
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
		from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

class 							snake_case_ :





			def __init__(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase=13      ,						__lowerCAmelCase=64      ,						__lowerCAmelCase=2      ,						__lowerCAmelCase=3      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=32      ,						__lowerCAmelCase=5      ,						__lowerCAmelCase=4      ,						__lowerCAmelCase=37      ,						__lowerCAmelCase="gelu"      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=10      ,						__lowerCAmelCase=0.02      ,						__lowerCAmelCase=[1, 16, 4, 4]      ,						__lowerCAmelCase=None      ,						):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			parent
					SCREAMING_SNAKE_CASE_					:	Any		  =			batch_size
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			image_size
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			patch_size
					SCREAMING_SNAKE_CASE_					:	str		  =			num_channels
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			is_training
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			use_labels
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			hidden_size
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			num_hidden_layers
					SCREAMING_SNAKE_CASE_					:	Dict		  =			num_attention_heads
					SCREAMING_SNAKE_CASE_					:	Any		  =			intermediate_size
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			hidden_act
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			hidden_dropout_prob
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			type_sequence_label_size
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			initializer_range
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			scope
					SCREAMING_SNAKE_CASE_					:	Any		  =			backbone_featmap_shape

					# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
					# the number of patches is based on the feature map of the backbone, which by default uses an output stride
					# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			(self.image_size // 32) ** 2
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			num_patches + 1





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

					SCREAMING_SNAKE_CASE_					:	Any		  =			None
					if self.use_labels:
							SCREAMING_SNAKE_CASE_					:	str		  =			ids_tensor([self.batch_size]      ,						self.type_sequence_label_size      )

					SCREAMING_SNAKE_CASE_					:	str		  =			self.get_config()

					return config, pixel_values, labels





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			{
					    'global_padding': 'same',
					    'layer_type': 'bottleneck',
					    'depths': [3, 4, 9],
					    'out_features': ['stage1', 'stage2', 'stage3'],
					    'embedding_dynamic_padding': True,
					    'hidden_sizes': [4, 8, 16, 32],
					    'num_groups': 2,
					}

					return ViTHybridConfig(
					    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      ,						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      ,						backbone_featmap_shape=self.backbone_featmap_shape      ,						backbone_config=_UpperCamelCase      ,						)





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			ViTHybridModel(config=_UpperCamelCase      )
					model.to(_UpperCamelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			model(_UpperCamelCase      )
					self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)      )





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			self.type_sequence_label_size
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			ViTHybridForImageClassification(_UpperCamelCase      )
					model.to(_UpperCamelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			model(_UpperCamelCase      ,						labels=_UpperCamelCase      )
					self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)      )






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

@require_torch
class 							snake_case_ (				A__   ,  A__   ,  unittest.TestCase    ):
			__lowerCamelCase							:     Tuple						=			(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			__lowerCamelCase							:     Tuple						=			(
			    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			__lowerCamelCase							:     Optional[Any]						=			False
			__lowerCamelCase							:     Optional[int]						=			False
			__lowerCamelCase							:     int						=			False





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	str		  =			ViTHybridModelTester(self      )
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			ConfigTester(self      ,						config_class=_UpperCamelCase      ,						has_text_modality=_UpperCamelCase      ,						hidden_size=37      )





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





			@unittest.skip(reason='ViT does not use inputs_embeds'      )
			def 						__A						(		self      ):
					pass





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	List[Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			model_class(_UpperCamelCase      )
							self.assertIsInstance(model.get_input_embeddings()      ,						(nn.Module)      )
							SCREAMING_SNAKE_CASE_					:	Dict		  =			model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_UpperCamelCase      ,						nn.Linear      )      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	List[Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

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

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





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_UpperCamelCase      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	int		  =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			_config_zero_init(_UpperCamelCase      )
					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_					:	Dict		  =			model_class(config=_UpperCamelCase      )
							# Skip the check for the backbone
							for name, module in model.named_modules():
									if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
											SCREAMING_SNAKE_CASE_					:	List[str]		  =			[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'      ,						)






			@slow
			def 						__A						(		self      ):
					for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE_					:	int		  =			ViTHybridModel.from_pretrained(_UpperCamelCase      )
							self.assertIsNotNone(_UpperCamelCase      )



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

@require_torch
@require_vision
class 							snake_case_ (				unittest.TestCase    ):





			@cached_property
			def 						__A						(		self      ):
					return (
					    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
					    if is_vision_available()
					    else None
					)





			@slow
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
					    _UpperCamelCase      )

					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			self.default_image_processor
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			prepare_img()
					SCREAMING_SNAKE_CASE_					:	int		  =			image_processor(images=_UpperCamelCase      ,						return_tensors='pt'      ).to(_UpperCamelCase      )

					# forward pass
					with torch.no_grad():
							SCREAMING_SNAKE_CASE_					:	int		  =			model(**_UpperCamelCase      )

					# verify the logits
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			torch.Size((1, 1_000)      )
					self.assertEqual(outputs.logits.shape      ,						_UpperCamelCase      )

					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			torch.tensor([-1.90_90, -0.49_93, -0.23_89]      ).to(_UpperCamelCase      )

					self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,						_UpperCamelCase      ,						atol=1e-4      )      )






			@slow
			@require_accelerate
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'      )
					SCREAMING_SNAKE_CASE_					:	int		  =			ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'      ,						device_map='auto'      )

					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			prepare_img()

					SCREAMING_SNAKE_CASE_					:	Tuple		  =			image_processor(images=_UpperCamelCase      ,						return_tensors='pt'      )
					SCREAMING_SNAKE_CASE_					:	int		  =			model(**_UpperCamelCase      )
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			outputs.logits
					# model predicts one of the 1000 ImageNet classes
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			logits.argmax(-1      ).item()

					self.assertTrue(model.config.idalabel[predicted_class_idx]      ,						'tabby, tabby cat'      )













import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        warnings.warn(
            '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use GLPNImageProcessor instead.'''				,							_UpperCamelCase				,							)
        super().__init__(*_UpperCamelCase				,							**_UpperCamelCase					)