



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__					:


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Optional[Any]=2						, __UpperCamelCase      :  str=32						, __UpperCamelCase      :  int=16						, __UpperCamelCase      :  Union[str, Any]=3						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  Tuple=32						, __UpperCamelCase      :  str=4						, __UpperCamelCase      :  List[Any]=[0, 1, 2, 3]						, __UpperCamelCase      :  Union[str, Any]=4						, __UpperCamelCase      :  Optional[int]=37						, __UpperCamelCase      :  Any="gelu"						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  Tuple=0.1						, __UpperCamelCase      :  Tuple=0.0_2						, __UpperCamelCase      :  Optional[int]=3						, __UpperCamelCase      :  int=[1, 384, 24, 24]						, __UpperCamelCase      :  List[str]=True						, __UpperCamelCase      :  Optional[int]=None						, )		->     int:
											A      						=    parent
											A      						=    batch_size
											A      						=    image_size
											A      						=    patch_size
											A      						=    num_channels
											A      						=    is_training
											A      						=    use_labels
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    backbone_out_indices
											A      						=    num_attention_heads
											A      						=    intermediate_size
											A      						=    hidden_act
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    initializer_range
											A      						=    num_labels
											A      						=    backbone_featmap_shape
											A      						=    scope
											A      						=    is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											A      						=    (image_size // patch_size) ** 2
											A      						=    num_patches + 1


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											A      						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

											A      						=    self.get_config()

											return config, pixel_values, labels


				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											A      						=    {
											    '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 __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  int						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											A      						=    DPTModel(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase       )
											self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)       )


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Any       )		->     List[Any]:
											A      						=    self.num_labels
											A      						=    DPTForDepthEstimation(__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase       )
											self.parent.assertEqual(result.predicted_depth.shape						, (self.batch_size, self.image_size, self.image_size)       )


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[Any]       )		->     List[str]:
											A      						=    self.num_labels
											A      						=    DPTForSemanticSegmentation(__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, labels=__UpperCamelCase       )
											self.parent.assertEqual(
											    result.logits.shape						, (self.batch_size, self.num_labels, self.image_size, self.image_size)       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A      						=    config_and_inputs
											A      						=    {'pixel_values': pixel_values}
											return config, inputs_dict





@require_torch
class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase       ):
				A_   :     str								=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				A_   :     Union[str, Any]								=     (
				    {
				        'depth-estimation': DPTForDepthEstimation,
				        'feature-extraction': DPTModel,
				        'image-segmentation': DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				A_   :     List[str]								=     False
				A_   :     int								=     False
				A_   :     Any								=     False


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    DPTModelTester(self       )
											A      						=    ConfigTester(self						, config_class=__UpperCamelCase						, has_text_modality=__UpperCamelCase						, hidden_size=37       )


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											self.config_tester.run_common_tests()


				@unittest.skip(reason='DPT does not use inputs_embeds'       )
				def __UpperCamelCase						(							self      :  Tuple       )		->     Union[str, Any]:
											pass


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		A      						=    ['pixel_values']
																		self.assertListEqual(arg_names[:1]						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[int]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     str:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     int:
											for model_class in self.all_model_classes:
																		if model_class.__name__ == "DPTForDepthEstimation":
																									continue

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

																		if model_class in get_values(__UpperCamelCase       ):
																									continue

																		A      						=    model_class(__UpperCamelCase       )
																		model.to(__UpperCamelCase       )
																		model.train()
																		A      						=    self._prepare_for_class(__UpperCamelCase						, __UpperCamelCase						, return_labels=__UpperCamelCase       )
																		A      						=    model(**__UpperCamelCase       ).loss
																		loss.backward()


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

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

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[int]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

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


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     List[Any]:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																		A      						=    DPTModel.from_pretrained(__UpperCamelCase       )
																		self.assertIsNotNone(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     Tuple:
											# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()
											A      						=    'add'
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    DPTForDepthEstimation(__UpperCamelCase       )






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






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



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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											A      						=    DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'       )
											A      						=    DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'       ).to(__UpperCamelCase       )

											A      						=    prepare_img()
											A      						=    image_processor(images=__UpperCamelCase						, return_tensors='pt'       ).to(__UpperCamelCase       )

											# forward pass
											with torch.no_grad():
																		A      						=    model(**__UpperCamelCase       )
																		A      						=    outputs.predicted_depth

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

											A      						=    torch.tensor(
											    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]       ).to(__UpperCamelCase       )

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



import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )




@require_torch
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	int					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =3
             _lowerCamelCase			:						Dict            =250

             _lowerCamelCase			:						Tuple            =ids_tensor((batch_size, length)     ,  lowercase_					)
             _lowerCamelCase			:						str            =torch.ones((batch_size, length)     ,  device=lowercase_     ,  dtype=torch.float					) / length
             return input_ids, scores






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Dict            =StoppingCriteriaList(
                 [
                     MaxLengthCriteria(max_length=10					),
                     MaxTimeCriteria(max_time=0.1					),
                 ]					)

             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Any            =MaxLengthCriteria(max_length=10					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =MaxNewTokensCriteria(start_length=5     ,  max_new_tokens=5					)

             _lowerCamelCase      ,	_lowerCamelCase			:						str            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						Optional[Any]            =StoppingCriteriaList([criteria]					)
             self.assertEqual(criteria_list.max_length     ,  10					)






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Tuple            =MaxTimeCriteria(max_time=0.1					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						int            =MaxTimeCriteria(max_time=0.1     ,  initial_timestamp=time.time() - 0.2					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  10					)

             with self.assertWarns(lowercase_					):
                    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  11					)

             _lowerCamelCase			:						Optional[Any]            =validate_stopping_criteria(StoppingCriteriaList()     ,  11					)

             self.assertEqual(len(lowercase_					)     ,  1					)


