'''simple docstring'''

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__a:			int	       =       logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__a:			Optional[int]	       =       {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__a:			int	       =       {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__a:			Optional[Any]	       =       sorted(arg_to_scheduler.keys())
__a:			List[str]	       =       '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class       UpperCAmelCase  (						pl.LightningModule ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="base"     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase     ,       )  ->  Dict:
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowerCAmelCase__			)
        lowercase__    :   List[Any]						= 0
        lowercase__    :   str						= Path(self.hparams.output_dir			)
        lowercase__    :   str						= self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
         lowercase__    :   Any						= AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,       **({'''num_labels''': num_labels} if num_labels is not None else {})     ,       cache_dir=lowerCAmelCase__     ,       **lowerCAmelCase__     ,       )
        else:
         lowercase__    :   PretrainedConfig						= config

        lowercase__    :   Tuple						= ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
        for p in extra_model_params:
         if getattr(self.hparams     ,       lowerCAmelCase__     ,       lowerCAmelCase__			):
          assert hasattr(self.config     ,       lowerCAmelCase__			), F"""model config doesn't have a `{p}` attribute"""
          setattr(self.config     ,       lowerCAmelCase__     ,       getattr(self.hparams     ,       lowerCAmelCase__			)			)

        if tokenizer is None:
         lowercase__    :   Union[str, Any]						= AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,       cache_dir=lowerCAmelCase__     ,       )
        else:
         lowercase__    :   PreTrainedTokenizer						= tokenizer
        lowercase__    :   Any						= MODEL_MODES[mode]
        if model is None:
         lowercase__    :   int						= self.model_type.from_pretrained(
             self.hparams.model_name_or_path     ,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path			)     ,       config=self.config     ,       cache_dir=lowerCAmelCase__     ,       )
        else:
         lowercase__    :   List[str]						= model

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   str						= self.model_type.from_pretrained(*lowerCAmelCase__     ,       **lowerCAmelCase__			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Any						= arg_to_scheduler[self.hparams.lr_scheduler]
        lowercase__    :   Optional[Any]						= get_schedule_func(
            self.opt     ,       num_warmup_steps=self.hparams.warmup_steps     ,       num_training_steps=self.total_steps()			)
        lowercase__    :   List[Any]						= {"scheduler": scheduler, "interval": "step", "frequency": 1}
        return scheduler

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   List[str]						= self.model
        lowercase__    :   Dict						= ["bias", "LayerNorm.weight"]
        lowercase__    :   List[str]						= [
            {
                "params": [
                    p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)
                ],  # check this named paramters
                "weight_decay": self.hparams.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)],
                "weight_decay": 0.0,
            },
        ]
        if self.hparams.adafactor:
         lowercase__    :   Dict						= Adafactor(
             lowerCAmelCase__     ,       lr=self.hparams.learning_rate     ,       scale_parameter=lowerCAmelCase__     ,       relative_step=lowerCAmelCase__			)

        else:
         lowercase__    :   Optional[int]						= AdamW(
             lowerCAmelCase__     ,       lr=self.hparams.learning_rate     ,       eps=self.hparams.adam_epsilon			)
        lowercase__    :   Optional[Any]						= optimizer

        lowercase__    :   Union[str, Any]						= self.get_lr_scheduler()

        return [optimizer], [scheduler]

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        return self.validation_step(lowerCAmelCase__     ,       lowerCAmelCase__			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self.validation_end(lowerCAmelCase__			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= max(1     ,       self.hparams.gpus			)  # TODO: consider num_tpu_cores
        lowercase__    :   Union[str, Any]						= self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        if stage == "test":
         lowercase__    :   int						= len(self.test_dataloader().dataset			)
        else:
         lowercase__    :   Optional[Any]						= self.get_dataloader('''train'''     ,       self.hparams.train_batch_size     ,       shuffle=lowerCAmelCase__			)
         lowercase__    :   Union[str, Any]						= len(self.train_dataloader().dataset			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = False			)  ->  List[str]:
        raise NotImplementedError('''You must implement this for your task'''			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        return self.train_loader

       def _lowerCAmelCase(						self			)  ->  Any:
        return self.get_dataloader('''dev'''     ,       self.hparams.eval_batch_size     ,       shuffle=lowerCAmelCase__			)

       def _lowerCAmelCase(						self			)  ->  int:
        return self.get_dataloader('''test'''     ,       self.hparams.eval_batch_size     ,       shuffle=lowerCAmelCase__			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        return os.path.join(
            self.hparams.data_dir     ,       '''cached_{}_{}_{}'''.format(
                lowerCAmelCase__     ,       list(filter(lowerCAmelCase__     ,       self.hparams.model_name_or_path.split('''/'''			)			)			).pop()     ,       str(self.hparams.max_seq_length			)     ,       )     ,       )

       @pl.utilities.rank_zero_only
       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  None:
        lowercase__    :   Tuple						= self.output_dir.joinpath('''best_tfmr'''			)
        lowercase__    :   str						= self.step_count
        self.model.save_pretrained(lowerCAmelCase__			)
        self.tokenizer.save_pretrained(lowerCAmelCase__			)


       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        parser.add_argument(
            '''--model_name_or_path'''     ,       default=lowerCAmelCase__     ,       type=lowerCAmelCase__     ,       required=lowerCAmelCase__     ,       help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,       )
        parser.add_argument(
            '''--config_name'''     ,       default=''''''     ,       type=lowerCAmelCase__     ,       help='''Pretrained config name or path if not the same as model_name'''			)
        parser.add_argument(
            '''--tokenizer_name'''     ,       default=lowerCAmelCase__     ,       type=lowerCAmelCase__     ,       help='''Pretrained tokenizer name or path if not the same as model_name'''     ,       )
        parser.add_argument(
            '''--cache_dir'''     ,       default=str(Path(lowerCAmelCase__			).parent / '''test_run''' / '''cache'''			)     ,       type=lowerCAmelCase__     ,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,       )
        parser.add_argument(
            '''--encoder_layerdrop'''     ,       type=lowerCAmelCase__     ,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,       )
        parser.add_argument(
            '''--decoder_layerdrop'''     ,       type=lowerCAmelCase__     ,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,       )
        parser.add_argument(
            '''--dropout'''     ,       type=lowerCAmelCase__     ,       help='''Dropout probability (Optional). Goes into model.config'''     ,       )
        parser.add_argument(
            '''--attention_dropout'''     ,       type=lowerCAmelCase__     ,       help='''Attention dropout probability (Optional). Goes into model.config'''     ,       )
        parser.add_argument('''--learning_rate'''     ,       default=5E-5     ,       type=lowerCAmelCase__     ,       help='''The initial learning rate for Adam.'''			)
        parser.add_argument(
            '''--lr_scheduler'''     ,       default='''linear'''     ,       choices=lowerCAmelCase__     ,       metavar=lowerCAmelCase__     ,       type=lowerCAmelCase__     ,       help='''Learning rate scheduler'''     ,       )
        parser.add_argument('''--weight_decay'''     ,       default=0.0     ,       type=lowerCAmelCase__     ,       help='''Weight decay if we apply some.'''			)
        parser.add_argument('''--adam_epsilon'''     ,       default=1E-8     ,       type=lowerCAmelCase__     ,       help='''Epsilon for Adam optimizer.'''			)
        parser.add_argument('''--warmup_steps'''     ,       default=0     ,       type=lowerCAmelCase__     ,       help='''Linear warmup over warmup_steps.'''			)
        parser.add_argument('''--num_workers'''     ,       default=4     ,       type=lowerCAmelCase__     ,       help='''kwarg passed to DataLoader'''			)
        parser.add_argument('''--num_train_epochs'''     ,       dest='''max_epochs'''     ,       default=3     ,       type=lowerCAmelCase__			)
        parser.add_argument('''--train_batch_size'''     ,       default=32     ,       type=lowerCAmelCase__			)
        parser.add_argument('''--eval_batch_size'''     ,       default=32     ,       type=lowerCAmelCase__			)
        parser.add_argument('''--adafactor'''     ,       action='''store_true'''			)



class       UpperCAmelCase  (						pl.Callback ):





       '''simple docstring'''



       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
         pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class       UpperCAmelCase  (						pl.Callback ):





       '''simple docstring'''



       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        # print(pl_module.model.rag)
        for name, param in pl_module.model.rag.named_parameters():
         if param.grad is None:
          print(lowerCAmelCase__			)



class       UpperCAmelCase  (						pl.Callback ):





