


"""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 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=32       ,		UpperCAmelCase__=16       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=32       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=[0, 1, 2, 3]       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=37       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=[1, 384, 24, 24]       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=None       ,		):
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				batch_size
								SCREAMING_SNAKE_CASE__     							=				image_size
								SCREAMING_SNAKE_CASE__     							=				patch_size
								SCREAMING_SNAKE_CASE__     							=				num_channels
								SCREAMING_SNAKE_CASE__     							=				is_training
								SCREAMING_SNAKE_CASE__     							=				use_labels
								SCREAMING_SNAKE_CASE__     							=				hidden_size
								SCREAMING_SNAKE_CASE__     							=				num_hidden_layers
								SCREAMING_SNAKE_CASE__     							=				backbone_out_indices
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads
								SCREAMING_SNAKE_CASE__     							=				intermediate_size
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				hidden_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				initializer_range
								SCREAMING_SNAKE_CASE__     							=				num_labels
								SCREAMING_SNAKE_CASE__     							=				backbone_featmap_shape
								SCREAMING_SNAKE_CASE__     							=				scope
								SCREAMING_SNAKE_CASE__     							=				is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								SCREAMING_SNAKE_CASE__     							=				(image_size // patch_size) ** 2
								SCREAMING_SNAKE_CASE__     							=				num_patches + 1



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

								SCREAMING_SNAKE_CASE__     							=				self.get_config()

								return config, pixel_values, labels



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				{
								    "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=_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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				DPTModel(config=_SCREAMING_SNAKE_CASE     )
								model.to(_SCREAMING_SNAKE_CASE     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(_SCREAMING_SNAKE_CASE     )
								self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.num_labels
								SCREAMING_SNAKE_CASE__     							=				DPTForDepthEstimation(_SCREAMING_SNAKE_CASE     )
								model.to(_SCREAMING_SNAKE_CASE     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(_SCREAMING_SNAKE_CASE     )
								self.parent.assertEqual(result.predicted_depth.shape       ,		(self.batch_size, self.image_size, self.image_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.num_labels
								SCREAMING_SNAKE_CASE__     							=				DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE     )
								model.to(_SCREAMING_SNAKE_CASE     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				config_and_inputs
								SCREAMING_SNAKE_CASE__     							=				{"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class 			lowerCamelCase_		(			lowercase       ,  lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Any      							=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				_lowerCAmelCase      :      List[str]      							=					(
				    {
				        "depth-estimation": DPTForDepthEstimation,
				        "feature-extraction": DPTModel,
				        "image-segmentation": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				_lowerCAmelCase      :      Optional[int]      							=					False
				_lowerCAmelCase      :      Any      							=					False
				_lowerCAmelCase      :      Union[str, Any]      							=					False



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				DPTModelTester(self     )
								SCREAMING_SNAKE_CASE__     							=				ConfigTester(self       ,		config_class=_SCREAMING_SNAKE_CASE       ,		has_text_modality=_SCREAMING_SNAKE_CASE       ,		hidden_size=37     )



				def   lowerCAmelCase__		(						self     ):
								self.config_tester.run_common_tests()



				@unittest.skip(reason="DPT does not use inputs_embeds"     )
				def   lowerCAmelCase__		(						self     ):
								pass



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

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

												SCREAMING_SNAKE_CASE__     							=				["pixel_values"]
												self.assertListEqual(arg_names[:1]       ,		_SCREAMING_SNAKE_CASE     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE     )



				def   lowerCAmelCase__		(						self     ):






								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE__     							=				True

												if model_class in get_values(_SCREAMING_SNAKE_CASE     ):
																continue

												SCREAMING_SNAKE_CASE__     							=				model_class(_SCREAMING_SNAKE_CASE     )
												model.to(_SCREAMING_SNAKE_CASE     )
												model.train()
												SCREAMING_SNAKE_CASE__     							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		return_labels=_SCREAMING_SNAKE_CASE     )
												SCREAMING_SNAKE_CASE__     							=				model(**_SCREAMING_SNAKE_CASE     ).loss
												loss.backward()



				def   lowerCAmelCase__		(						self     ):






								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
												SCREAMING_SNAKE_CASE__     							=				False
												SCREAMING_SNAKE_CASE__     							=				True

												if model_class in get_values(_SCREAMING_SNAKE_CASE     ) or not model_class.supports_gradient_checkpointing:
																continue
												SCREAMING_SNAKE_CASE__     							=				model_class(_SCREAMING_SNAKE_CASE     )
												model.to(_SCREAMING_SNAKE_CASE     )
												model.gradient_checkpointing_enable()
												model.train()
												SCREAMING_SNAKE_CASE__     							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		return_labels=_SCREAMING_SNAKE_CASE     )
												SCREAMING_SNAKE_CASE__     							=				model(**_SCREAMING_SNAKE_CASE     ).loss
												loss.backward()



