
'''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


UpperCAmelCase    					=   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCAmelCase    					=   {
    "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
UpperCAmelCase    					=   {
    "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
}
UpperCAmelCase    					=   sorted(arg_to_scheduler.keys())
UpperCAmelCase    					=   "{" + ", ".join(arg_to_scheduler_choices) + "}"

class 						a   (				pl.LightningModule    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				argparse.Namespace,    SCREAMING_SNAKE_CASE_	:				Tuple=None,    SCREAMING_SNAKE_CASE_	:				Any="base",    SCREAMING_SNAKE_CASE_	:				Tuple=None,    SCREAMING_SNAKE_CASE_	:				str=None,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any],    ):
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    0
										snake_case       :     int =    Path(self.hparams.output_dir      )
										snake_case       :     Tuple =    self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																snake_case       :     int =    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=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										else:
																snake_case       :     PretrainedConfig =    config

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

										if tokenizer is None:
																snake_case       :     str =    AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,    cache_dir=SCREAMING_SNAKE_CASE_,    )
										else:
																snake_case       :     PreTrainedTokenizer =    tokenizer
										snake_case       :     int =    MODEL_MODES[mode]
										if model is None:
																snake_case       :     List[str] =    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=SCREAMING_SNAKE_CASE_,    )
										else:
																snake_case       :     int =    model




				def 	__snake_case							(							self	:				List[str],    *SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     str =    self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     int =    arg_to_scheduler[self.hparams.lr_scheduler]
										snake_case       :     Optional[int] =    get_schedule_func(
										    self.opt,    num_warmup_steps=self.hparams.warmup_steps,    num_training_steps=self.total_steps()      )
										snake_case       :     int =    {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
										return scheduler




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Dict =    self.model
										snake_case       :     Optional[int] =    ['''bias''', '''LayerNorm.weight''']
										snake_case       :     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:
																snake_case       :     Union[str, Any] =    Adafactor(
																    SCREAMING_SNAKE_CASE_,    lr=self.hparams.learning_rate,    scale_parameter=SCREAMING_SNAKE_CASE_,    relative_step=SCREAMING_SNAKE_CASE_      )

										else:
																snake_case       :     List[str] =    AdamW(
																    SCREAMING_SNAKE_CASE_,    lr=self.hparams.learning_rate,    eps=self.hparams.adam_epsilon      )
										snake_case       :     Optional[Any] =    optimizer

										snake_case       :     Dict =    self.get_lr_scheduler()

										return [optimizer], [scheduler]




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Any      ):
										return self.validation_step(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.validation_end(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     List[Any] =    max(1,    self.hparams.gpus      )  # TODO: consider num_tpu_cores
										snake_case       :     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 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										if stage == "test":
																snake_case       :     Tuple =    len(self.test_dataloader().dataset      )
										else:
																snake_case       :     Optional[Any] =    self.get_dataloader('''train''',    self.hparams.train_batch_size,    shuffle=SCREAMING_SNAKE_CASE_      )
																snake_case       :     Tuple =    len(self.train_dataloader().dataset      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				bool = False      ):
										raise NotImplementedError('''You must implement this for your task'''      )




				def 	__snake_case							(							self	:				str      ):
										return self.train_loader




				def 	__snake_case							(							self	:				List[str]      ):
										return self.get_dataloader('''dev''',    self.hparams.eval_batch_size,    shuffle=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any]      ):
										return self.get_dataloader('''test''',    self.hparams.eval_batch_size,    shuffle=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										return os.path.join(
										    self.hparams.data_dir,    '''cached_{}_{}_{}'''.format(
										        SCREAMING_SNAKE_CASE_,    list(filter(SCREAMING_SNAKE_CASE_,    self.hparams.model_name_or_path.split('''/'''      )      )      ).pop(),    str(self.hparams.max_seq_length      ),    ),    )




				@pl.utilities.rank_zero_only
				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict[str, Any]      ):
										snake_case       :     int =    self.output_dir.joinpath('''best_tfmr'''      )
										snake_case       :     int =    self.step_count
										self.model.save_pretrained(SCREAMING_SNAKE_CASE_      )
										self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )




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




class 						a   (				pl.Callback    ):




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str      ):
										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 						a   (				pl.Callback    ):




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Any      ):
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
																if param.grad is None:
																						print(SCREAMING_SNAKE_CASE_      )




class 						a   (				pl.Callback    ):