       '''simple docstring'''



       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Any						= trainer.lr_schedulers[0]["scheduler"]
        lowercase__    :   Union[str, Any]						= {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
        pl_module.logger.log_metrics(lowerCAmelCase__			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        rank_zero_info('''***** Validation results *****'''			)
        lowercase__    :   Dict						= trainer.callback_metrics
        # Log results
        for key in sorted(lowerCAmelCase__			):
         if key not in ["log", "progress_bar"]:
          rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__     ,       str(metrics[key]			)			)			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        rank_zero_info('''***** Test results *****'''			)
        lowercase__    :   Any						= trainer.callback_metrics
        # Log and save results to file
        lowercase__    :   Union[str, Any]						= os.path.join(pl_module.hparams.output_dir     ,       '''test_results.txt'''			)
        with open(lowerCAmelCase__     ,       '''w'''			) as writer:
         for key in sorted(lowerCAmelCase__			):
          if key not in ["log", "progress_bar"]:
           rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase__     ,       str(metrics[key]			)			)			)
           writer.write('''{} = {}\n'''.format(lowerCAmelCase__     ,       str(metrics[key]			)			)			)


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 parser.add_argument(
     '''--output_dir'''		,					default=str(Path(_lowercase					).parent / '''test_run''' / '''model_checkpoints'''					)		,					type=_lowercase		,					help='''The output directory where the model predictions and checkpoints will be written.'''		,					)
 parser.add_argument(
     '''--fp16'''		,					action='''store_true'''		,					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''		,					)

 parser.add_argument(
     '''--fp16_opt_level'''		,					type=_lowercase		,					default='''O2'''		,					help=(
         '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
         '''See details at https://nvidia.github.io/apex/amp.html'''
     )		,					)
 parser.add_argument('''--n_tpu_cores'''		,					dest='''tpu_cores'''		,					type=_lowercase					)
 parser.add_argument('''--max_grad_norm'''		,					dest='''gradient_clip_val'''		,					default=1.0		,					type=_lowercase		,					help='''Max gradient norm'''					)
 parser.add_argument('''--do_train'''		,					action='''store_true'''		,					help='''Whether to run training.'''					)
 parser.add_argument('''--do_predict'''		,					action='''store_true'''		,					help='''Whether to run predictions on the test set.'''					)
 parser.add_argument(
     '''--gradient_accumulation_steps'''		,					dest='''accumulate_grad_batches'''		,					type=_lowercase		,					default=1		,					help='''Number of updates steps to accumulate before performing a backward/update pass.'''		,					)
 parser.add_argument('''--seed'''		,					type=_lowercase		,					default=42		,					help='''random seed for initialization'''					)
 parser.add_argument(
     '''--data_dir'''		,					default=str(Path(_lowercase					).parent / '''test_run''' / '''dummy-train-data'''					)		,					type=_lowercase		,					help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''		,					)





def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=None		,					UpperCAmelCase=True		,					UpperCAmelCase=[]		,					UpperCAmelCase=None		,					UpperCAmelCase=None		,					**UpperCAmelCase		,					):
 pl.seed_everything(args.seed					)

 # init model
 lowercase__    :   int						= Path(model.hparams.output_dir					)
 odir.mkdir(exist_ok=_lowercase					)

 # add custom checkpoints
 if checkpoint_callback is None:
  lowercase__    :   Optional[int]						= pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir		,					prefix='''checkpoint'''		,					monitor='''val_loss'''		,					mode='''min'''		,					save_top_k=1					)
 if early_stopping_callback:
  extra_callbacks.append(_lowercase					)
 if logging_callback is None:
  lowercase__    :   str						= LoggingCallback()

 lowercase__    :   Optional[Any]						= {}

 if args.fpaa:
  lowercase__    :   Dict						= 16

 if args.gpus > 1:
  lowercase__    :   Dict						= "auto"
  lowercase__    :   Tuple						= "ddp"

 lowercase__    :   Optional[int]						= args.accumulate_grad_batches
 lowercase__    :   Tuple						= None
 lowercase__    :   str						= "auto"

 lowercase__    :   Union[str, Any]						= pl.Trainer.from_argparse_args(
     _lowercase		,					weights_summary=_lowercase		,					callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,					logger=_lowercase		,					val_check_interval=1		,					num_sanity_val_steps=2		,					**_lowercase		,					)

 if args.do_train:
  trainer.fit(_lowercase					)

 else:
  print('''RAG modeling tests with new set functions successfuly executed!'''					)
 return trainer





