"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
				import jax

				from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Any							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			int=13							,					UpperCAmelCase      :			Union[str, Any]=30							,					UpperCAmelCase      :			List[str]=2							,					UpperCAmelCase      :			str=3							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			Union[str, Any]=True							,					UpperCAmelCase      :			Dict=32							,					UpperCAmelCase      :			Union[str, Any]=5							,					UpperCAmelCase      :			str=4							,					UpperCAmelCase      :			Any=37							,					UpperCAmelCase      :			Union[str, Any]="gelu"							,					UpperCAmelCase      :			Optional[int]=0.1							,					UpperCAmelCase      :			Any=0.1							,					UpperCAmelCase      :			Dict=10							,					UpperCAmelCase      :			List[Any]=0.0_2							,					):
													__lowerCamelCase					:		Tuple		   =     parent
													__lowerCamelCase					:		str		   =     batch_size
													__lowerCamelCase					:		Optional[Any]		   =     image_size
													__lowerCamelCase					:		Any		   =     patch_size
													__lowerCamelCase					:		List[Any]		   =     num_channels
													__lowerCamelCase					:		Union[str, Any]		   =     is_training
													__lowerCamelCase					:		Union[str, Any]		   =     use_labels
													__lowerCamelCase					:		int		   =     hidden_size
													__lowerCamelCase					:		int		   =     num_hidden_layers
													__lowerCamelCase					:		int		   =     num_attention_heads
													__lowerCamelCase					:		Dict		   =     intermediate_size
													__lowerCamelCase					:		Optional[int]		   =     hidden_act
													__lowerCamelCase					:		Optional[int]		   =     hidden_dropout_prob
													__lowerCamelCase					:		List[str]		   =     attention_probs_dropout_prob
													__lowerCamelCase					:		Union[str, Any]		   =     type_sequence_label_size
													__lowerCamelCase					:		List[str]		   =     initializer_range

													# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
													__lowerCamelCase					:		List[Any]		   =     (image_size // patch_size) ** 2
													__lowerCamelCase					:		str		   =     num_patches + 1






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		Union[str, Any]		   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													__lowerCamelCase					:		Any		   =     ViTConfig(
													    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							,					)

													return config, pixel_values






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		List[Any]		   =     FlaxViTModel(config=UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     model(UpperCAmelCase    )
													# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
													__lowerCamelCase					:		Dict		   =     (self.image_size, self.image_size)
													__lowerCamelCase					:		List[Any]		   =     (self.patch_size, self.patch_size)
													__lowerCamelCase					:		Optional[int]		   =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
													self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, num_patches + 1, self.hidden_size)    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Dict    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.type_sequence_label_size
													__lowerCamelCase					:		Any		   =     FlaxViTForImageClassification(config=UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase    )
													self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)    )

													# test greyscale images
													__lowerCamelCase					:		List[str]		   =     1
													__lowerCamelCase					:		List[str]		   =     FlaxViTForImageClassification(UpperCAmelCase    )

													__lowerCamelCase					:		Any		   =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
													__lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Tuple		   =     self.prepare_config_and_inputs()
													(
													    (
													    __lowerCamelCase
													)   ,  (
													    __lowerCamelCase
													)   ,  
													)					:		Any		   =     config_and_inputs
													__lowerCamelCase					:		int		   =     {"pixel_values": pixel_values}
													return config, inputs_dict

@require_flax
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     FlaxViTModelTester(self    )
													__lowerCamelCase					:		str		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					has_text_modality=UpperCAmelCase							,					hidden_size=37    )






						def       lowerCamelCase__     (       self      :			str    ):
													self.config_tester.run_common_tests()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowerCamelCase					:		List[Any]		   =     model_class(UpperCAmelCase    )
																				__lowerCamelCase					:		Any		   =     inspect.signature(model.__call__    )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__lowerCamelCase					:		int		   =     [*signature.parameters.keys()]

																				__lowerCamelCase					:		List[Any]		   =     ["pixel_values"]
																				self.assertListEqual(arg_names[:1]							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__    ):
																											__lowerCamelCase					:		List[Any]		   =     self._prepare_for_class(UpperCAmelCase							,					UpperCAmelCase    )
																											__lowerCamelCase					:		List[Any]		   =     model_class(UpperCAmelCase    )

																											@jax.jit
																											def model_jitted(UpperCAmelCase      :			Optional[int]							,					**UpperCAmelCase      :			Dict    ):
																																		return model(pixel_values=UpperCAmelCase							,					**UpperCAmelCase    )

																											with self.subTest("JIT Enabled"    ):
																																		__lowerCamelCase					:		Any		   =     model_jitted(**UpperCAmelCase    ).to_tuple()

																											with self.subTest("JIT Disabled"    ):
																																		with jax.disable_jit():
																																									__lowerCamelCase					:		Dict		   =     model_jitted(**UpperCAmelCase    ).to_tuple()

																											self.assertEqual(len(UpperCAmelCase    )							,					len(UpperCAmelCase    )    )
																											for jitted_output, output in zip(UpperCAmelCase							,					UpperCAmelCase    ):
																																		self.assertEqual(jitted_output.shape							,					output.shape    )







						@slow
						def       lowerCamelCase__     (       self      :			Dict    ):
													for model_class_name in self.all_model_classes:
																				__lowerCamelCase					:		List[str]		   =     model_class_name.from_pretrained("google/vit-base-patch16-224"    )
																				__lowerCamelCase					:		Optional[Any]		   =     model(np.ones((1, 3, 224, 224)    )    )
																				self.assertIsNotNone(UpperCAmelCase    )
"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)