

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 __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=1_3				,      lowerCamelCase__=6_4				,      lowerCamelCase__=2				,      lowerCamelCase__=3				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=3_2				,      lowerCamelCase__=5				,      lowerCamelCase__=4				,      lowerCamelCase__=3_7				,      lowerCamelCase__="gelu"				,      lowerCamelCase__=0.1				,      lowerCamelCase__=0.1				,      lowerCamelCase__=1_0				,      lowerCamelCase__=0.02				,      lowerCamelCase__=[1, 1_6, 4, 4]				,      lowerCamelCase__=None				,      ):
						UpperCAmelCase__:	Any									= parent
						UpperCAmelCase__:	int									= batch_size
						UpperCAmelCase__:	int									= image_size
						UpperCAmelCase__:	Dict									= patch_size
						UpperCAmelCase__:	Any									= num_channels
						UpperCAmelCase__:	Optional[Any]									= is_training
						UpperCAmelCase__:	Tuple									= use_labels
						UpperCAmelCase__:	List[str]									= hidden_size
						UpperCAmelCase__:	Union[str, Any]									= num_hidden_layers
						UpperCAmelCase__:	Union[str, Any]									= num_attention_heads
						UpperCAmelCase__:	Dict									= intermediate_size
						UpperCAmelCase__:	Any									= hidden_act
						UpperCAmelCase__:	str									= hidden_dropout_prob
						UpperCAmelCase__:	List[Any]									= attention_probs_dropout_prob
						UpperCAmelCase__:	Tuple									= type_sequence_label_size
						UpperCAmelCase__:	Tuple									= initializer_range
						UpperCAmelCase__:	int									= scope
						UpperCAmelCase__:	Optional[int]									= 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
						UpperCAmelCase__:	str									= (self.image_size // 3_2) ** 2
						UpperCAmelCase__:	Optional[Any]									= num_patches + 1





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

						UpperCAmelCase__:	List[str]									= self.get_config()

						return config, pixel_values, labels





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= {
						    "global_padding": "same",
						    "layer_type": "bottleneck",
						    "depths": [3, 4, 9],
						    "out_features": ["stage1", "stage2", "stage3"],
						    "embedding_dynamic_padding": True,
						    "hidden_sizes": [4, 8, 1_6, 3_2],
						    "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=SCREAMING_SNAKE_CASE__				,      initializer_range=self.initializer_range				,      backbone_featmap_shape=self.backbone_featmap_shape				,      backbone_config=SCREAMING_SNAKE_CASE__				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Union[str, Any]									= ViTHybridModel(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						UpperCAmelCase__:	Tuple									= model(SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= self.type_sequence_label_size
						UpperCAmelCase__:	Optional[Any]									= ViTHybridForImageClassification(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						UpperCAmelCase__:	int									= model(SCREAMING_SNAKE_CASE__				,      labels=SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.type_sequence_label_size)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= self.prepare_config_and_inputs()
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	int									= config_and_inputs
						UpperCAmelCase__:	Tuple									= {"pixel_values": pixel_values}
						return config, inputs_dict

@require_torch
class __UpperCamelCase     (				_lowerCAmelCase					,_lowerCAmelCase					,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					__magic_name__        =   (
					    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					__magic_name__        =   False
					__magic_name__        =   False
					__magic_name__        =   False





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= ViTHybridModelTester(self						)
						UpperCAmelCase__:	List[Any]									= ConfigTester(self				,      config_class=SCREAMING_SNAKE_CASE__				,      has_text_modality=SCREAMING_SNAKE_CASE__				,      hidden_size=3_7						)





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





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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	int									= self.model_tester.prepare_config_and_inputs_for_common()

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[Any]									= self.model_tester.prepare_config_and_inputs_for_common()

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

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase__:	int									= _config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
							UpperCAmelCase__:	Union[str, Any]									= model_class(config=SCREAMING_SNAKE_CASE__						)
							# Skip the check for the backbone
							for name, module in model.named_modules():
								if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
									UpperCAmelCase__:	Optional[int]									= [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    _UpperCAmelCase    (				self						):
						for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase__:	Tuple									= ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE__						)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)



def 			_A	(      ):
	UpperCAmelCase__:	Tuple									= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
	return image

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






					'''simple docstring'''



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





					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
						    SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Any									= self.default_image_processor
						UpperCAmelCase__:	str									= prepare_img()
						UpperCAmelCase__:	int									= image_processor(images=SCREAMING_SNAKE_CASE__				,      return_tensors="pt"						).to(SCREAMING_SNAKE_CASE__						)

						# forward pass
						with torch.no_grad():
							UpperCAmelCase__:	List[str]									= model(**SCREAMING_SNAKE_CASE__						)

						# verify the logits
						UpperCAmelCase__:	Tuple									= torch.Size((1, 1_0_0_0)						)
						self.assertEqual(outputs.logits.shape				,      SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Union[str, Any]									= torch.tensor([-1.9_090, -0.4_993, -0.2_389]						).to(SCREAMING_SNAKE_CASE__						)

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





					@slow
					@require_accelerate
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"						)
						UpperCAmelCase__:	List[str]									= ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"				,      device_map="auto"						)

						UpperCAmelCase__:	List[Any]									= prepare_img()

						UpperCAmelCase__:	Optional[int]									= image_processor(images=SCREAMING_SNAKE_CASE__				,      return_tensors="pt"						)
						UpperCAmelCase__:	List[Any]									= model(**SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Tuple									= outputs.logits
						# model predicts one of the 1000 ImageNet classes
						UpperCAmelCase__:	Dict									= logits.argmax(-1						).item()

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




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	None:
				warnings.warn(
				    "The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use DeiTImageProcessor instead."						,	SCREAMING_SNAKE_CASE__						,	)
				super().__init__(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)