'''simple docstring'''

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
  from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor



class       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=2     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=16     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=[0, 1, 2, 3]     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=37     ,       __lowerCAmelCase="gelu"     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.0_2     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=[1, 384, 24, 24]     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=None     ,       )  ->  Dict:
        lowercase__    :   str						= parent
        lowercase__    :   List[Any]						= batch_size
        lowercase__    :   Dict						= image_size
        lowercase__    :   Tuple						= patch_size
        lowercase__    :   str						= num_channels
        lowercase__    :   Dict						= is_training
        lowercase__    :   Optional[int]						= use_labels
        lowercase__    :   List[Any]						= hidden_size
        lowercase__    :   int						= num_hidden_layers
        lowercase__    :   int						= backbone_out_indices
        lowercase__    :   List[str]						= num_attention_heads
        lowercase__    :   Union[str, Any]						= intermediate_size
        lowercase__    :   List[Any]						= hidden_act
        lowercase__    :   str						= hidden_dropout_prob
        lowercase__    :   Tuple						= attention_probs_dropout_prob
        lowercase__    :   List[Any]						= initializer_range
        lowercase__    :   Optional[int]						= num_labels
        lowercase__    :   Optional[int]						= backbone_featmap_shape
        lowercase__    :   int						= scope
        lowercase__    :   List[str]						= is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase__    :   List[str]						= (image_size // patch_size) ** 2
        lowercase__    :   Tuple						= num_patches + 1

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Union[str, Any]						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__    :   int						= None
        if self.use_labels:
         lowercase__    :   Tuple						= ids_tensor([self.batch_size, self.image_size, self.image_size]     ,       self.num_labels			)

        lowercase__    :   Union[str, Any]						= self.get_config()

        return config, pixel_values, labels

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= {
            '''global_padding''': '''same''',
            '''layer_type''': '''bottleneck''',
            '''depths''': [3, 4, 9],
            '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
            '''embedding_dynamic_padding''': True,
            '''hidden_sizes''': [96, 192, 384, 768],
            '''num_groups''': 2,
        }

        return DPTConfig(
            image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       backbone_out_indices=self.backbone_out_indices     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       is_decoder=__lowerCAmelCase     ,       initializer_range=self.initializer_range     ,       is_hybrid=self.is_hybrid     ,       backbone_config=__lowerCAmelCase     ,       backbone_featmap_shape=self.backbone_featmap_shape     ,       )

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= DPTModel(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Dict						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Union[str, Any]						= self.num_labels
        lowercase__    :   str						= DPTForDepthEstimation(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   List[str]						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.predicted_depth.shape     ,       (self.batch_size, self.image_size, self.image_size)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[Any]:
        lowercase__    :   str						= self.num_labels
        lowercase__    :   Tuple						= DPTForSemanticSegmentation(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Dict						= model(__lowerCAmelCase     ,       labels=__lowerCAmelCase			)
        self.parent.assertEqual(
            result.logits.shape     ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)			)


       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   List[str]						= self.prepare_config_and_inputs()
        lowercase__			,							lowercase__			,							lowercase__    :   Dict						= config_and_inputs
        lowercase__    :   Tuple						= {'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase  (						a__   ,						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE 		=   (
           {
               "depth-estimation": DPTForDepthEstimation,
               "feature-extraction": DPTModel,
               "image-segmentation": DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   str						= DPTModelTester(self			)
        lowercase__    :   int						= ConfigTester(self     ,       config_class=__lowerCAmelCase     ,       has_text_modality=__lowerCAmelCase     ,       hidden_size=37			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        self.config_tester.run_common_tests()

       @unittest.skip(reason='''DPT does not use inputs_embeds'''			)
       def _lowerCAmelCase(						self			)  ->  Tuple:
        pass

