from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 				a_ (						__UpperCAmelCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					'mobilevit'




 def __init__(			self					:  str			,    lowercase__					:  List[Any]=3			,    lowercase__					:  Optional[Any]=256			,    lowercase__					:  Dict=2			,    lowercase__					:  Tuple=[144, 192, 240]			,    lowercase__					:  Dict=[16, 32, 64, 96, 128, 160, 640]			,    lowercase__					:  Dict=4			,    lowercase__					:  int=2.0			,    lowercase__					:  Union[str, Any]=4.0			,    lowercase__					:  List[Any]="silu"			,    lowercase__					:  Union[str, Any]=3			,    lowercase__					:  Tuple=32			,    lowercase__					:  List[Any]=0.1			,    lowercase__					:  int=0.0			,    lowercase__					:  Optional[int]=0.1			,    lowercase__					:  Any=0.02			,    lowercase__					:  Any=1e-5			,    lowercase__					:  Optional[Any]=True			,    lowercase__					:  List[str]=256			,    lowercase__					:  Any=[6, 12, 18]			,    lowercase__					:  str=0.1			,    lowercase__					:  List[str]=255			,    **lowercase__					:  Union[str, Any]			,    ):







        '''simple docstring'''



        super().__init__(**UpperCAmelCase_)

        lowerCAmelCase__     	=					num_channels
        lowerCAmelCase__     	=					image_size
        lowerCAmelCase__     	=					patch_size
        lowerCAmelCase__     	=					hidden_sizes
        lowerCAmelCase__     	=					neck_hidden_sizes
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					mlp_ratio
        lowerCAmelCase__     	=					expand_ratio
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					conv_kernel_size
        lowerCAmelCase__     	=					output_stride
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					classifier_dropout_prob
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					layer_norm_eps
        lowerCAmelCase__     	=					qkv_bias

        # decode head attributes for semantic segmentation
        lowerCAmelCase__     	=					aspp_out_channels
        lowerCAmelCase__     	=					atrous_rates
        lowerCAmelCase__     	=					aspp_dropout_prob
        lowerCAmelCase__     	=					semantic_loss_ignore_index






class 				a_ (						__UpperCAmelCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					version.parse('1.11'						)




 @property
 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})])




 @property
 def 		__snake_case						(			self					:  List[Any]):







        '''simple docstring'''



        if self.task == "image-classification":
               return OrderedDict([('logits', {0: 'batch'})])
        else:
               return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})])




 @property
 def 		__snake_case						(			self					:  Union[str, Any]):







        '''simple docstring'''



        return 1e-4


"""simple docstring"""

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRContextEncoderTokenizer
	lowercase_        =	DPRContextEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRQuestionEncoderTokenizer
	lowercase_        =	DPRQuestionEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRReaderTokenizer
	lowercase_        =	DPRReaderTokenizerFast
	lowercase_        =	True




	@slow
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			int		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    tokenizer.encode('text sequence ' * 4		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    [[101] + text_a + [102] + text_a + [102] + text_a]
			lowerCamelCase     :			List[str]		=    BatchEncoding({'input_ids': input_ids}					)

			lowerCamelCase     :			Optional[int]		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			str		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			List[Any]		=    [0]
			lowerCamelCase     :			Tuple		=    DPRReaderOutput(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    8, 9
			lowerCamelCase     :			Optional[int]		=    10
			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			List[Any]		=    tokenizer.decode_best_spans(UpperCAmelCase_		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].start_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].end_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].doc_id		, 0					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Union[str, Any]		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			List[str]		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('text sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    [101] + text_a + [102] + text_a + [102] + text_a
			lowerCamelCase     :			Optional[Any]		=    tokenizer(questions=['question sequence']		, titles=['title sequence']		, texts=['text sequence']					)
			self.assertIn('input_ids'		, UpperCAmelCase_					)
			self.assertIn('attention_mask'		, UpperCAmelCase_					)
			self.assertListEqual(encoded_input['input_ids'][0]		, UpperCAmelCase_					)






