
'''simple docstring'''





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def      A (       A_							:					List[Any]    ,							A_							:					Tuple    ,							A_							:					Union[str, Any]    ,							A_							:					Union[str, Any]    ,							A_							:					List[Any]=True    ,							A_							:					Tuple="pt"       ):
						snake_case       :     Optional[int] =    {'''add_prefix_space''': True} if isinstance(A_    ,							A_       ) and not line.startswith(''' '''       ) else {}
						snake_case       :     int =    padding_side
						return tokenizer(
						    [line]    ,							max_length=A_    ,							padding='''max_length''' if pad_to_max_length else None    ,							truncation=A_    ,							return_tensors=A_    ,							add_special_tokens=A_    ,							**A_    ,							)

def      A (       A_							:					int    ,							A_							:					Optional[int]    ,							A_							:					Optional[int]=None    ,							):
						snake_case       :     Union[str, Any] =    input_ids.ne(A_       ).any(dim=0       )
						if attention_mask is None:
												return input_ids[:, keep_column_mask]
						else:
												return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 						a   (				__magic_name__    ):




				def __init__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Tuple="train",    SCREAMING_SNAKE_CASE_	:				str=None,    SCREAMING_SNAKE_CASE_	:				Dict=None,    SCREAMING_SNAKE_CASE_	:				str=None,    SCREAMING_SNAKE_CASE_	:				List[str]="",    ):
										super().__init__()
										snake_case       :     Tuple =    Path(SCREAMING_SNAKE_CASE_      ).joinpath(type_path + '''.source'''      )
										snake_case       :     Dict =    Path(SCREAMING_SNAKE_CASE_      ).joinpath(type_path + '''.target'''      )
										snake_case       :     Any =    self.get_char_lens(self.src_file      )
										snake_case       :     int =    max_source_length
										snake_case       :     Dict =    max_target_length
										assert min(self.src_lens      ) > 0, F"""found empty line in {self.src_file}"""
										snake_case       :     Dict =    tokenizer
										snake_case       :     Dict =    prefix
										if n_obs is not None:
																snake_case       :     str =    self.src_lens[:n_obs]
										snake_case       :     Any =    src_lang
										snake_case       :     Any =    tgt_lang




				def __len__(							self	:				Dict      ):
										return len(self.src_lens      )




				def __getitem__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     Any =    index + 1  # linecache starts at 1
										snake_case       :     Tuple =    self.prefix + linecache.getline(str(self.src_file      ),    SCREAMING_SNAKE_CASE_      ).rstrip('''\n'''      )
										snake_case       :     Optional[int] =    linecache.getline(str(self.tgt_file      ),    SCREAMING_SNAKE_CASE_      ).rstrip('''\n'''      )
										assert source_line, F"""empty source line for index {index}"""
										assert tgt_line, F"""empty tgt line for index {index}"""

										# Need to add eos token manually for T5
										if isinstance(self.tokenizer,    SCREAMING_SNAKE_CASE_      ):
																source_line += self.tokenizer.eos_token
																tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										snake_case       :     Dict =    (
										    self.tokenizer.question_encoder if isinstance(self.tokenizer,    SCREAMING_SNAKE_CASE_      ) else self.tokenizer
										)
										snake_case       :     Tuple =    self.tokenizer.generator if isinstance(self.tokenizer,    SCREAMING_SNAKE_CASE_      ) else self.tokenizer

										snake_case       :     str =    encode_line(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    self.max_source_length,    '''right'''      )
										snake_case       :     Dict =    encode_line(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    self.max_target_length,    '''right'''      )

										snake_case       :     Optional[int] =    source_inputs['''input_ids'''].squeeze()
										snake_case       :     Dict =    target_inputs['''input_ids'''].squeeze()
										snake_case       :     int =    source_inputs['''attention_mask'''].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										return [len(SCREAMING_SNAKE_CASE_      ) for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     Union[str, Any] =    torch.stack([x['''input_ids'''] for x in batch]      )
										snake_case       :     Tuple =    torch.stack([x['''attention_mask'''] for x in batch]      )
										snake_case       :     str =    torch.stack([x['''decoder_input_ids'''] for x in batch]      )
										snake_case       :     Union[str, Any] =    (
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer,    SCREAMING_SNAKE_CASE_      )
										    else self.tokenizer.pad_token_id
										)
										snake_case       :     List[str] =    (
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer,    SCREAMING_SNAKE_CASE_      )
										    else self.tokenizer.pad_token_id
										)
										snake_case       :     Union[str, Any] =    trim_batch(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case,					snake_case       :     int =    trim_batch(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    {
										    '''input_ids''': source_ids,
										    '''attention_mask''': source_mask,
										    '''decoder_input_ids''': y,
										}
										return batch




UpperCAmelCase    					=   getLogger(__name__)

def      A (       A_							:					List[List]       ):
						return list(itertools.chain.from_iterable(A_       )       )

def      A (       A_							:					str       ):
						snake_case       :     List[str] =    get_git_info()
						save_json(A_    ,							os.path.join(A_    ,							'''git_log.json'''       )       )

def      A (       A_							:					Dict    ,							A_							:					str    ,							A_							:					Optional[int]=4    ,							**A_							:					Union[str, Any]       ):
						with open(A_    ,							'''w'''       ) as f:
												json.dump(A_    ,							A_    ,							indent=A_    ,							**A_       )

def      A (       A_							:					Dict       ):
						with open(A_       ) as f:
												return json.load(A_       )

def      A (       ):
						snake_case       :     Union[str, Any] =    git.Repo(search_parent_directories=A_       )
						snake_case       :     Optional[Any] =    {
						    '''repo_id''': str(A_       ),
						    '''repo_sha''': str(repo.head.object.hexsha       ),
						    '''repo_branch''': str(repo.active_branch       ),
						    '''hostname''': str(socket.gethostname()       ),
						}
						return repo_infos

def      A (       A_							:					Callable    ,							A_							:					Iterable       ):
						return list(map(A_    ,							A_       )       )

def      A (       A_							:					Any    ,							A_							:					Optional[Any]       ):
						with open(A_    ,							'''wb'''       ) as f:
												return pickle.dump(A_    ,							A_       )

def      A (       A_							:					str       ):

						def remove_articles(A_							:					List[str]       ):
												return re.sub(R'''\b(a|an|the)\b'''    ,							''' '''    ,							A_       )

						def white_space_fix(A_							:					Optional[Any]       ):
												return " ".join(text.split()       )

						def remove_punc(A_							:					Union[str, Any]       ):
												snake_case       :     Optional[Any] =    set(string.punctuation       )
												return "".join(ch for ch in text if ch not in exclude       )

						def lower(A_							:					Optional[Any]       ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(A_       )       )       )       )

def      A (       A_							:					str    ,							A_							:					Any       ):
						snake_case       :     Optional[Any] =    normalize_answer(A_       ).split()
						snake_case       :     str =    normalize_answer(A_       ).split()
						snake_case       :     Dict =    Counter(A_       ) & Counter(A_       )
						snake_case       :     List[str] =    sum(common.values()       )
						if num_same == 0:
												return 0
						snake_case       :     Any =    1.0 * num_same / len(A_       )
						snake_case       :     List[Any] =    1.0 * num_same / len(A_       )
						snake_case       :     List[Any] =    (2 * precision * recall) / (precision + recall)
						return fa

def      A (       A_							:					List[Any]    ,							A_							:					Any       ):
						return normalize_answer(A_       ) == normalize_answer(A_       )

def      A (       A_							:					List[str]    ,							A_							:					List[str]       ):
						assert len(A_       ) == len(A_       )
						snake_case       :     Tuple =    0
						for hypo, pred in zip(A_    ,							A_       ):
												em += exact_match_score(A_    ,							A_       )
						if len(A_       ) > 0:
												em /= len(A_       )
						return {"em": em}

def      A (       A_							:					List[Any]       ):
						return model_prefix.startswith('''rag'''       )





def      A (       A_							:					List[str]    ,							A_							:					List[str]    ,							A_							:					str       ):
						snake_case       :     int =    {p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						snake_case       :     Union[str, Any] =    '''dropout_rate'''
						for p in extra_params:
												if getattr(A_    ,							A_    ,							A_       ):
																		if not hasattr(A_    ,							A_       ) and not hasattr(A_    ,							equivalent_param[p]       ):
																								logger.info('''config doesn\'t have a `{}` attribute'''.format(A_       )       )
																								delattr(A_    ,							A_       )
																								continue
																		snake_case       :     List[Any] =    p if hasattr(A_    ,							A_       ) else equivalent_param[p]
																		setattr(A_    ,							A_    ,							getattr(A_    ,							A_       )       )
																		delattr(A_    ,							A_       )
						return hparams, config



'''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      )      )