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__			,							lowercase__    :   Union[str, Any]						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__    :   str						= model_class(__lowerCAmelCase			)
         self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)			)
         lowercase__    :   Tuple						= model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__lowerCAmelCase     ,       nn.Linear			)			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__    :   Dict						= model_class(__lowerCAmelCase			)
         lowercase__    :   Optional[Any]						= inspect.signature(model.forward			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase__    :   Tuple						= [*signature.parameters.keys()]

         lowercase__    :   int						= ['''pixel_values''']
         self.assertListEqual(arg_names[:1]     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Any						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        for model_class in self.all_model_classes:
         if model_class.__name__ == "DPTForDepthEstimation":
          continue

         lowercase__			,							lowercase__    :   str						= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__    :   Dict						= True

         if model_class in get_values(__lowerCAmelCase			):
          continue

         lowercase__    :   List[str]						= model_class(__lowerCAmelCase			)
         model.to(__lowerCAmelCase			)
         model.train()
         lowercase__    :   Tuple						= self._prepare_for_class(__lowerCAmelCase     ,       __lowerCAmelCase     ,       return_labels=__lowerCAmelCase			)
         lowercase__    :   str						= model(**__lowerCAmelCase			).loss
         loss.backward()

       def _lowerCAmelCase(						self			)  ->  Tuple:
        for model_class in self.all_model_classes:
         if model_class.__name__ == "DPTForDepthEstimation":
          continue

         lowercase__			,							lowercase__    :   Optional[int]						= self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__    :   Any						= False
         lowercase__    :   str						= True

         if model_class in get_values(__lowerCAmelCase			) or not model_class.supports_gradient_checkpointing:
          continue
         lowercase__    :   List[str]						= model_class(__lowerCAmelCase			)
         model.to(__lowerCAmelCase			)
         model.gradient_checkpointing_enable()
         model.train()
         lowercase__    :   str						= self._prepare_for_class(__lowerCAmelCase     ,       __lowerCAmelCase     ,       return_labels=__lowerCAmelCase			)
         lowercase__    :   List[Any]						= model(**__lowerCAmelCase			).loss
         loss.backward()

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs_for_common()

        lowercase__    :   Union[str, Any]						= _config_zero_init(__lowerCAmelCase			)
        for model_class in self.all_model_classes:
         lowercase__    :   Dict						= model_class(config=__lowerCAmelCase			)
         # Skip the check for the backbone
         lowercase__    :   Union[str, Any]						= []
         for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
           lowercase__    :   List[Any]						= [F"""{name}.{key}""" for key in module.state_dict().keys()]
           break

         for name, param in model.named_parameters():
          if param.requires_grad:
           if name in backbone_params:
            continue
           self.assertIn(
               ((param.data.mean() * 1E9).round() / 1E9).item()     ,       [0.0, 1.0]     ,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,       )

       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
       def _lowerCAmelCase(						self			)  ->  List[str]:
        pass

       @slow
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
         lowercase__    :   Dict						= DPTModel.from_pretrained(__lowerCAmelCase			)
         self.assertIsNotNone(__lowerCAmelCase			)


       def _lowerCAmelCase(						self			)  ->  str:
        # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
        lowercase__			,							lowercase__    :   Any						= self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__    :   List[str]						= '''add'''
        with self.assertRaises(__lowerCAmelCase			):
         lowercase__    :   Tuple						= DPTForDepthEstimation(__lowerCAmelCase			)


def       __UpperCamelCase	(       ):
 lowercase__    :   Optional[int]						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
 return image





@require_torch
@require_vision
@slow
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Optional[int]						= DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''			)
        lowercase__    :   List[Any]						= DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''			).to(__lowerCAmelCase			)

        lowercase__    :   Optional[Any]						= prepare_img()
        lowercase__    :   Optional[Any]						= image_processor(images=__lowerCAmelCase     ,       return_tensors='''pt'''			).to(__lowerCAmelCase			)

        # forward pass
        with torch.no_grad():
         lowercase__    :   Optional[Any]						= model(**__lowerCAmelCase			)
         lowercase__    :   str						= outputs.predicted_depth

        # verify the predicted depth
        lowercase__    :   Optional[Any]						= torch.Size((1, 384, 384)			)
        self.assertEqual(predicted_depth.shape     ,       __lowerCAmelCase			)

        lowercase__    :   str						= torch.tensor(
            [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]			).to(__lowerCAmelCase			)

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,       __lowerCAmelCase     ,       atol=1E-4			)			)

