






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 							SCREAMING_SNAKE_CASE__						:



					def __init__( self					:  Tuple							,		__A					:  Any							,		__A					:  List[str]=1_3							,		__A					:  List[Any]=3_0							,		__A					:  Optional[Any]=2							,		__A					:  Union[str, Any]=3							,		__A					:  Optional[Any]=True							,		__A					:  Tuple=True							,		__A					:  Dict=3_2							,		__A					:  Dict=2							,		__A					:  Tuple=4							,		__A					:  Optional[Any]=3_7							,		__A					:  Union[str, Any]="gelu"							,		__A					:  Optional[int]=0.1							,		__A					:  Dict=0.1							,		__A					:  str=1_0							,		__A					:  Dict=0.0_2							,		__A					:  List[Any]=3							,		__A					:  str=None							,		):
						snake_case__			:				Union[str, Any]       =  parent
						snake_case__			:				Dict       =  batch_size
						snake_case__			:				Optional[int]       =  image_size
						snake_case__			:				List[Any]       =  patch_size
						snake_case__			:				Optional[Any]       =  num_channels
						snake_case__			:				List[Any]       =  is_training
						snake_case__			:				Union[str, Any]       =  use_labels
						snake_case__			:				List[Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				Optional[int]       =  num_attention_heads
						snake_case__			:				str       =  intermediate_size
						snake_case__			:				Dict       =  hidden_act
						snake_case__			:				Union[str, Any]       =  hidden_dropout_prob
						snake_case__			:				Union[str, Any]       =  attention_probs_dropout_prob
						snake_case__			:				Optional[int]       =  type_sequence_label_size
						snake_case__			:				Optional[Any]       =  initializer_range
						snake_case__			:				Any       =  scope

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



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Any       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						snake_case__			:				Union[str, Any]       =  None
						if self.use_labels:
							snake_case__			:				int       =  ids_tensor([self.batch_size]							,		self.type_sequence_label_size	)

						snake_case__			:				List[Any]       =  self.get_config()

						return config, pixel_values, labels



					def 	_lowercase					( self					:  List[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=__A							,		initializer_range=self.initializer_range							,		)



					def 	_lowercase					( self					:  List[str]							,		__A					:  Tuple							,		__A					:  Optional[int]							,		__A					:  int	):
						snake_case__			:				str       =  TFViTModel(config=__A	)
						snake_case__			:				Optional[int]       =  model(__A							,		training=__A	)
						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__			:				int       =  self.image_size // 2
						snake_case__			:				Optional[int]       =  pixel_values[:, :, :image_size, :image_size]
						snake_case__			:				str       =  model(__A							,		interpolate_pos_encoding=__A							,		training=__A	)
						snake_case__			:				int       =  (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]							,		__A					:  str							,		__A					:  List[str]							,		__A					:  List[Any]	):
						snake_case__			:				Optional[int]       =  self.type_sequence_label_size
						snake_case__			:				str       =  TFViTForImageClassification(__A	)
						snake_case__			:				str       =  model(__A							,		labels=__A							,		training=__A	)
						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__			:				Dict       =  self.image_size // 2
						snake_case__			:				Union[str, Any]       =  pixel_values[:, :, :image_size, :image_size]
						snake_case__			:				List[Any]       =  model(__A							,		interpolate_pos_encoding=__A							,		training=__A	)
						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(__A	)
						snake_case__			:				Optional[Any]       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
						snake_case__			:				int       =  model(__A	)
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.type_sequence_label_size)	)






					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Optional[int]       =  self.prepare_config_and_inputs()
						snake_case__			:				Dict       =  config_and_inputs
						snake_case__			:				str       =  {"pixel_values": pixel_values}
						return config, inputs_dict



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

					a_     =		False
					a_     =		False
					a_     =		False



					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				List[str]       =  TFViTModelTester(self	)
						snake_case__			:				Optional[Any]       =  ConfigTester(self							,		config_class=__A							,		has_text_modality=__A							,		hidden_size=3_7	)



					def 	_lowercase					( self					:  Any	):
						self.config_tester.run_common_tests()



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



					@unittest.skip(reason="ViT does not use inputs_embeds"	)
					def 	_lowercase					( self					:  str	):
						pass



					def 	_lowercase					( self					:  str	):
						snake_case__			:				str       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				Any       =  model_class(__A	)
							self.assertIsInstance(model.get_input_embeddings()							,		(tf.keras.layers.Layer)	)
							snake_case__			:				Optional[int]       =  model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__A							,		tf.keras.layers.Layer	)	)



					def 	_lowercase					( self					:  Any	):
						snake_case__			:				Dict       =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__			:				Dict       =  model_class(__A	)
							snake_case__			:				Dict       =  inspect.signature(model.call	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case__			:				Optional[int]       =  [*signature.parameters.keys()]

							snake_case__			:				str       =  ["pixel_values"]
							self.assertListEqual(arg_names[:1]							,		__A	)



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Union[str, Any]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A	)



					def 	_lowercase					( self					:  int	):
						snake_case__			:				int       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__A	)






					@slow
					def 	_lowercase					( self					:  int	):
						snake_case__			:				List[Any]       =  TFViTModel.from_pretrained("google/vit-base-patch16-224"	)
						self.assertIsNotNone(__A	)







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				List[Any]       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
	return image





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



					@cached_property
					def 	_lowercase					( self					:  Optional[int]	):
						return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"	) if is_vision_available() else None






					@slow
					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Optional[Any]       =  TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"	)

						snake_case__			:				Optional[int]       =  self.default_image_processor
						snake_case__			:				Dict       =  prepare_img()
						snake_case__			:				Optional[int]       =  image_processor(images=__A							,		return_tensors="tf"	)

						# forward pass
						snake_case__			:				str       =  model(**__A	)

						# verify the logits
						snake_case__			:				Optional[Any]       =  tf.TensorShape((1, 1_0_0_0)	)
						self.assertEqual(outputs.logits.shape							,		__A	)

						snake_case__			:				Optional[int]       =  tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]	)

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











__lowerCamelCase :      Optional[int]										=				"""Tobias Carryer"""

from time import time



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  List[str]							,		__A					:  Dict=int(time()	)	):  # noqa: B008
						snake_case__			:				List[Any]       =  multiplier
						snake_case__			:				Optional[int]       =  increment
						snake_case__			:				Optional[int]       =  modulo
						snake_case__			:				Union[str, Any]       =  seed






					def 	_lowercase					( self					:  str	):
						snake_case__			:				Union[str, Any]       =  (self.multiplier * self.seed + self.increment) % self.modulo
						return self.seed


if __name__ == "__main__":
	# Show the LCG in action.
	__lowerCamelCase :      int										=				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
	while True:
		print(lcg.next_number())



