



"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class       _a  :



			def __init__(			self							:				Dict   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Dict=13   ,	_lowercase							:				Optional[int]=30   ,	_lowercase							:				Dict=2   ,	_lowercase							:				int=3   ,	_lowercase							:				Union[str, Any]=True   ,	_lowercase							:				str=True   ,	_lowercase							:				str=32   ,	_lowercase							:				Optional[int]=2   ,	_lowercase							:				Tuple=4   ,	_lowercase							:				str=37   ,	_lowercase							:				str="gelu"   ,	_lowercase							:				Any=0.1   ,	_lowercase							:				int=0.1   ,	_lowercase							:				Tuple=10   ,	_lowercase							:				int=0.02   ,	_lowercase							:				Tuple=3   ,	_lowercase							:				Dict=None   ,	)							->					Tuple:
									snake_case   :			Any						    = parent
									snake_case   :			Any						    = batch_size
									snake_case   :			Optional[int]						    = image_size
									snake_case   :			List[Any]						    = patch_size
									snake_case   :			int						    = num_channels
									snake_case   :			Optional[Any]						    = is_training
									snake_case   :			int						    = use_labels
									snake_case   :			int						    = hidden_size
									snake_case   :			Optional[Any]						    = num_hidden_layers
									snake_case   :			str						    = num_attention_heads
									snake_case   :			Any						    = intermediate_size
									snake_case   :			List[str]						    = hidden_act
									snake_case   :			int						    = hidden_dropout_prob
									snake_case   :			str						    = attention_probs_dropout_prob
									snake_case   :			Tuple						    = type_sequence_label_size
									snake_case   :			Any						    = initializer_range
									snake_case   :			Dict						    = scope

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					str:
									snake_case   :			Tuple						    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									snake_case   :			str						    = None
									if self.use_labels:
															snake_case   :			List[str]						    = ids_tensor([self.batch_size]   ,	self.type_sequence_label_size		)

									snake_case   :			Tuple						    = self.get_config()

									return config, pixel_values, labels



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					str:
									return 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=_lowercase   ,	initializer_range=self.initializer_range   ,	)



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				Tuple   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Any		)							->					List[str]:
									snake_case   :			Optional[int]						    = TFViTModel(config=_lowercase		)
									snake_case   :			Optional[Any]						    = model(_lowercase   ,	training=_lowercase		)
									self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)		)

									# Test with an image with different size than the one specified in config.
									snake_case   :			Dict						    = self.image_size // 2
									snake_case   :			Any						    = pixel_values[:, :, :image_size, :image_size]
									snake_case   :			List[Any]						    = model(_lowercase   ,	interpolate_pos_encoding=_lowercase   ,	training=_lowercase		)
									snake_case   :			List[Any]						    = (image_size // self.patch_size) ** 2 + 1
									self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, seq_length, self.hidden_size)		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				int   ,	_lowercase							:				Optional[int]		)							->					Any:
									snake_case   :			Optional[Any]						    = self.type_sequence_label_size
									snake_case   :			List[Any]						    = TFViTForImageClassification(_lowercase		)
									snake_case   :			int						    = model(_lowercase   ,	labels=_lowercase   ,	training=_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)		)

									# Test with an image with different size than the one specified in config.
									snake_case   :			List[str]						    = self.image_size // 2
									snake_case   :			Any						    = pixel_values[:, :, :image_size, :image_size]
									snake_case   :			int						    = model(_lowercase   ,	interpolate_pos_encoding=_lowercase   ,	training=_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)		)

									# test greyscale images
									snake_case   :			List[Any]						    = 1
									snake_case   :			Any						    = TFViTForImageClassification(_lowercase		)
									snake_case   :			Dict						    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									snake_case   :			Optional[int]						    = model(_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)		)



			def 				__lowercase					(			self							:				Tuple		)							->					Any:
									snake_case   :			Union[str, Any]						    = self.prepare_config_and_inputs()
									snake_case    ,			snake_case    ,			snake_case   :			Union[str, Any]						    = config_and_inputs
									snake_case   :			str						    = {"pixel_values": pixel_values}
									return config, inputs_dict



@require_tf
class       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
			__magic_name__     					=     (
			    {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
			    if is_tf_available()
			    else {}
			)

			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False



			def 				__lowercase					(			self							:				Any		)							->					Optional[int]:
									snake_case   :			int						    = TFViTModelTester(self		)
									snake_case   :			List[str]						    = ConfigTester(self   ,	config_class=_lowercase   ,	has_text_modality=_lowercase   ,	hidden_size=37		)



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									self.config_tester.run_common_tests()



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



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



			def 				__lowercase					(			self							:				str		)							->					Optional[Any]:
									snake_case    ,			snake_case   :			Optional[int]						    = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case   :			List[str]						    = model_class(_lowercase		)
															self.assertIsInstance(model.get_input_embeddings()   ,	(tf.keras.layers.Layer)		)
															snake_case   :			Dict						    = model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(_lowercase   ,	tf.keras.layers.Layer		)		)



			def 				__lowercase					(			self							:				Optional[Any]		)							->					str:
									snake_case    ,			snake_case   :			Tuple						    = self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case   :			Union[str, Any]						    = ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,	_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]		)							->					Dict:
									snake_case   :			Union[str, Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase		)



			def 				__lowercase					(			self							:				Dict		)							->					Any:
									snake_case   :			Union[str, Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase		)



			@slow
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Optional[Any]						    = TFViTModel.from_pretrained("google/vit-base-patch16-224"		)
									self.assertIsNotNone(_lowercase		)



def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			List[str]						    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						return image

@require_tf
@require_vision
class       _a  ( unittest.TestCase):



			@cached_property
			def 				__lowercase					(			self							:				List[str]		)							->					List[Any]:
									return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"		) if is_vision_available() else None



			@slow
			def 				__lowercase					(			self							:				Dict		)							->					Optional[Any]:
									snake_case   :			Optional[Any]						    = TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"		)

									snake_case   :			List[str]						    = self.default_image_processor
									snake_case   :			int						    = prepare_img()
									snake_case   :			List[str]						    = image_processor(images=_lowercase   ,	return_tensors="tf"		)

									# forward pass
									snake_case   :			Union[str, Any]						    = model(**_lowercase		)

									# verify the logits
									snake_case   :			int						    = tf.TensorShape((1, 1000)		)
									self.assertEqual(outputs.logits.shape   ,	_lowercase		)

									snake_case   :			str						    = tf.constant([-0.2744, 0.8215, -0.0836]		)

									tf.debugging.assert_near(outputs.logits[0, :3]   ,	_lowercase   ,	atol=1E-4		)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A		=						{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['GPTSw3Tokenizer']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_gpt_swa import GPTSwaTokenizer

else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



