
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A	:		Dict    =       logging.get_logger(__name__)

A	:		str    =       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''data2vec-text'''

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						Any=3_0522				, _UpperCAmelCase   :						Dict=768				, _UpperCAmelCase   :						List[str]=12				, _UpperCAmelCase   :						Optional[int]=12				, _UpperCAmelCase   :						Union[str, Any]=3072				, _UpperCAmelCase   :						str="gelu"				, _UpperCAmelCase   :						Optional[int]=0.1				, _UpperCAmelCase   :						List[str]=0.1				, _UpperCAmelCase   :						Tuple=512				, _UpperCAmelCase   :						str=2				, _UpperCAmelCase   :						Optional[int]=0.02				, _UpperCAmelCase   :						Tuple=1E-1_2				, _UpperCAmelCase   :						Any=1				, _UpperCAmelCase   :						Optional[int]=0				, _UpperCAmelCase   :						Optional[Any]=2				, _UpperCAmelCase   :						Tuple="absolute"				, _UpperCAmelCase   :						Any=True				, _UpperCAmelCase   :						int=None				, **_UpperCAmelCase   :						List[str]				, )						->       Tuple:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase				, bos_token_id=_UpperCAmelCase				, eos_token_id=_UpperCAmelCase				, **_UpperCAmelCase		)

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						int		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)





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 							A   :
					'''simple docstring'''






					def __init__(self   :						List[str]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Any=13				, _UpperCAmelCase   :						int=64				, _UpperCAmelCase   :						Any=2				, _UpperCAmelCase   :						str=3				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						List[Any]=True				, _UpperCAmelCase   :						Optional[int]=32				, _UpperCAmelCase   :						Tuple=5				, _UpperCAmelCase   :						Tuple=4				, _UpperCAmelCase   :						Union[str, Any]=37				, _UpperCAmelCase   :						Tuple="gelu"				, _UpperCAmelCase   :						Optional[int]=0.1				, _UpperCAmelCase   :						int=0.1				, _UpperCAmelCase   :						Any=10				, _UpperCAmelCase   :						str=0.02				, _UpperCAmelCase   :						Any=[1, 16, 4, 4]				, _UpperCAmelCase   :						str=None				, )						->       int:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		patch_size
							lowercase__					      =		num_channels
							lowercase__					      =		is_training
							lowercase__					      =		use_labels
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_act
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		type_sequence_label_size
							lowercase__					      =		initializer_range
							lowercase__					      =		scope
							lowercase__					      =		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
							lowercase__					      =		(self.image_size // 32) ** 2
							lowercase__					      =		num_patches + 1

					def   lowerCamelCase__					(self   :						Any		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							lowercase__					      =		None
							if self.use_labels:
									lowercase__					      =		ids_tensor([self.batch_size]				, self.type_sequence_label_size		)

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		{
							    """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   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		ViTHybridModel(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)
							self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						int		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		self.type_sequence_label_size
							lowercase__					      =		ViTHybridForImageClassification(_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase				, labels=_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)		)


					def   lowerCamelCase__					(self   :						int		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__					      =		config_and_inputs
							lowercase__					      =		{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






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

					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ViTHybridModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, has_text_modality=_UpperCAmelCase				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						Dict		)						->       List[str]:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					@unittest.skip(reason="""ViT does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						List[str]		)						->       Any:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Dict		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

					def   lowerCamelCase__					(self   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__					      =		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Tuple		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Dict		)						->       Any:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__					      =		_config_zero_init(_UpperCAmelCase		)
							for model_class in self.all_model_classes:
									lowercase__					      =		model_class(config=_UpperCAmelCase		)
									# Skip the check for the backbone
									for name, module in model.named_modules():
											if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
													lowercase__					      =		[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   lowerCamelCase__					(self   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__					      =		ViTHybridModel.from_pretrained(_UpperCAmelCase		)
									self.assertIsNotNone(_UpperCAmelCase		)






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




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







@require_torch
@require_vision
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@cached_property
					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Union[str, Any]:




							"""simple docstring"""


							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
							    if is_vision_available()
							    else None
							)

					@slow
					def   lowerCamelCase__					(self   :						List[Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
							    _UpperCAmelCase		)

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(images=_UpperCAmelCase				, return_tensors="""pt"""		).to(_UpperCAmelCase		)

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**_UpperCAmelCase		)

							# verify the logits
							lowercase__					      =		torch.Size((1, 1000)		)
							self.assertEqual(outputs.logits.shape				, _UpperCAmelCase		)

							lowercase__					      =		torch.tensor([-1.9_090, -0.4_993, -0.2_389]		).to(_UpperCAmelCase		)

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


					@slow
					@require_accelerate
					def   lowerCamelCase__					(self   :						Any		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""		)
							lowercase__					      =		ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""				, device_map="""auto"""		)

							lowercase__					      =		prepare_img()

							lowercase__					      =		image_processor(images=_UpperCAmelCase				, return_tensors="""pt"""		)
							lowercase__					      =		model(**_UpperCAmelCase		)
							lowercase__					      =		outputs.logits
							# model predicts one of the 1000 ImageNet classes
							lowercase__					      =		logits.argmax(-1		).item()

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