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     Union[str, Any] =    trainer.lr_schedulers[0]['''scheduler''']
										snake_case       :     Optional[int] =    {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
										pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				pl.Trainer,    SCREAMING_SNAKE_CASE_	:				pl.LightningModule      ):
										rank_zero_info('''***** Validation results *****'''      )
										snake_case       :     Dict =    trainer.callback_metrics
										# Log results
										for key in sorted(SCREAMING_SNAKE_CASE_      ):
																if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_,    str(metrics[key]      )      )      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				pl.Trainer,    SCREAMING_SNAKE_CASE_	:				pl.LightningModule      ):
										rank_zero_info('''***** Test results *****'''      )
										snake_case       :     Optional[Any] =    trainer.callback_metrics
										# Log and save results to file
										snake_case       :     Dict =    os.path.join(pl_module.hparams.output_dir,    '''test_results.txt'''      )
										with open(SCREAMING_SNAKE_CASE_,    '''w'''      ) as writer:
																for key in sorted(SCREAMING_SNAKE_CASE_      ):
																						if key not in ["log", "progress_bar"]:
																												rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_,    str(metrics[key]      )      )      )
																												writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_,    str(metrics[key]      )      )      )




def      A (       A_							:					List[Any]    ,							A_							:					Union[str, Any]       ):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    '''--output_dir'''    ,							default=str(Path(A_       ).parent / '''test_run''' / '''model_checkpoints'''       )    ,							type=A_    ,							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=A_    ,							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=A_       )
						parser.add_argument('''--max_grad_norm'''    ,							dest='''gradient_clip_val'''    ,							default=1.0    ,							type=A_    ,							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=A_    ,							default=1    ,							help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,							)
						parser.add_argument('''--seed'''    ,							type=A_    ,							default=42    ,							help='''random seed for initialization'''       )
						parser.add_argument(
						    '''--data_dir'''    ,							default=str(Path(A_       ).parent / '''test_run''' / '''dummy-train-data'''       )    ,							type=A_    ,							help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,							)





def      A (       A_							:					BaseTransformer    ,							A_							:					argparse.Namespace    ,							A_							:					Union[str, Any]=None    ,							A_							:					List[Any]=True    ,							A_							:					List[Any]=[]    ,							A_							:					Tuple=None    ,							A_							:					Union[str, Any]=None    ,							**A_							:					List[Any]    ,							):
						pl.seed_everything(args.seed       )

						# init model
						snake_case       :     int =    Path(model.hparams.output_dir       )
						odir.mkdir(exist_ok=A_       )

						# add custom checkpoints
						if checkpoint_callback is None:
												snake_case       :     Optional[Any] =    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(A_       )
						if logging_callback is None:
												snake_case       :     Any =    LoggingCallback()

						snake_case       :     List[str] =    {}

						if args.fpaa:
												snake_case       :     str =    16

						if args.gpus > 1:
												snake_case       :     Optional[int] =    '''auto'''
												snake_case       :     Optional[Any] =    '''ddp'''

						snake_case       :     Optional[Any] =    args.accumulate_grad_batches
						snake_case       :     List[str] =    None
						snake_case       :     Union[str, Any] =    '''auto'''

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

						if args.do_train:
												trainer.fit(A_       )

						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 						a   :




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]=2,    SCREAMING_SNAKE_CASE_	:				str=32,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=16,    SCREAMING_SNAKE_CASE_	:				str=3,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=True,    SCREAMING_SNAKE_CASE_	:				int=True,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=32,    SCREAMING_SNAKE_CASE_	:				Any=4,    SCREAMING_SNAKE_CASE_	:				Dict=[0, 1, 2, 3],    SCREAMING_SNAKE_CASE_	:				Dict=4,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=37,    SCREAMING_SNAKE_CASE_	:				str="gelu",    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.1,    SCREAMING_SNAKE_CASE_	:				List[Any]=0.1,    SCREAMING_SNAKE_CASE_	:				Dict=0.02,    SCREAMING_SNAKE_CASE_	:				int=3,    SCREAMING_SNAKE_CASE_	:				List[str]=[1, 3_84, 24, 24],    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    SCREAMING_SNAKE_CASE_	:				Optional[int]=None,    ):
										snake_case       :     int =    parent
										snake_case       :     Tuple =    batch_size
										snake_case       :     List[str] =    image_size
										snake_case       :     Union[str, Any] =    patch_size
										snake_case       :     Dict =    num_channels
										snake_case       :     int =    is_training
										snake_case       :     Any =    use_labels
										snake_case       :     Any =    hidden_size
										snake_case       :     Union[str, Any] =    num_hidden_layers
										snake_case       :     Tuple =    backbone_out_indices
										snake_case       :     Dict =    num_attention_heads
										snake_case       :     Optional[Any] =    intermediate_size
										snake_case       :     Any =    hidden_act
										snake_case       :     List[str] =    hidden_dropout_prob
										snake_case       :     List[str] =    attention_probs_dropout_prob
										snake_case       :     Tuple =    initializer_range
										snake_case       :     Any =    num_labels
										snake_case       :     Union[str, Any] =    backbone_featmap_shape
										snake_case       :     Optional[int] =    scope
										snake_case       :     Tuple =    is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										snake_case       :     Dict =    (image_size // patch_size) ** 2
										snake_case       :     Any =    num_patches + 1




