
"""simple docstring"""






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
					    MODEL_MAPPING,
					    DataaVecVisionForImageClassification,
					    DataaVecVisionForSemanticSegmentation,
					    DataaVecVisionModel,
					)
					from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor



class  lowercase				:






			def __init__(self     :  Any			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  List[str]=100			,SCREAMING_SNAKE_CASE_     :  Tuple=13			,SCREAMING_SNAKE_CASE_     :  Optional[int]=30			,SCREAMING_SNAKE_CASE_     :  Dict=2			,SCREAMING_SNAKE_CASE_     :  Dict=3			,SCREAMING_SNAKE_CASE_     :  Tuple=True			,SCREAMING_SNAKE_CASE_     :  str=True			,SCREAMING_SNAKE_CASE_     :  str=32			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]=4			,SCREAMING_SNAKE_CASE_     :  List[str]=4			,SCREAMING_SNAKE_CASE_     :  str=37			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]="gelu"			,SCREAMING_SNAKE_CASE_     :  Any=0.1			,SCREAMING_SNAKE_CASE_     :  Any=0.1			,SCREAMING_SNAKE_CASE_     :  Tuple=10			,SCREAMING_SNAKE_CASE_     :  Optional[int]=0.02			,SCREAMING_SNAKE_CASE_     :  Any=3			,SCREAMING_SNAKE_CASE_     :  Tuple=None			,SCREAMING_SNAKE_CASE_     :  Tuple=[0, 1, 2, 3]			,)					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     parent
				lowerCAmelCase				       =     100
				lowerCAmelCase				       =     batch_size
				lowerCAmelCase				       =     image_size
				lowerCAmelCase				       =     patch_size
				lowerCAmelCase				       =     num_channels
				lowerCAmelCase				       =     is_training
				lowerCAmelCase				       =     use_labels
				lowerCAmelCase				       =     hidden_size
				lowerCAmelCase				       =     num_hidden_layers
				lowerCAmelCase				       =     num_attention_heads
				lowerCAmelCase				       =     intermediate_size
				lowerCAmelCase				       =     hidden_act
				lowerCAmelCase				       =     hidden_dropout_prob
				lowerCAmelCase				       =     attention_probs_dropout_prob
				lowerCAmelCase				       =     type_sequence_label_size
				lowerCAmelCase				       =     initializer_range
				lowerCAmelCase				       =     scope
				lowerCAmelCase				       =     out_indices
				lowerCAmelCase				       =     num_labels

				# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				lowerCAmelCase				       =     (image_size // patch_size) ** 2
				lowerCAmelCase				       =     num_patches + 1






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





				"""simple docstring"""


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

				lowerCAmelCase				       =     None
				lowerCAmelCase				       =     None
				if self.use_labels:
					lowerCAmelCase				       =     ids_tensor([self.batch_size]			,self.type_sequence_label_size )
					lowerCAmelCase				       =     ids_tensor([self.batch_size, self.image_size, self.image_size]			,self.num_labels )

				lowerCAmelCase				       =     self.get_config()

				return config, pixel_values, labels, pixel_labels






			def 				UpperCAmelCase       (self     :  str )					->  List[Any]:





				"""simple docstring"""


				return DataaVecVisionConfig(
				    vocab_size=self.vocab_size			,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			,out_indices=self.out_indices			,)






			def 				UpperCAmelCase       (self     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  Tuple			,SCREAMING_SNAKE_CASE_     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  int )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     DataaVecVisionModel(config=SCREAMING_SNAKE_CASE_ )
				model.to(SCREAMING_SNAKE_CASE_ )
				model.eval()
				lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_ )
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				lowerCAmelCase				       =     (self.image_size // self.patch_size) ** 2
				self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, num_patches + 1, self.hidden_size) )






			def 				UpperCAmelCase       (self     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  str			,SCREAMING_SNAKE_CASE_     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.type_sequence_label_size
				lowerCAmelCase				       =     DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_ )
				model.to(SCREAMING_SNAKE_CASE_ )
				model.eval()
				lowerCAmelCase				       =     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     :  Any			,SCREAMING_SNAKE_CASE_     :  Tuple			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Tuple			,SCREAMING_SNAKE_CASE_     :  List[Any] )					->  str:





				"""simple docstring"""


				lowerCAmelCase				       =     self.num_labels
				lowerCAmelCase				       =     DataaVecVisionForSemanticSegmentation(SCREAMING_SNAKE_CASE_ )
				model.to(SCREAMING_SNAKE_CASE_ )
				model.eval()
				lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_ )
				self.parent.assertEqual(
				    result.logits.shape			,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
				lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,labels=SCREAMING_SNAKE_CASE_ )
				self.parent.assertEqual(
				    result.logits.shape			,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )






			def 				UpperCAmelCase       (self     :  List[Any] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.prepare_config_and_inputs()
				lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     config_and_inputs
				lowerCAmelCase				       =     {'''pixel_values''': pixel_values}
				return config, inputs_dict





@require_torch
class  lowercase				(   lowercase__      ,lowercase__      ,unittest.TestCase		):
			lowercase			     =    (
			    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			lowercase			     =    (
			    {
			        '''feature-extraction''': DataaVecVisionModel,
			        '''image-classification''': DataaVecVisionForImageClassification,
			        '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowercase			     =    False
			lowercase			     =    False
			lowercase			     =    False






			def 				UpperCAmelCase       (self     :  Any )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     DataaVecVisionModelTester(self )
				lowerCAmelCase				       =     ConfigTester(
				    self			,config_class=SCREAMING_SNAKE_CASE_			,has_text_modality=SCREAMING_SNAKE_CASE_			,hidden_size=37 )






			def 				UpperCAmelCase       (self     :  List[Any] )					->  int:





				"""simple docstring"""


				self.config_tester.run_common_tests()






			def 				UpperCAmelCase       (self     :  List[Any] )					->  int:





				"""simple docstring"""


				pass






			@require_torch_multi_gpu
			@unittest.skip(
			    reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
			def 				UpperCAmelCase       (self     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				pass






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





				"""simple docstring"""


				lowerCAmelCase       ,					lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs_for_common()

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






			def 				UpperCAmelCase       (self     :  Any )					->  List[Any]:





				"""simple docstring"""


				lowerCAmelCase       ,					lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCAmelCase				       =     ['''pixel_values''']
					self.assertListEqual(arg_names[:1]			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  List[str] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[int] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_segmentation(*SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Tuple )					->  Any:





				"""simple docstring"""


				if not self.model_tester.is_training:
					return

				lowerCAmelCase       ,					lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase				       =     True

				for model_class in self.all_model_classes:
					if model_class in [*get_values(SCREAMING_SNAKE_CASE_ )]:
						continue

					lowerCAmelCase				       =     model_class(SCREAMING_SNAKE_CASE_ )
					model.to(SCREAMING_SNAKE_CASE_ )
					model.train()
					lowerCAmelCase				       =     self._prepare_for_class(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,return_labels=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     model(**SCREAMING_SNAKE_CASE_ ).loss
					loss.backward()






			def 				UpperCAmelCase       (self     :  Optional[int] )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase       ,					lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
					return

				lowerCAmelCase				       =     False
				lowerCAmelCase				       =     True

				for model_class in self.all_model_classes:
					if model_class in [*get_values(SCREAMING_SNAKE_CASE_ )] or not model_class.supports_gradient_checkpointing:
						continue
					# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
					# this can then be incorporated into _prepare_for_class in test_modeling_common.py
					elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
						lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     inputs_dict['''pixel_values'''].shape
						lowerCAmelCase				       =     torch.zeros(
						    [self.model_tester.batch_size, height, width]			,device=SCREAMING_SNAKE_CASE_ ).long()
					lowerCAmelCase				       =     model_class(SCREAMING_SNAKE_CASE_ )
					model.gradient_checkpointing_enable()
					model.to(SCREAMING_SNAKE_CASE_ )
					model.train()
					lowerCAmelCase				       =     self._prepare_for_class(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,return_labels=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     model(**SCREAMING_SNAKE_CASE_ ).loss
					loss.backward()






			def 				UpperCAmelCase       (self     :  int )					->  str:





				"""simple docstring"""


				lowerCAmelCase       ,					lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs_for_common()

				lowerCAmelCase				       =     _config_zero_init(SCREAMING_SNAKE_CASE_ )
				for model_class in self.all_model_classes:
					lowerCAmelCase				       =     model_class(config=SCREAMING_SNAKE_CASE_ )
					for name, param in model.named_parameters():
						# we skip lambda parameters as these require special initial values
						# determined by config.layer_scale_init_value
						if "lambda" in name:
							continue
						if param.requires_grad:
							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"""			,)






			def 				UpperCAmelCase       (self     :  Tuple			,SCREAMING_SNAKE_CASE_     :  str			,SCREAMING_SNAKE_CASE_     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  List[str]=2e-4			,SCREAMING_SNAKE_CASE_     :  int="outputs"			,SCREAMING_SNAKE_CASE_     :  str=None )					->  List[str]:





