





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_					:    Any      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Any      	=			{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='bert'




				def __init__(  self     :					Dict					,				__a     :					Dict=3_05_22					,				__a     :					int=7_68					,				__a     :					Any=12					,				__a     :					Tuple=12					,				__a     :					List[str]=30_72					,				__a     :					int="gelu"					,				__a     :					List[str]=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					str=5_12					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					int=1e-1_2					,				__a     :					Tuple=0					,				__a     :					Tuple="absolute"					,				__a     :					Optional[Any]=True					,				__a     :					Optional[Any]=None					,				**__a     :					List[Any]					,				):
								super().__init__(pad_token_id=__a					,				**__a					)

								_a								= vocab_size
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= hidden_act
								_a								= intermediate_size
								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= max_position_embeddings
								_a								= type_vocab_size
								_a								= initializer_range
								_a								= layer_norm_eps
								_a								= position_embedding_type
								_a								= use_cache
								_a								= classifier_dropout


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								if self.task == "multiple-choice":
												_a								= {0: "batch", 1: "choice", 2: "sequence"}
								else:
												_a								= {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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Dict=3 ,  __lowerCamelCase				:					int=224 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					Tuple=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Dict=[0.5, 0.5, 0.5] ,  __lowerCamelCase				:					List[Any]=[0.5, 0.5, 0.5] ,  ):
		UpperCamelCase     :List[Any]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Dict          =	num_channels
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :Dict          =	min_resolution
		UpperCamelCase     :str          =	max_resolution
		UpperCamelCase     :Union[str, Any]          =	do_resize
		UpperCamelCase     :Optional[Any]          =	size
		UpperCamelCase     :Any          =	do_normalize
		UpperCamelCase     :Optional[Any]          =	image_mean
		UpperCamelCase     :Tuple          =	image_std





	def 						_A (		self				:					int	):
		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     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  ViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					str	):
		UpperCamelCase     :Tuple          =	EfficientFormerImageProcessorTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_proc_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)





	def 						_A (		self				:					Optional[int]	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image_processor
		UpperCamelCase     :Union[str, Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Union[str, Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[str]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :List[Any]          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image_processor
		UpperCamelCase     :Optional[int]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :List[Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Dict          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[Any]	):
		# Initialize image_processor
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )