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Optional[Any] =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

										snake_case       :     Union[str, Any] =    None
										if self.use_labels:
																snake_case       :     List[Any] =    ids_tensor([self.batch_size, self.image_size, self.image_size],    self.num_labels      )

										snake_case       :     Optional[int] =    self.get_config()

										return config, pixel_values, labels




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Optional[int] =    {
										    '''global_padding''': '''same''',
										    '''layer_type''': '''bottleneck''',
										    '''depths''': [3, 4, 9],
										    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
										    '''embedding_dynamic_padding''': True,
										    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
										    '''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=SCREAMING_SNAKE_CASE_,    initializer_range=self.initializer_range,    is_hybrid=self.is_hybrid,    backbone_config=SCREAMING_SNAKE_CASE_,    backbone_featmap_shape=self.backbone_featmap_shape,    )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     Dict =    DPTModel(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     List[Any] =    model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     int =    self.num_labels
										snake_case       :     Any =    DPTForDepthEstimation(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.predicted_depth.shape,    (self.batch_size, self.image_size, self.image_size)      )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     List[str] =    self.num_labels
										snake_case       :     List[Any] =    DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_,    labels=SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(
										    result.logits.shape,    (self.batch_size, self.num_labels, self.image_size, self.image_size)      )




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Optional[int] =    self.prepare_config_and_inputs()
										snake_case,					snake_case,					snake_case       :     Tuple =    config_and_inputs
										snake_case       :     List[str] =    {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 						a   (				__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				_snake_case		    = (
				    {
				        '''depth-estimation''': DPTForDepthEstimation,
				        '''feature-extraction''': DPTModel,
				        '''image-segmentation''': DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Optional[Any] =    DPTModelTester(self      )
										snake_case       :     Any =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_,    has_text_modality=SCREAMING_SNAKE_CASE_,    hidden_size=37      )




				def 	__snake_case							(							self	:				Dict      ):
										self.config_tester.run_common_tests()




				@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
				def 	__snake_case							(							self	:				int      ):
										pass




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case,					snake_case       :     str =    self.model_tester.prepare_config_and_inputs_for_common()

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




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case,					snake_case       :     Optional[int] =    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case       :     int =    model_class(SCREAMING_SNAKE_CASE_      )
																snake_case       :     int =    inspect.signature(model.forward      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case       :     Optional[int] =    [*signature.parameters.keys()]

																snake_case       :     Union[str, Any] =    ['''pixel_values''']
																self.assertListEqual(arg_names[:1],    SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Tuple =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     List[Any] =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Tuple =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Any      ):
										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																snake_case,					snake_case       :     Optional[Any] =    self.model_tester.prepare_config_and_inputs_for_common()
																snake_case       :     Dict =    True

																if model_class in get_values(SCREAMING_SNAKE_CASE_      ):
																						continue

																snake_case       :     Tuple =    model_class(SCREAMING_SNAKE_CASE_      )
																model.to(SCREAMING_SNAKE_CASE_      )
																model.train()
																snake_case       :     Any =    self._prepare_for_class(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    return_labels=SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    model(**SCREAMING_SNAKE_CASE_      ).loss
																loss.backward()




				def 	__snake_case							(							self	:				int      ):
										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																snake_case,					snake_case       :     Any =    self.model_tester.prepare_config_and_inputs_for_common()
																snake_case       :     Dict =    False
																snake_case       :     Dict =    True

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




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case,					snake_case       :     List[str] =    self.model_tester.prepare_config_and_inputs_for_common()

										snake_case       :     Union[str, Any] =    _config_zero_init(SCREAMING_SNAKE_CASE_      )
										for model_class in self.all_model_classes:
																snake_case       :     List[str] =    model_class(config=SCREAMING_SNAKE_CASE_      )
																# Skip the check for the backbone
																snake_case       :     Optional[int] =    []
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												snake_case       :     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 	__snake_case							(							self	:				Union[str, Any]      ):
										pass




				@slow
				def 	__snake_case							(							self	:				List[Any]      ):
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																snake_case       :     int =    DPTModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict      ):
										# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
										snake_case,					snake_case       :     str =    self.model_tester.prepare_config_and_inputs_for_common()
										snake_case       :     int =    '''add'''
										with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Any =    DPTForDepthEstimation(SCREAMING_SNAKE_CASE_      )




def      A (       ):
						snake_case       :     int =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image





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




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Dict =    DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
										snake_case       :     int =    DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(SCREAMING_SNAKE_CASE_      )

										snake_case       :     str =    prepare_img()
										snake_case       :     List[str] =    image_processor(images=SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

										# forward pass
										with torch.no_grad():
																snake_case       :     List[Any] =    model(**SCREAMING_SNAKE_CASE_      )
																snake_case       :     Optional[Any] =    outputs.predicted_depth

										# verify the predicted depth
										snake_case       :     List[str] =    torch.Size((1, 3_84, 3_84)      )
										self.assertEqual(predicted_depth.shape,    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    torch.tensor(
										    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]      ).to(SCREAMING_SNAKE_CASE_      )

										self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00,    SCREAMING_SNAKE_CASE_,    atol=1e-4      )      )

