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 MobileViTImageProcessor






class 						A   (   unittest.TestCase   ):





							def __init__(      self				:							int     ,		__a				:							List[str]     ,		__a				:							Optional[int]=7     ,		__a				:							List[str]=3     ,		__a				:							Dict=1_8     ,		__a				:							Optional[int]=3_0     ,		__a				:							Optional[Any]=4_0_0     ,		__a				:							List[str]=True     ,		__a				:							Union[str, Any]=None     ,		__a				:							List[Any]=True     ,		__a				:							Optional[int]=None     ,		__a				:							int=True     ,		)							->	Tuple:
								__UpperCAmelCase							=			size if size is not None else {'''shortest_edge''': 2_0}
								__UpperCAmelCase							=			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			image_size
								__UpperCAmelCase							=			min_resolution
								__UpperCAmelCase							=			max_resolution
								__UpperCAmelCase							=			do_resize
								__UpperCAmelCase							=			size
								__UpperCAmelCase							=			do_center_crop
								__UpperCAmelCase							=			crop_size
								__UpperCAmelCase							=			do_flip_channel_order






							def       snake_case__						(      self				:							Union[str, Any]	)							->	Union[str, Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_flip_channel_order": self.do_flip_channel_order,
								}






@require_torch
@require_vision
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      MobileViTImageProcessor if is_vision_available() else None





							def       snake_case__						(      self				:							List[str]	)							->	int:
								__UpperCAmelCase							=			MobileViTImageProcessingTester(self	)





							@property
							def       snake_case__						(      self				:							Dict	)							->	Dict:
								return self.image_processor_tester.prepare_image_processor_dict()





							def       snake_case__						(      self				:							List[str]	)							->	Tuple:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(__a     ,		'''do_resize'''	)	)
								self.assertTrue(hasattr(__a     ,		'''size'''	)	)
								self.assertTrue(hasattr(__a     ,		'''do_center_crop'''	)	)
								self.assertTrue(hasattr(__a     ,		'''center_crop'''	)	)
								self.assertTrue(hasattr(__a     ,		'''do_flip_channel_order'''	)	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size     ,		{'''shortest_edge''': 2_0}	)
								self.assertEqual(image_processor.crop_size     ,		{'''height''': 1_8, '''width''': 1_8}	)

								__UpperCAmelCase							=			self.image_processing_class.from_dict(self.image_processor_dict     ,		size=4_2     ,		crop_size=8_4	)
								self.assertEqual(image_processor.size     ,		{'''shortest_edge''': 4_2}	)
								self.assertEqual(image_processor.crop_size     ,		{'''height''': 8_4, '''width''': 8_4}	)





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





							def       snake_case__						(      self				:							List[Any]	)							->	int:
								# Initialize image_processing
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								__UpperCAmelCase							=			prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=__a	)
								for image in image_inputs:
									self.assertIsInstance(__a     ,		Image.Image	)

								# Test not batched input
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(__a     ,		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       snake_case__						(      self				:							Optional[int]	)							->	List[str]:
								# Initialize image_processing
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								__UpperCAmelCase							=			prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=__a     ,		numpify=__a	)
								for image in image_inputs:
									self.assertIsInstance(__a     ,		np.ndarray	)

								# Test not batched input
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(__a     ,		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       snake_case__						(      self				:							Tuple	)							->	Union[str, Any]:
								# Initialize image_processing
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								__UpperCAmelCase							=			prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=__a     ,		torchify=__a	)
								for image in image_inputs:
									self.assertIsInstance(__a     ,		torch.Tensor	)

								# Test not batched input
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			image_processing(__a     ,		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'''],
								    )     ,		)

'''simple docstring'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
