import unittest

import numpy as np

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

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 DPTImageProcessor





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase=7		,     lowercase=3		,     lowercase=1_8		,     lowercase=3_0		,     lowercase=4_0_0		,     lowercase=True		,     lowercase=None		,     lowercase=True		,     lowercase=[0.5, 0.5, 0.5]		,     lowercase=[0.5, 0.5, 0.5]		,     ):



						"""simple docstring"""





						A_		:    Tuple         =				size if size is not None else {'''height''': 1_8, '''width''': 1_8}
						A_		:    Tuple         =				parent
						A_		:    Optional[int]         =				batch_size
						A_		:    Optional[int]         =				num_channels
						A_		:    Tuple         =				image_size
						A_		:    Optional[Any]         =				min_resolution
						A_		:    Dict         =				max_resolution
						A_		:    str         =				do_resize
						A_		:    List[str]         =				size
						A_		:    int         =				do_normalize
						A_		:    Optional[Any]         =				image_mean
						A_		:    List[str]         =				image_std







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}





@require_torch
@require_vision
class UpperCAmelCase						( __A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = DPTImageProcessor if is_vision_available() else None
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				DPTImageProcessingTester(self )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return self.image_processor_tester.prepare_image_processor_dict()
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,     'image_mean' ) )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,     'image_std' ) )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,     'do_normalize' ) )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,     'do_resize' ) )
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		,     'size' ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size		,     {'height': 1_8, 'width': 1_8} )

						A_		:    Optional[int]         =				self.image_processing_class.from_dict(self.image_processor_dict		,     size=4_2 )
						self.assertEqual(image_processor.size		,     {'height': 4_2, 'width': 4_2} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						A_		:    List[Any]         =				prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_SCREAMING_SNAKE_CASE )
						for image in image_inputs:
								self.assertIsInstance(_SCREAMING_SNAKE_CASE		,     Image.Image )

						# Test not batched input
						A_		:    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.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,     )

						# Test batched
						A_		:    Dict         =				image_processing(_SCREAMING_SNAKE_CASE		,     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,     )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						A_		:    List[str]         =				prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_SCREAMING_SNAKE_CASE		,     numpify=_SCREAMING_SNAKE_CASE )
						for image in image_inputs:
								self.assertIsInstance(_SCREAMING_SNAKE_CASE		,     np.ndarray )

						# Test not batched input
						A_		:    List[str]         =				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.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,     )

						# Test batched
						A_		:    int         =				image_processing(_SCREAMING_SNAKE_CASE		,     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,     )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						A_		:    Any         =				prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_SCREAMING_SNAKE_CASE		,     torchify=_SCREAMING_SNAKE_CASE )
						for image in image_inputs:
								self.assertIsInstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor )

						# Test not batched input
						A_		:    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.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,     )

						# Test batched
						A_		:    List[str]         =				image_processing(_SCREAMING_SNAKE_CASE		,     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.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,     )

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''lilt'''
				def __init__(						self		,     lowercase=3_0_5_2_2		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=2		,     lowercase=0.02		,     lowercase=1E-12		,     lowercase=0		,     lowercase="absolute"		,     lowercase=None		,     lowercase=4		,     lowercase=1_0_2_4		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase		,     **lowercase )

						A_		:    str         =				vocab_size
						A_		:    Tuple         =				hidden_size
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    Any         =				num_attention_heads
						A_		:    Union[str, Any]         =				hidden_act
						A_		:    int         =				intermediate_size
						A_		:    int         =				hidden_dropout_prob
						A_		:    Union[str, Any]         =				attention_probs_dropout_prob
						A_		:    List[str]         =				max_position_embeddings
						A_		:    List[str]         =				type_vocab_size
						A_		:    Any         =				initializer_range
						A_		:    str         =				layer_norm_eps
						A_		:    Optional[int]         =				position_embedding_type
						A_		:    Union[str, Any]         =				classifier_dropout
						A_		:    Optional[Any]         =				channel_shrink_ratio
						A_		:    int         =				max_ad_position_embeddings






