



"""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__   :




	'''simple docstring'''





	def __init__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=2       ,			_SCREAMING_SNAKE_CASE:		Dict=32       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=16       ,			_SCREAMING_SNAKE_CASE:		Dict=3       ,			_SCREAMING_SNAKE_CASE:		Tuple=True       ,			_SCREAMING_SNAKE_CASE:		Dict=True       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=32       ,			_SCREAMING_SNAKE_CASE:		Dict=4       ,			_SCREAMING_SNAKE_CASE:		Tuple=[0, 1, 2, 3]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=4       ,			_SCREAMING_SNAKE_CASE:		List[str]=37       ,			_SCREAMING_SNAKE_CASE:		int="gelu"       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.1       ,			_SCREAMING_SNAKE_CASE:		str=0.1       ,			_SCREAMING_SNAKE_CASE:		List[str]=0.02       ,			_SCREAMING_SNAKE_CASE:		Dict=3       ,			_SCREAMING_SNAKE_CASE:		Dict=[1, 384, 24, 24]       ,			_SCREAMING_SNAKE_CASE:		Any=True       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None       ,			)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       parent
				__lowerCAmelCase      :  List[Any]												=       batch_size
				__lowerCAmelCase      :  str												=       image_size
				__lowerCAmelCase      :  List[Any]												=       patch_size
				__lowerCAmelCase      :  Optional[Any]												=       num_channels
				__lowerCAmelCase      :  Dict												=       is_training
				__lowerCAmelCase      :  Optional[int]												=       use_labels
				__lowerCAmelCase      :  Optional[int]												=       hidden_size
				__lowerCAmelCase      :  List[Any]												=       num_hidden_layers
				__lowerCAmelCase      :  Optional[Any]												=       backbone_out_indices
				__lowerCAmelCase      :  Optional[Any]												=       num_attention_heads
				__lowerCAmelCase      :  Optional[Any]												=       intermediate_size
				__lowerCAmelCase      :  str												=       hidden_act
				__lowerCAmelCase      :  List[str]												=       hidden_dropout_prob
				__lowerCAmelCase      :  Optional[Any]												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  List[Any]												=       initializer_range
				__lowerCAmelCase      :  Any												=       num_labels
				__lowerCAmelCase      :  List[Any]												=       backbone_featmap_shape
				__lowerCAmelCase      :  Optional[int]												=       scope
				__lowerCAmelCase      :  str												=       is_hybrid
				# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
				__lowerCAmelCase      :  List[str]												=       (image_size // patch_size) ** 2
				__lowerCAmelCase      :  List[str]												=       num_patches + 1

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

				__lowerCAmelCase      :  str												=       self.get_config()

				return config, pixel_values, labels

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       {
				    "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 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       DPTModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  Union[str, Any]												=       model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.num_labels
				__lowerCAmelCase      :  int												=       DPTForDepthEstimation(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  List[Any]												=       model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.predicted_depth.shape       ,			(self.batch_size, self.image_size, self.image_size))

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.num_labels
				__lowerCAmelCase      :  List[str]												=       DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase      :  Dict												=       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 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.prepare_config_and_inputs()
				__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase      :  Dict												=       config_and_inputs
				__lowerCAmelCase      :  List[str]												=       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		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 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       DPTModelTester(self)
				__lowerCAmelCase      :  Union[str, Any]												=       ConfigTester(self       ,			config_class=_SCREAMING_SNAKE_CASE       ,			has_text_modality=_SCREAMING_SNAKE_CASE       ,			hidden_size=37)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	@unittest.skip(reason="DPT does not use inputs_embeds")
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Union[str, Any]:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase      :  List[Any]												=       model_class(_SCREAMING_SNAKE_CASE)
							self.assertIsInstance(model.get_input_embeddings()       ,			(nn.Module))
							__lowerCAmelCase      :  Optional[Any]												=       model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,			nn.Linear))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  int												=       self.model_tester.prepare_config_and_inputs_for_common()

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

							__lowerCAmelCase      :  Optional[Any]												=       ["pixel_values"]
							self.assertListEqual(arg_names[:1]       ,			_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Tuple:

				"""simple docstring"""

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

							__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
							__lowerCAmelCase      :  Tuple												=       True

							if model_class in get_values(_SCREAMING_SNAKE_CASE):
										continue

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Optional[int]:

				"""simple docstring"""

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

							__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[int]												=       self.model_tester.prepare_config_and_inputs_for_common()
							__lowerCAmelCase      :  List[Any]												=       False
							__lowerCAmelCase      :  Tuple												=       True

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs_for_common()

				__lowerCAmelCase      :  str												=       _config_zero_init(_SCREAMING_SNAKE_CASE)
				for model_class in self.all_model_classes:
							__lowerCAmelCase      :  Union[str, Any]												=       model_class(config=_SCREAMING_SNAKE_CASE)
							# Skip the check for the backbone
							__lowerCAmelCase      :  List[Any]												=       []
							for name, module in model.named_modules():
										if module.__class__.__name__ == "DPTViTHybridEmbeddings":
													__lowerCAmelCase      :  Dict												=       [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 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       str:

				"""simple docstring"""

				pass

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       List[str]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[int]												=       self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase      :  Any												=       "add"
				with self.assertRaises(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase      :  Tuple												=       DPTForDepthEstimation(_SCREAMING_SNAKE_CASE)






def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Dict												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
			return image





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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
				__lowerCAmelCase      :  Tuple												=       DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Dict												=       prepare_img()
				__lowerCAmelCase      :  int												=       image_processor(images=_SCREAMING_SNAKE_CASE       ,			return_tensors="pt").to(_SCREAMING_SNAKE_CASE)

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

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

				__lowerCAmelCase      :  List[Any]												=       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] / 100       ,			_SCREAMING_SNAKE_CASE       ,			atol=1e-4))




"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'  ,		'False'							)							) is not True  ,		reason='Skipping test because should only be run when releasing minor transformers version'  ,		)
@pytest.mark.usefixtures('sm_env'							)
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""simple docstring"""

				if self.framework == "pytorch":
							subprocess.run(
							    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()       ,			encoding="utf-8"       ,			check=_SCREAMING_SNAKE_CASE       ,			)
				assert hasattr(self       ,			"env")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""simple docstring"""

				return HuggingFace(
				    entry_point=self.script       ,			source_dir=self.env.test_path       ,			role=self.env.role       ,			image_uri=self.env.image_uri       ,			base_job_name=F"""{self.env.base_job_name}-single"""       ,			instance_count=_SCREAMING_SNAKE_CASE       ,			instance_type=self.instance_type       ,			debugger_hook_config=_SCREAMING_SNAKE_CASE       ,			hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Tuple												=       (
				    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"       ,			99_9999)
				)

				# assert kpis
				assert train_runtime <= self.results["train_runtime"]
				assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
				assert all(t <= self.results["eval_loss"] for t in eval_loss)

				# dump tests result into json file to share in PR
				with open(F"""{estimator.latest_training_job.name}.json"""       ,			"w") as outfile:
							json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,			_SCREAMING_SNAKE_CASE)