				"""simple docstring"""


				super().check_pt_tf_outputs(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )






			@slow
			def 				UpperCAmelCase       (self     :  List[str] )					->  List[Any]:





				"""simple docstring"""


				for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase				       =     DataaVecVisionModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )





def __magic_name__  (							)				->	Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase				       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
	return image



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






			@cached_property
			def 				UpperCAmelCase       (self     :  Dict )					->  Dict:





				"""simple docstring"""


				return (
				    BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ) if is_vision_available() else None
				)






			@slow
			def 				UpperCAmelCase       (self     :  int )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ).to(
				    SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     self.default_image_processor
				lowerCAmelCase				       =     prepare_img()
				lowerCAmelCase				       =     image_processor(images=SCREAMING_SNAKE_CASE_			,return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE_ )

				# forward pass
				with torch.no_grad():
					lowerCAmelCase				       =     model(**SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     outputs.logits

				# verify the logits
				lowerCAmelCase				       =     torch.Size((1, 1_000) )
				self.assertEqual(logits.shape			,SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     torch.tensor([0.32_77, -0.13_95, 0.09_11] ).to(SCREAMING_SNAKE_CASE_ )

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

				lowerCAmelCase				       =     [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
				self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist()			,SCREAMING_SNAKE_CASE_ )




"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase          =							TypeVar("""T""")



class  lowercase				(   lowercase__		):
			pass





class  lowercase				(   lowercase__		):
			pass





class  lowercase				(   Generic[T]		):






			def __init__(self     :  Any			,SCREAMING_SNAKE_CASE_     :  int = 10 )					->  Any:





				"""simple docstring"""


				lowerCAmelCase				       =     []
				lowerCAmelCase				       =     limit






			def __bool__(self     :  List[Any] )					->  bool:





				"""simple docstring"""


				return bool(self.stack )






			def __str__(self     :  Any )					->  str:





				"""simple docstring"""


				return str(self.stack )






			def 				UpperCAmelCase       (self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  T )					->  None:





				"""simple docstring"""


				if len(self.stack ) >= self.limit:
					raise StackOverflowError
				self.stack.append(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Tuple )					->  T:





				"""simple docstring"""


				if not self.stack:
					raise StackUnderflowError
				return self.stack.pop()






			def 				UpperCAmelCase       (self     :  int )					->  T:





				"""simple docstring"""


				if not self.stack:
					raise StackUnderflowError
				return self.stack[-1]






			def 				UpperCAmelCase       (self     :  Any )					->  bool:





				"""simple docstring"""


				return not bool(self.stack )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  bool:





				"""simple docstring"""


				return self.size() == self.limit






			def 				UpperCAmelCase       (self     :  Tuple )					->  int:





				"""simple docstring"""


				return len(self.stack )






			def __contains__(self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  T )					->  bool:





				"""simple docstring"""


				return item in self.stack





def __magic_name__  (							)				->	None:

	'''simple docstring'''




	lowerCAmelCase				       =     Stack(10 )
	assert bool(_lowerCamelCase ) is False
	assert stack.is_empty() is True
	assert stack.is_full() is False
	assert str(_lowerCamelCase ) == "[]"

	try:
		lowerCAmelCase				       =     stack.pop()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	try:
		lowerCAmelCase				       =     stack.peek()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	for i in range(10 ):
		assert stack.size() == i
		stack.push(_lowerCamelCase )

	assert bool(_lowerCamelCase )
	assert not stack.is_empty()
	assert stack.is_full()
	assert str(_lowerCamelCase ) == str(list(range(10 ) ) )
	assert stack.pop() == 9
	assert stack.peek() == 8

	stack.push(100 )
	assert str(_lowerCamelCase ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )

	try:
		stack.push(200 )
		raise AssertionError  # This should not happen
	except StackOverflowError:
		assert True  # This should happen

	assert not stack.is_empty()
	assert stack.size() == 10

	assert 5 in stack
	assert 55 not in stack


if __name__ == "__main__":
					test_stack()


