
from collections import OrderedDict
from typing import Mapping

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


lowercase__				:				Any						=      logging.get_logger(__name__)

lowercase__				:				Any						=      {
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	'rembert'



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=250300  ,						SCREAMING_SNAKE_CASE_=1152  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=18  ,						SCREAMING_SNAKE_CASE_=256  ,						SCREAMING_SNAKE_CASE_=1664  ,						SCREAMING_SNAKE_CASE_=4608  ,						SCREAMING_SNAKE_CASE_="gelu"  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=0.0  ,						SCREAMING_SNAKE_CASE_=0.1  ,						SCREAMING_SNAKE_CASE_=512  ,						SCREAMING_SNAKE_CASE_=2  ,						SCREAMING_SNAKE_CASE_=0.0_2  ,						SCREAMING_SNAKE_CASE_=1E-12  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=0  ,						SCREAMING_SNAKE_CASE_=312  ,						SCREAMING_SNAKE_CASE_=313  ,						**SCREAMING_SNAKE_CASE_  ,						)-> int:





													'''simple docstring'''






													super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_  ,						bos_token_id=SCREAMING_SNAKE_CASE_  ,						eos_token_id=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      vocab_size
													__UpperCamelCase  	=      input_embedding_size
													__UpperCamelCase  	=      output_embedding_size
													__UpperCamelCase  	=      max_position_embeddings
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      intermediate_size
													__UpperCamelCase  	=      hidden_act
													__UpperCamelCase  	=      hidden_dropout_prob
													__UpperCamelCase  	=      attention_probs_dropout_prob
													__UpperCamelCase  	=      classifier_dropout_prob
													__UpperCamelCase  	=      initializer_range
													__UpperCamelCase  	=      type_vocab_size
													__UpperCamelCase  	=      layer_norm_eps
													__UpperCamelCase  	=      use_cache
													__UpperCamelCase  	=      False






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							@property
							def      A__		(	self      )-> Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






													if self.task == "multiple-choice":
																			__UpperCamelCase  	=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
													else:
																			__UpperCamelCase  	=      {0: '''batch''', 1: '''sequence'''}
													return OrderedDict(
													    [
													        ('''input_ids''', dynamic_axis),
													        ('''attention_mask''', dynamic_axis),
													        ('''token_type_ids''', dynamic_axis),
													    ]      )



							@property
							def      A__		(	self      )-> float:





													'''simple docstring'''






													return 1E-4










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 CLIPImageProcessor




class     A					(				unittest.TestCase       ):







							def __init__(    self   :	Any   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	List[Any]=7   ,     __UpperCAmelCase   :	Union[str, Any]=3   ,     __UpperCAmelCase   :	Optional[Any]=18   ,     __UpperCAmelCase   :	List[str]=30   ,     __UpperCAmelCase   :	int=400   ,     __UpperCAmelCase   :	List[Any]=True   ,     __UpperCAmelCase   :	Tuple=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Any=None   ,     __UpperCAmelCase   :	Optional[int]=True   ,     __UpperCAmelCase   :	List[str]=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     __UpperCAmelCase   :	List[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     __UpperCAmelCase   :	Dict=True   ,     )		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= size if size is not None else {'shortest_edge': 20}
										UpperCamelCase_     				= crop_size if crop_size is not None else {'height': 18, 'width': 18}
										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_normalize
										UpperCamelCase_     				= image_mean
										UpperCamelCase_     				= image_std
										UpperCamelCase_     				= do_convert_rgb







							def        lowercase__							(    self   :	Tuple				)		->					List[Any]:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	str=False   ,     __UpperCAmelCase   :	Tuple=False   ,     __UpperCAmelCase   :	Optional[int]=False				)		->					Any:

										"""simple docstring"""



										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
													UpperCamelCase_     				= []
													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:
													UpperCamelCase_     				= []
													for i in range(self.batch_size				):
																UpperCamelCase_				,   UpperCamelCase_     				= 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
													UpperCamelCase_     				= [Image.fromarray(np.moveaxis(__UpperCAmelCase   ,     0   ,     -1				)				) for x in image_inputs]

										if torchify:
													UpperCamelCase_     				= [torch.from_numpy(__UpperCAmelCase				) for x in image_inputs]

										return image_inputs







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		Any			  =       CLIPImageProcessor if is_vision_available() else None







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

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self				)







							@property
							def        lowercase__							(    self   :	Optional[int]				)		->					Optional[Any]:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Tuple				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	Optional[int]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 20}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 18, 'width': 18}				)

										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict   ,     size=42   ,     crop_size=84				)
										self.assertEqual(image_processor.size   ,     {'shortest_edge': 42}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 84, 'width': 84}				)







							def        lowercase__							(    self   :	Union[str, Any]				)		->					Optional[int]:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	int				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     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(__UpperCAmelCase   ,     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        lowercase__							(    self   :	Optional[Any]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										UpperCamelCase_     				= 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
										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(__UpperCAmelCase   ,     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        lowercase__							(    self   :	Optional[Any]				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										UpperCamelCase_     				= 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
										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(__UpperCAmelCase   ,     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'],
										    )   ,     )







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       CLIPImageProcessor if is_vision_available() else None







							def        lowercase__							(    self   :	str				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= CLIPImageProcessingTester(self   ,     num_channels=4				)
										UpperCamelCase_     				= 3







							@property
							def        lowercase__							(    self   :	Dict				)		->					str:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	Optional[int]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'image_std'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_convert_rgb'				)				)







							def        lowercase__							(    self   :	int				)		->					Any:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	Any				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										UpperCamelCase_     				= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     Image.Image				)

										# Test not batched input
										UpperCamelCase_     				= image_processing(image_inputs[0]   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )

										# Test batched
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,     (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )   ,     )