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

								SCREAMING_SNAKE_CASE__     							=				_config_zero_init(_SCREAMING_SNAKE_CASE     )
								for model_class in self.all_model_classes:
												SCREAMING_SNAKE_CASE__     							=				model_class(config=_SCREAMING_SNAKE_CASE     )
												# Skip the check for the backbone
												SCREAMING_SNAKE_CASE__     							=				[]
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				SCREAMING_SNAKE_CASE__     							=				[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     ):
								pass



				@slow
				def   lowerCAmelCase__		(						self     ):






								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												SCREAMING_SNAKE_CASE__     							=				DPTModel.from_pretrained(_SCREAMING_SNAKE_CASE     )
												self.assertIsNotNone(_SCREAMING_SNAKE_CASE     )






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE__     							=				"add"
								with self.assertRaises(_SCREAMING_SNAKE_CASE     ):
												SCREAMING_SNAKE_CASE__     							=				DPTForDepthEstimation(_SCREAMING_SNAKE_CASE     )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
				return image


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





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"     )
								SCREAMING_SNAKE_CASE__     							=				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"     ).to(_SCREAMING_SNAKE_CASE     )

								SCREAMING_SNAKE_CASE__     							=				prepare_img()
								SCREAMING_SNAKE_CASE__     							=				image_processor(images=_SCREAMING_SNAKE_CASE       ,		return_tensors="pt"     ).to(_SCREAMING_SNAKE_CASE     )

								# forward pass
								with torch.no_grad():
												SCREAMING_SNAKE_CASE__     							=				model(**_SCREAMING_SNAKE_CASE     )
												SCREAMING_SNAKE_CASE__     							=				outputs.predicted_depth

								# verify the predicted depth
								SCREAMING_SNAKE_CASE__     							=				torch.Size((1, 384, 384)     )
								self.assertEqual(predicted_depth.shape       ,		_SCREAMING_SNAKE_CASE     )

								SCREAMING_SNAKE_CASE__     							=				torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]     ).to(_SCREAMING_SNAKE_CASE     )

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




"""simple docstring"""






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.task_name.lower()


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"train"
				_lowerCAmelCase      :      Union[str, Any]      							=					"dev"
				_lowerCAmelCase      :      List[Any]      							=					"test"


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      GlueDataTrainingArguments
				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[InputFeatures]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = Split.train       ,		UpperCAmelCase__ = None       ,		):
								warnings.warn(
								    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
								    "library. You can have a look at this example script for pointers: "
								    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"       ,		UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				args
								SCREAMING_SNAKE_CASE__     							=				glue_processors[args.task_name]()
								SCREAMING_SNAKE_CASE__     							=				glue_output_modes[args.task_name]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												try:
																SCREAMING_SNAKE_CASE__     							=				Split[mode]
												except KeyError:
																raise KeyError("mode is not a valid split name"     )
        # Load data features from cache or dataset file
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir       ,		f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE__     							=				label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
								with FileLock(UpperCAmelCase__     ):
												if os.path.exists(UpperCAmelCase__     ) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE__     							=				time.time()
																SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
																logger.info(
																    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''       ,		time.time() - start     )
												else:
																logger.info(f'''Creating features from dataset file at {args.data_dir}'''     )

																if mode == Split.dev:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_dev_examples(args.data_dir     )
																elif mode == Split.test:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_test_examples(args.data_dir     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_train_examples(args.data_dir     )
																if limit_length is not None:
																				SCREAMING_SNAKE_CASE__     							=				examples[:limit_length]
																SCREAMING_SNAKE_CASE__     							=				glue_convert_examples_to_features(
																    UpperCAmelCase__       ,		UpperCAmelCase__       ,		max_length=args.max_seq_length       ,		label_list=UpperCAmelCase__       ,		output_mode=self.output_mode       ,		)
																SCREAMING_SNAKE_CASE__     							=				time.time()
																torch.save(self.features       ,		UpperCAmelCase__     )
																# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																logger.info(
																    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''     )



				def __len__(						self     ):
								return len(self.features     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								return self.features[i]






				def   lowerCAmelCase__		(						self     ):
								return self.label_list
