




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 lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=13						,   lowercase=64						,   lowercase=2						,   lowercase=3						,   lowercase=True						,   lowercase=True						,   lowercase=32						,   lowercase=5						,   lowercase=4						,   lowercase=37						,   lowercase="gelu"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=10						,   lowercase=0.02						,   lowercase=[1, 16, 4, 4]						,   lowercase=None						,   ):
							_lowerCamelCase  :    Dict					=					parent
							_lowerCamelCase  :    str					=					batch_size
							_lowerCamelCase  :    List[str]					=					image_size
							_lowerCamelCase  :    Tuple					=					patch_size
							_lowerCamelCase  :    Optional[int]					=					num_channels
							_lowerCamelCase  :    Optional[Any]					=					is_training
							_lowerCamelCase  :    List[str]					=					use_labels
							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    int					=					num_hidden_layers
							_lowerCamelCase  :    List[str]					=					num_attention_heads
							_lowerCamelCase  :    int					=					intermediate_size
							_lowerCamelCase  :    List[Any]					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    List[Any]					=					attention_probs_dropout_prob
							_lowerCamelCase  :    Dict					=					type_sequence_label_size
							_lowerCamelCase  :    Optional[Any]					=					initializer_range
							_lowerCamelCase  :    List[Any]					=					scope
							_lowerCamelCase  :    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
							_lowerCamelCase  :    Union[str, Any]					=					(self.image_size // 32) ** 2
							_lowerCamelCase  :    Union[str, Any]					=					num_patches + 1
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							_lowerCamelCase  :    Optional[int]					=					None
							if self.use_labels:
											_lowerCamelCase  :    Optional[int]					=					ids_tensor([self.batch_size]						,   self.type_sequence_label_size						)

							_lowerCamelCase  :    Optional[Any]					=					self.get_config()

							return config, pixel_values, labels
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					{
							    '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=_lowerCAmelCase						,   initializer_range=self.initializer_range						,   backbone_featmap_shape=self.backbone_featmap_shape						,   backbone_config=_lowerCAmelCase						,   )
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Optional[Any]					=					ViTHybridModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							_lowerCamelCase  :    Optional[Any]					=					model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    List[str]					=					self.type_sequence_label_size
							_lowerCamelCase  :    Union[str, Any]					=					ViTHybridForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							_lowerCamelCase  :    Tuple					=					model(_lowerCAmelCase						,   labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size)						)






			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					self.prepare_config_and_inputs()
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					config_and_inputs
							_lowerCamelCase  :    Dict					=					{'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__			(  lowercase,			lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			lowerCamelCase__   =    (
			    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__   =    False
			lowerCamelCase__   =    False
			lowerCamelCase__   =    False
			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					ViTHybridModelTester(self						)
							_lowerCamelCase  :    List[Any]					=					ConfigTester(self						,   config_class=_lowerCAmelCase						,   has_text_modality=_lowerCAmelCase						,   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						):
							_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase  :    int					=					model_class(_lowerCAmelCase						)
											self.assertIsInstance(model.get_input_embeddings()						,   (nn.Module)						)
											_lowerCamelCase  :    Optional[Any]					=					model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_lowerCAmelCase						,   nn.Linear						)						)
			def       A_					(						self						):
							_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

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

											_lowerCamelCase  :    List[str]					=					['pixel_values']
											self.assertListEqual(arg_names[:1]						,   _lowerCAmelCase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)
			def       A_					(						self						):
							_lowerCamelCase,				_lowerCamelCase  :    int					=					self.model_tester.prepare_config_and_inputs_for_common()

							_lowerCamelCase  :    List[Any]					=					_config_zero_init(_lowerCAmelCase						)
							for model_class in self.all_model_classes:
											_lowerCamelCase  :    str					=					model_class(config=_lowerCAmelCase						)
											# Skip the check for the backbone
											for name, module in model.named_modules():
															if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																			_lowerCamelCase  :    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       A_					(						self						):
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase  :    Tuple					=					ViTHybridModel.from_pretrained(_lowerCAmelCase						)
											self.assertIsNotNone(_lowerCAmelCase						)







def   _snake_case    (       ):
				_lowerCamelCase  :    Optional[int]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
				return image






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






			'''simple docstring'''



			@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						):
							_lowerCamelCase  :    List[str]					=					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    _lowerCAmelCase						)

							_lowerCamelCase  :    Tuple					=					self.default_image_processor
							_lowerCamelCase  :    Any					=					prepare_img()
							_lowerCamelCase  :    Any					=					image_processor(images=_lowerCAmelCase						,   return_tensors='pt'						).to(_lowerCAmelCase						)

							# forward pass
							with torch.no_grad():
											_lowerCamelCase  :    Optional[int]					=					model(**_lowerCAmelCase						)

							# verify the logits
							_lowerCamelCase  :    Optional[Any]					=					torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape						,   _lowerCAmelCase						)

							_lowerCamelCase  :    Optional[Any]					=					torch.tensor([-1.90_90, -0.49_93, -0.23_89]						).to(_lowerCAmelCase						)

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






			@slow
			@require_accelerate
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'						)
							_lowerCamelCase  :    str					=					ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'						,   device_map='auto'						)

							_lowerCamelCase  :    Tuple					=					prepare_img()

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

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





"""simple docstring"""






from math import pow, sqrt







def   _snake_case    (       *lowercase__	):
				_lowerCamelCase  :    Dict					=					len(lowercase__	) > 0 and all(value > 0.0 for value in values	)
				return result







def   _snake_case    (       lowercase__							,					lowercase__	):
				return (
				    round(sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__	)
				    else ValueError('Input Error: Molar mass values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(molar_mass / pow(effusion_rate_a / effusion_rate_a							,					2	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(pow(effusion_rate_a / effusion_rate_a							,					2	) / molar_mass							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)