



'''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      __magic_name__						(   unittest.TestCase       ):







       def 							__lowercase      (							self :	List[str]		):
         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=_UpperCAmelCase     ,)
         assert hasattr(self     ,'env'		)







       def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Optional[int]=1		):
         # creates estimator
         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=_UpperCAmelCase     ,instance_type=self.instance_type     ,debugger_hook_config=_UpperCAmelCase     ,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}     ,metric_definitions=self.env.metric_definitions     ,py_version='py36'     ,)







       def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	Optional[int]		):
         TrainingJobAnalytics(_UpperCAmelCase		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







       def 							__lowercase      (							self :	Optional[Any]		):
         # create estimator
         _a					:				List[Any]				=							self.create_estimator()

         # run training
         estimator.fit()

         # result dataframe
         _a					:				Optional[int]				=							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

         # extract kpis
         _a					:				Optional[int]				=							list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']		)
         _a					:				Dict				=							list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']		)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         _a					:				Tuple				=							(
             Session().describe_training_job(estimator.latest_training_job.name		).get('TrainingTimeInSeconds'     ,999999		)
         )

         # 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}     ,_UpperCAmelCase		)









'''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      __magic_name__						:







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







							def 							__lowercase      (							self :	Dict		):
									_a					:				str				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

									_a					:				int				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	int		):
									_a					:				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=_UpperCAmelCase     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=_UpperCAmelCase     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Optional[Any]				=							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[Any]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							self.num_labels
									_a					:				Dict				=							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				str				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[Any]		):
									_a					:				Optional[int]				=							self.num_labels
									_a					:				Optional[int]				=							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[int]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[str]				=							self.prepare_config_and_inputs()
									_a	, _a	, _a					:				List[Any]				=							config_and_inputs
									_a					:				List[Any]				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase	:   int           = (
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Dict           = False
							lowerCAmelCase	:   List[str]           = False







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Union[str, Any]				=							DPTModelTester(self		)
									_a					:				int				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	Any		):
									self.config_tester.run_common_tests()







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







							def 							__lowercase      (							self :	List[str]		):
									_a	, _a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

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







							def 							__lowercase      (							self :	Any		):
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Optional[int]				=							model_class(_UpperCAmelCase		)
											_a					:				Tuple				=							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				Union[str, Any]				=							[*signature.parameters.keys()]

											_a					:				Optional[Any]				=							['pixel_values']
											self.assertListEqual(arg_names[:1]     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







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

											_a	, _a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Tuple				=							True

											if model_class in get_values(_UpperCAmelCase		):
													continue

											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a					:				Optional[int]				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







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

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

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







							def 							__lowercase      (							self :	str		):
									_a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

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







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a					:				List[Any]				=							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
									_a					:				Optional[Any]				=							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a					:				List[str]				=							DPTForDepthEstimation(_UpperCAmelCase		)



def 					__lowerCamelCase					(				)      ->				Optional[Any]:
		_a					:				Optional[Any]				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[Any]				=							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a					:				Union[str, Any]				=							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a					:				Optional[int]				=							prepare_img()
									_a					:				Optional[Any]				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				Tuple				=							model(**_UpperCAmelCase		)
											_a					:				Union[str, Any]				=							outputs.predicted_depth

									# verify the predicted depth
									_a					:				str				=							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape     ,_UpperCAmelCase		)

									_a					:				int				=							torch.tensor(
									    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]		).to(_UpperCAmelCase		)

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




