          =       processor.get_labels()
          A_          =       len(_UpperCAmelCase				)
     except KeyError:
          raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     A_          =       AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,						num_labels=_UpperCAmelCase  ,						finetuning_task=data_args.task_name  ,						cache_dir=model_args.cache_dir  ,						)
     A_          =       AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,						cache_dir=model_args.cache_dir  ,						)
     A_          =       AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path  ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)  ,						config=_UpperCAmelCase  ,						cache_dir=model_args.cache_dir  ,						)

     # Get datasets
     A_          =       (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir  ,						tokenizer=_UpperCAmelCase  ,						task=data_args.task_name  ,						max_seq_length=data_args.max_seq_length  ,						overwrite_cache=data_args.overwrite_cache  ,						mode=Split.train  ,						)
         if training_args.do_train
         else None
     )
     A_          =       (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir  ,						tokenizer=_UpperCAmelCase  ,						task=data_args.task_name  ,						max_seq_length=data_args.max_seq_length  ,						overwrite_cache=data_args.overwrite_cache  ,						mode=Split.dev  ,						)
         if training_args.do_eval
         else None
     )

     def compute_metrics(_UpperCAmelCase							:EvalPrediction				) -> Dict:
          A_          =       np.argmax(p.predictions  ,						axis=1				)
          return {"acc": simple_accuracy(_UpperCAmelCase  ,						p.label_ids				)}

     # Data collator
     A_          =       DataCollatorWithPadding(_UpperCAmelCase  ,						pad_to_multiple_of=8				) if training_args.fpaa else None

     # Initialize our Trainer
     A_          =       Trainer(
         model=_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						train_dataset=_UpperCAmelCase  ,						eval_dataset=_UpperCAmelCase  ,						compute_metrics=_UpperCAmelCase  ,						data_collator=_UpperCAmelCase  ,						)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
     A_          =       {}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''				)

          A_          =       trainer.evaluate()

          A_          =       os.path.join(training_args.output_dir  ,						'''eval_results.txt'''				)
          if trainer.is_world_master():
               with open(_UpperCAmelCase  ,						'''w'''				) as writer:
                    logger.info('''***** Eval results *****'''				)
                    for key, value in result.items():
                         logger.info('''  %s = %s'''  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
                         writer.write('''%s = %s\n''' % (key, value)				)

                    results.update(_UpperCAmelCase				)

     return results

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[Any]				)	->				Optional[Any]:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()










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()
a__      :     Any         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[int]:
     '''simple docstring'''


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

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Tuple:
     '''simple docstring'''


     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)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       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
               A_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[config.hidden_sizes[i] :]

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]=False  ,						_UpperCAmelCase							:Any=None				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,						decoder_hidden_size=64  ,						depths=[3, 8, 27, 3]				)

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

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

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

     # load original state dict
     A_          =       torch.load(_UpperCAmelCase  ,						map_location=torch.device('''cpu'''				)				)

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase				)

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

     # create HuggingFace model and load state dict
     A_          =       GLPNForDepthEstimation(_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase				)
     model.eval()

     # forward pass
     A_          =       model(_UpperCAmelCase				)
     A_          =       outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               A_          =       torch.tensor(
                   [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]				)
          elif "kitti" in model_name:
               A_          =       torch.tensor(
                   [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]				)
          else:
               raise ValueError(f'Unknown model name: {model_name}'				)

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

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]  ,						_UpperCAmelCase  ,						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(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add model'''  ,						use_temp_dir=_UpperCAmelCase  ,						)
          image_processor.push_to_hub(
              repo_path_or_name=Path(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add image processor'''  ,						use_temp_dir=_UpperCAmelCase  ,						)


if __name__ == "__main__":
      a__      :     Dict         =		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.',
      )
      a__      :     int         =		parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



