



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a           =							logging.get_logger(__name__)

__a           =							{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     int		=	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
					_A						:     Optional[int]		=	"""mobilebert"""






					def __init__(			self:				int		,	snake_case:				str=30_522		,	snake_case:				List[Any]=512		,	snake_case:				Any=24		,	snake_case:				List[str]=4		,	snake_case:				Optional[Any]=512		,	snake_case:				List[str]="relu"		,	snake_case:				Optional[Any]=0.0		,	snake_case:				Tuple=0.1		,	snake_case:				Tuple=512		,	snake_case:				int=2		,	snake_case:				List[Any]=0.0_2		,	snake_case:				List[Any]=1E-12		,	snake_case:				int=0		,	snake_case:				List[str]=128		,	snake_case:				str=True		,	snake_case:				Any=True		,	snake_case:				Optional[int]=128		,	snake_case:				List[Any]=False		,	snake_case:				Optional[int]=True		,	snake_case:				List[Any]=4		,	snake_case:				List[str]="no_norm"		,	snake_case:				List[str]=True		,	snake_case:				Any=None		,	**snake_case:				Tuple		,	)  ->      str:
											super().__init__(pad_token_id=snake_case		,	**snake_case		)

											snake_case_					:Tuple		=     vocab_size
											snake_case_					:int		=     hidden_size
											snake_case_					:Optional[int]		=     num_hidden_layers
											snake_case_					:Optional[int]		=     num_attention_heads
											snake_case_					:Union[str, Any]		=     hidden_act
											snake_case_					:Optional[int]		=     intermediate_size
											snake_case_					:List[str]		=     hidden_dropout_prob
											snake_case_					:List[str]		=     attention_probs_dropout_prob
											snake_case_					:Dict		=     max_position_embeddings
											snake_case_					:int		=     type_vocab_size
											snake_case_					:Optional[Any]		=     initializer_range
											snake_case_					:str		=     layer_norm_eps
											snake_case_					:int		=     embedding_size
											snake_case_					:Any		=     trigram_input
											snake_case_					:List[str]		=     use_bottleneck
											snake_case_					:Any		=     intra_bottleneck_size
											snake_case_					:List[str]		=     use_bottleneck_attention
											snake_case_					:Union[str, Any]		=     key_query_shared_bottleneck
											snake_case_					:Optional[Any]		=     num_feedforward_networks
											snake_case_					:List[Any]		=     normalization_type
											snake_case_					:List[str]		=     classifier_activation

											if self.use_bottleneck:
																	snake_case_					:Optional[int]		=     intra_bottleneck_size
											else:
																	snake_case_					:Optional[int]		=     hidden_size

											snake_case_					:Any		=     classifier_dropout

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					@property
					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																	snake_case_					:List[Any]		=     {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																	snake_case_					:List[str]		=     {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ]		)








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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )