





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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    return (preds == labels).mean()



@dataclass
class 					__snake_case	:


    '''simple docstring'''







    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 					__snake_case	:


    '''simple docstring'''







    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=1_2_8			,     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 							__snake_case							(    )				->						Tuple:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    SCREAMING_SNAKE_CASE__											=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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''' ,							lowerCAmelCase_	)

    # Set seed
    set_seed(training_args.seed	)

    try:
        SCREAMING_SNAKE_CASE__											=			processors[data_args.task_name]()
        SCREAMING_SNAKE_CASE__											=			processor.get_labels()
        SCREAMING_SNAKE_CASE__											=			len(lowerCAmelCase_	)
    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.

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

    # Get datasets
    SCREAMING_SNAKE_CASE__											=			(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir ,							tokenizer=lowerCAmelCase_ ,							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
    )
    SCREAMING_SNAKE_CASE__											=			(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir ,							tokenizer=lowerCAmelCase_ ,							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(lowerCAmelCase_	) -> Dict:
        SCREAMING_SNAKE_CASE__											=			np.argmax(p.predictions ,							axis=1	)
        return {"acc": simple_accuracy(lowerCAmelCase_ ,							p.label_ids	)}

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

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE__											=			Trainer(
        model=lowerCAmelCase_ ,							args=lowerCAmelCase_ ,							train_dataset=lowerCAmelCase_ ,							eval_dataset=lowerCAmelCase_ ,							compute_metrics=lowerCAmelCase_ ,							data_collator=lowerCAmelCase_ ,							)

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

        SCREAMING_SNAKE_CASE__											=			trainer.evaluate()

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

                results.update(lowerCAmelCase_	)

    return results


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()






'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_      :  Optional[int]				     =  logging.get_logger(__name__)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							List[Any]     						=  DPTConfig(embedding_type="""hybrid"""				)

     if "large" in checkpoint_url:
          a_     :							str     						=  1024
          a_     :							List[str]     						=  4096
          a_     :							int     						=  24
          a_     :							int     						=  16
          a_     :							Optional[Any]     						=  [5, 11, 17, 23]
          a_     :							Optional[int]     						=  [256, 512, 1024, 1024]
          a_     :							Union[str, Any]     						=  (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          a_     :							Dict     						=  768
          a_     :							str     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  [256, 512, 768, 768]
          a_     :							int     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  (1, 384, 384)
          a_     :							Optional[Any]     						=  False
          a_     :							Dict     						=  """project"""

     if "ade" in checkpoint_url:
          a_     :							int     						=  True
          a_     :							int     						=  768
          a_     :							Optional[int]     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  """huggingface/label-files"""
          a_     :							Optional[int]     						=  """ade20k-id2label.json"""
          a_     :							Union[str, Any]     						=  json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,    UpperCamelCase__ ,    repo_type="""dataset"""				)				) ,    """r"""				)				)
          a_     :							str     						=  {int(UpperCamelCase__				): v for k, v in idalabel.items()}
          a_     :							Optional[int]     						=  idalabel
          a_     :							Optional[Any]     						=  {v: k for k, v in idalabel.items()}
          a_     :							int     						=  [1, 150, 480, 480]

     return config, expected_shape



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							Dict     						=  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str				):




     """simple docstring"""


     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          a_     :							List[str]     						=  name.replace("""pretrained.model""" ,    """dpt.encoder"""				)
     if "pretrained.model" in name:
          a_     :							Union[str, Any]     						=  name.replace("""pretrained.model""" ,    """dpt.embeddings"""				)
     if "patch_embed" in name:
          a_     :							Optional[int]     						=  name.replace("""patch_embed""" ,    """"""				)
     if "pos_embed" in name:
          a_     :							List[str]     						=  name.replace("""pos_embed""" ,    """position_embeddings"""				)
     if "attn.proj" in name:
          a_     :							Any     						=  name.replace("""attn.proj""" ,    """attention.output.dense"""				)
     if "proj" in name and "project" not in name:
          a_     :							Dict     						=  name.replace("""proj""" ,    """projection"""				)
     if "blocks" in name:
          a_     :							Union[str, Any]     						=  name.replace("""blocks""" ,    """layer"""				)
     if "mlp.fc1" in name:
          a_     :							Optional[int]     						=  name.replace("""mlp.fc1""" ,    """intermediate.dense"""				)
     if "mlp.fc2" in name:
          a_     :							Any     						=  name.replace("""mlp.fc2""" ,    """output.dense"""				)
     if "norm1" in name and "backbone" not in name:
          a_     :							List[Any]     						=  name.replace("""norm1""" ,    """layernorm_before"""				)
     if "norm2" in name and "backbone" not in name:
          a_     :							int     						=  name.replace("""norm2""" ,    """layernorm_after"""				)
     if "scratch.output_conv" in name:
          a_     :							Tuple     						=  name.replace("""scratch.output_conv""" ,    """head"""				)
     if "scratch" in name:
          a_     :							str     						=  name.replace("""scratch""" ,    """neck"""				)
     if "layer1_rn" in name:
          a_     :							Optional[Any]     						=  name.replace("""layer1_rn""" ,    """convs.0"""				)
     if "layer2_rn" in name:
          a_     :							Dict     						=  name.replace("""layer2_rn""" ,    """convs.1"""				)
     if "layer3_rn" in name:
          a_     :							Tuple     						=  name.replace("""layer3_rn""" ,    """convs.2"""				)
     if "layer4_rn" in name:
          a_     :							str     						=  name.replace("""layer4_rn""" ,    """convs.3"""				)
     if "refinenet" in name:
          a_     :							List[Any]     						=  int(name[len("""neck.refinenet"""				) : len("""neck.refinenet"""				) + 1]				)
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          a_     :							List[str]     						=  name.replace(F"refinenet{layer_idx}" ,    F"fusion_stage.layers.{abs(layer_idx-4				)}"				)
     if "out_conv" in name:
          a_     :							Union[str, Any]     						=  name.replace("""out_conv""" ,    """projection"""				)
     if "resConfUnit1" in name:
          a_     :							Any     						=  name.replace("""resConfUnit1""" ,    """residual_layer1"""				)
     if "resConfUnit2" in name:
          a_     :							List[str]     						=  name.replace("""resConfUnit2""" ,    """residual_layer2"""				)
     if "conv1" in name:
          a_     :							str     						=  name.replace("""conv1""" ,    """convolution1"""				)
     if "conv2" in name:
          a_     :							Dict     						=  name.replace("""conv2""" ,    """convolution2"""				)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          a_     :							str     						=  name.replace("""pretrained.act_postprocess1.0.project.0""" ,    """neck.reassemble_stage.readout_projects.0.0"""				)
     if "pretrained.act_postprocess2.0.project.0" in name:
          a_     :							int     						=  name.replace("""pretrained.act_postprocess2.0.project.0""" ,    """neck.reassemble_stage.readout_projects.1.0"""				)
     if "pretrained.act_postprocess3.0.project.0" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess3.0.project.0""" ,    """neck.reassemble_stage.readout_projects.2.0"""				)
     if "pretrained.act_postprocess4.0.project.0" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess4.0.project.0""" ,    """neck.reassemble_stage.readout_projects.3.0"""				)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess1.3""" ,    """neck.reassemble_stage.layers.0.projection"""				)
     if "pretrained.act_postprocess1.4" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess1.4""" ,    """neck.reassemble_stage.layers.0.resize"""				)
     if "pretrained.act_postprocess2.3" in name:
          a_     :							str     						=  name.replace("""pretrained.act_postprocess2.3""" ,    """neck.reassemble_stage.layers.1.projection"""				)
     if "pretrained.act_postprocess2.4" in name:
          a_     :							Optional[int]     						=  name.replace("""pretrained.act_postprocess2.4""" ,    """neck.reassemble_stage.layers.1.resize"""				)
     if "pretrained.act_postprocess3.3" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess3.3""" ,    """neck.reassemble_stage.layers.2.projection"""				)
     if "pretrained.act_postprocess4.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess4.3""" ,    """neck.reassemble_stage.layers.3.projection"""				)
     if "pretrained.act_postprocess4.4" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess4.4""" ,    """neck.reassemble_stage.layers.3.resize"""				)
     if "pretrained" in name:
          a_     :							int     						=  name.replace("""pretrained""" ,    """dpt"""				)
     if "bn" in name:
          a_     :							Any     						=  name.replace("""bn""" ,    """batch_norm"""				)
     if "head" in name:
          a_     :							int     						=  name.replace("""head""" ,    """head.head"""				)
     if "encoder.norm" in name:
          a_     :							Any     						=  name.replace("""encoder.norm""" ,    """layernorm"""				)
     if "auxlayer" in name:
          a_     :							Union[str, Any]     						=  name.replace("""auxlayer""" ,    """auxiliary_head.head"""				)
     if "backbone" in name:
          a_     :							Any     						=  name.replace("""backbone""" ,    """backbone.bit.encoder"""				)

