
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__=0.0_2      ,       lowerCAmelCase__=[1, 16, 4, 4]      ,       lowerCAmelCase__=None      ,       )							->				Union[str, Any]:
							__magic_name__      :       Union[str, Any]       						= parent
							__magic_name__      :       Any       						= batch_size
							__magic_name__      :       Optional[int]       						= image_size
							__magic_name__      :       int       						= patch_size
							__magic_name__      :       Tuple       						= num_channels
							__magic_name__      :       Tuple       						= is_training
							__magic_name__      :       Union[str, Any]       						= use_labels
							__magic_name__      :       str       						= hidden_size
							__magic_name__      :       Dict       						= num_hidden_layers
							__magic_name__      :       Optional[int]       						= num_attention_heads
							__magic_name__      :       int       						= intermediate_size
							__magic_name__      :       Optional[int]       						= hidden_act
							__magic_name__      :       Optional[Any]       						= hidden_dropout_prob
							__magic_name__      :       Tuple       						= attention_probs_dropout_prob
							__magic_name__      :       Union[str, Any]       						= type_sequence_label_size
							__magic_name__      :       Dict       						= initializer_range
							__magic_name__      :       Optional[int]       						= scope
							__magic_name__      :       Union[str, 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
							__magic_name__      :       List[Any]       						= (self.image_size // 32) ** 2
							__magic_name__      :       str       						= num_patches + 1
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Any       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

							__magic_name__      :       Optional[int]       						= self.get_config()

							return config, pixel_values, labels
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       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, 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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       str       						= ViTHybridModel(config=lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       Tuple       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       Tuple       						= self.type_sequence_label_size
							__magic_name__      :       Dict       						= ViTHybridForImageClassification(lowerCAmelCase__			)
							model.to(lowerCAmelCase__			)
							model.eval()
							__magic_name__      :       str       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Optional[int]       						= self.prepare_config_and_inputs()
							__magic_name__      :       Optional[int]       						= config_and_inputs
							__magic_name__      :       List[str]       						= {"""pixel_values""": pixel_values}
							return config, inputs_dict





@require_torch
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[int]											=   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
	lowercase__						:					str											=   (
	    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
	    if is_torch_available()
	    else {}
	)
	lowercase__						:					List[Any]											=   False
	lowercase__						:					int											=   False
	lowercase__						:					Optional[Any]											=   False
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Optional[int]       						= ViTHybridModelTester(self			)
							__magic_name__      :       List[str]       						= ConfigTester(self      ,       config_class=lowerCAmelCase__      ,       has_text_modality=lowerCAmelCase__      ,       hidden_size=37			)
	def       __magic_name__    (    self			)							->				Tuple:
							self.config_tester.run_common_tests()
	@unittest.skip(reason="""ViT does not use inputs_embeds"""			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							pass
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       List[str]       						= model_class(lowerCAmelCase__			)
													self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)			)
													__magic_name__      :       Optional[int]       						= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__      ,       nn.Linear			)			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       str       						= self.model_tester.prepare_config_and_inputs_for_common()

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

													__magic_name__      :       List[Any]       						= ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Optional[int]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Dict       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Tuple       						= self.model_tester.prepare_config_and_inputs_for_common()

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



def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Any       						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image





@require_torch
@require_vision
class 	snake_case__ (							unittest.TestCase				):
	@cached_property
	def       __magic_name__    (    self			)							->				List[str]:
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
							    if is_vision_available()
							    else None
							)
	@slow
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       int       						= ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(
							    lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= self.default_image_processor
							__magic_name__      :       str       						= prepare_img()
							__magic_name__      :       List[Any]       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""pt"""			).to(lowerCAmelCase__			)

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

							# verify the logits
							__magic_name__      :       Tuple       						= torch.Size((1, 10_00)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       Any       						= torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]			).to(lowerCAmelCase__			)

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







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

							__magic_name__      :       Any       						= prepare_img()

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

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