     if ".." in name:
          a_     :							Any     						=  name.replace("""..""" ,    """."""				)

     if "stem.conv" in name:
          a_     :							Optional[int]     						=  name.replace("""stem.conv""" ,    """bit.embedder.convolution"""				)
     if "blocks" in name:
          a_     :							Dict     						=  name.replace("""blocks""" ,    """layers"""				)
     if "convolution" in name and "backbone" in name:
          a_     :							str     						=  name.replace("""convolution""" ,    """conv"""				)
     if "layer" in name and "backbone" in name:
          a_     :							Any     						=  name.replace("""layer""" ,    """layers"""				)
     if "backbone.bit.encoder.bit" in name:
          a_     :							Dict     						=  name.replace("""backbone.bit.encoder.bit""" ,    """backbone.bit"""				)
     if "embedder.conv" in name:
          a_     :							List[str]     						=  name.replace("""embedder.conv""" ,    """embedder.convolution"""				)
     if "backbone.bit.encoder.stem.norm" in name:
          a_     :							int     						=  name.replace("""backbone.bit.encoder.stem.norm""" ,    """backbone.bit.embedder.norm"""				)
     return name



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     for i in range(config.num_hidden_layers				):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          a_     :							List[str]     						=  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"				)
          a_     :							Optional[Any]     						=  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"				)
          # next, add query, keys and values (in that order) to the state dict
          a_     :							Any     						=  in_proj_weight[: config.hidden_size, :]
          a_     :							int     						=  in_proj_bias[: config.hidden_size]
          a_     :							List[Any]     						=  in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          a_     :							int     						=  in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          a_     :							Union[str, Any]     						=  in_proj_weight[
              -config.hidden_size :, :
          ]
          a_     :							str     						=  in_proj_bias[-config.hidden_size :]



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Optional[Any]     						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :							Union[str, Any]     						=  Image.open(requests.get(UpperCamelCase__ ,    stream=UpperCamelCase__				).raw				)
     return im



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_						,     a_     :							Optional[Any]     						=  get_dpt_config(UpperCamelCase__				)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     a_     :							int     						=  torch.load(UpperCamelCase__ ,    map_location="""cpu"""				)
     # remove certain keys
     remove_ignore_keys_(UpperCamelCase__				)
     # rename keys
     for key in state_dict.copy().keys():
          a_     :							Any     						=  state_dict.pop(UpperCamelCase__				)
          a_     :							Tuple     						=  val
     # read in qkv matrices
     read_in_q_k_v(UpperCamelCase__ ,    UpperCamelCase__				)

     # load HuggingFace model
     a_     :							Any     						=  DPTForSemanticSegmentation(UpperCamelCase__				) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__				)
     model.load_state_dict(UpperCamelCase__				)
     model.eval()

     # Check outputs on an image
     a_     :							str     						=  480 if """ade""" in checkpoint_url else 384
     a_     :							Optional[int]     						=  DPTImageProcessor(size=UpperCamelCase__				)

     a_     :							Dict     						=  prepare_img()
     a_     :							Any     						=  image_processor(UpperCamelCase__ ,    return_tensors="""pt"""				)

     # forward pass
     a_     :							Dict     						=  model(**UpperCamelCase__				).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__				).predicted_depth

     if show_prediction:
          a_     :							Any     						=  (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				) ,    size=(image.size[1], image.size[0]) ,    mode="""bicubic""" ,    align_corners=UpperCamelCase__ ,    )
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255				).show()

     if pytorch_dump_folder_path is not None:
          Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
          print(F"Saving model to {pytorch_dump_folder_path}"				)
          model.save_pretrained(UpperCamelCase__				)
          print(F"Saving image processor to {pytorch_dump_folder_path}"				)
          image_processor.save_pretrained(UpperCamelCase__				)

     if push_to_hub:
          model.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)
          image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)


if __name__ == "__main__":
 lowerCAmelCase_      :  Dict				     =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
     type=str,
     help='URL of the original DPT checkpoint you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     required=False,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
 )
 parser.add_argument(
     '--model_name',
     default='dpt-large',
     type=str,
     help='Name of the model, in case you\'re pushing to the hub.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

 lowerCAmelCase_      :  Optional[Any]				     =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )
