




'''simple docstring'''







import copy
import os
from typing import Union

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


A									=logging.get_logger(__name__)

A									={
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}


class     _a						(			__a				):
       __a       :  Dict            =      """blip_text_model"""
       def __init__(			self		:			Union[str, Any]     ,       lowercase		:			Optional[Any]=30_524     ,       lowercase		:			Optional[int]=768     ,       lowercase		:			Any=768     ,       lowercase		:			Optional[int]=3_072     ,       lowercase		:			int=768     ,       lowercase		:			Any=12     ,       lowercase		:			Dict=8     ,       lowercase		:			List[Any]=512     ,       lowercase		:			Any="gelu"     ,       lowercase		:			Tuple=1E-12     ,       lowercase		:			str=0.0     ,       lowercase		:			Optional[Any]=0.0     ,       lowercase		:			int=0.02     ,       lowercase		:			Dict=30_522     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			Optional[int]=0     ,       lowercase		:			Optional[int]=102     ,       lowercase		:			List[Any]=True     ,       lowercase		:			Optional[Any]=True     ,       **lowercase		:			Any     ,       ):



           '''simple docstring'''

           super().__init__(
               pad_token_id=lowercase     ,       bos_token_id=lowercase     ,       eos_token_id=lowercase     ,       sep_token_id=lowercase     ,       **lowercase     ,       )

           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	encoder_hidden_size
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	projection_dim
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	layer_norm_eps
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	is_decoder
           UpperCAmelCase     =	use_cache







       @classmethod
       def 					A   (			cls		:			int     ,       lowercase		:			Union[str, os.PathLike]     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           cls._set_token_in_kwargs(lowercase       )

           UpperCAmelCase ,  UpperCAmelCase     =	cls.get_config_dict(lowercase     ,       **lowercase       )

           # get the text config dict if we are loading from BlipConfig
           if config_dict.get('''model_type'''       ) == "blip":
               UpperCAmelCase     =	config_dict['''text_config''']

           if "model_type" in config_dict and hasattr(cls     ,       '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                   f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

           return cls.from_dict(lowercase     ,       **lowercase       )






class     _a						(			__a				):
       __a       :  List[Any]            =      """blip_vision_model"""
       def __init__(			self		:			Any     ,       lowercase		:			int=768     ,       lowercase		:			Optional[int]=3_072     ,       lowercase		:			List[Any]=512     ,       lowercase		:			Optional[Any]=12     ,       lowercase		:			Optional[Any]=12     ,       lowercase		:			Union[str, Any]=384     ,       lowercase		:			Dict=16     ,       lowercase		:			Optional[int]="gelu"     ,       lowercase		:			Union[str, Any]=1E-5     ,       lowercase		:			str=0.0     ,       lowercase		:			str=1E-10     ,       **lowercase		:			Tuple     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )

           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	projection_dim
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	patch_size
           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	attention_dropout
           UpperCAmelCase     =	layer_norm_eps
           UpperCAmelCase     =	hidden_act







       @classmethod
       def 					A   (			cls		:			Tuple     ,       lowercase		:			Union[str, os.PathLike]     ,       **lowercase		:			str       ):



           '''simple docstring'''

           cls._set_token_in_kwargs(lowercase       )

           UpperCAmelCase ,  UpperCAmelCase     =	cls.get_config_dict(lowercase     ,       **lowercase       )

           # get the vision config dict if we are loading from BlipConfig
           if config_dict.get('''model_type'''       ) == "blip":
               UpperCAmelCase     =	config_dict['''vision_config''']

           if "model_type" in config_dict and hasattr(cls     ,       '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                   f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

           return cls.from_dict(lowercase     ,       **lowercase       )






class     _a						(			__a				):
       __a       :  Tuple            =      """blip"""
       __a       :  str            =      True
       def __init__(			self		:			Union[str, Any]     ,       lowercase		:			Tuple=None     ,       lowercase		:			Any=None     ,       lowercase		:			str=512     ,       lowercase		:			str=2.6592     ,       lowercase		:			Union[str, Any]=256     ,       **lowercase		:			Union[str, Any]     ,       ):



           '''simple docstring'''

           super().__init__(**lowercase       )

           if text_config is None:
               UpperCAmelCase     =	{}
               logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''       )

           if vision_config is None:
               UpperCAmelCase     =	{}
               logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''       )

           UpperCAmelCase     =	BlipTextConfig(**lowercase       )
           UpperCAmelCase     =	BlipVisionConfig(**lowercase       )

           UpperCAmelCase     =	self.vision_config.hidden_size

           UpperCAmelCase     =	projection_dim
           UpperCAmelCase     =	logit_scale_init_value
           UpperCAmelCase     =	1.0
           UpperCAmelCase     =	0.02
           UpperCAmelCase     =	image_text_hidden_size
       @classmethod
       def 					A   (			cls		:			Optional[int]     ,       lowercase		:			BlipTextConfig     ,       lowercase		:			BlipVisionConfig     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           return cls(text_config=text_config.to_dict()     ,       vision_config=vision_config.to_dict()     ,       **lowercase       )







       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	copy.deepcopy(self.__dict__       )
           UpperCAmelCase     =	self.text_config.to_dict()
           UpperCAmelCase     =	self.vision_config.to_dict()
           UpperCAmelCase     =	self.__class__.model_type
           return output










'''simple docstring'''







import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )


class     _a						:
       def __init__(			self		:			List[str]     ,       lowercase		:			Dict     ,       lowercase		:			List[Any]=13     ,       lowercase		:			Optional[Any]=7     ,       lowercase		:			Any=True     ,       lowercase		:			str=True     ,       lowercase		:			List[Any]=True     ,       lowercase		:			str=True     ,       lowercase		:			List[str]=99     ,       lowercase		:			int=64     ,       lowercase		:			List[Any]=32     ,       lowercase		:			str=5     ,       lowercase		:			Optional[int]=4     ,       lowercase		:			int=37     ,       lowercase		:			str="gelu"     ,       lowercase		:			Any=0.1     ,       lowercase		:			Optional[Any]=0.1     ,       lowercase		:			Optional[int]=512     ,       lowercase		:			Union[str, Any]=16     ,       lowercase		:			List[str]=2     ,       lowercase		:			Tuple=0.02     ,       lowercase		:			List[Any]=3     ,       lowercase		:			int=4     ,       lowercase		:			Optional[Any]=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_input_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	embedding_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     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	num_choices
           UpperCAmelCase     =	scope
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

           UpperCAmelCase     =	None
           if self.use_input_mask:
               UpperCAmelCase     =	random_attention_mask([self.batch_size, self.seq_length]       )

           UpperCAmelCase     =	None
           if self.use_token_type_ids:
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.type_vocab_size       )

           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           if self.use_labels:
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.type_sequence_label_size       )
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels       )
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.num_choices       )

           UpperCAmelCase     =	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return MobileBertConfig(
               vocab_size=self.vocab_size     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       embedding_size=self.embedding_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       max_position_embeddings=self.max_position_embeddings     ,       type_vocab_size=self.type_vocab_size     ,       is_decoder=lowercase     ,       initializer_range=self.initializer_range     ,       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[int]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertModel(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase       )

           self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)       )
           self.parent.assertEqual(result.pooler_output.shape     ,       (self.batch_size, self.hidden_size)       )
       def 					A   (			self		:			Dict     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[str]     ,       lowercase		:			List[str]     ,       lowercase		:			Dict     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForMaskedLM(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
       def 					A   (			self		:			Dict     ,       lowercase		:			Any     ,       lowercase		:			Dict     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForNextSentencePrediction(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase     ,       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, 2)       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForPreTraining(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase     ,       next_sentence_label=lowercase     ,       )
           self.parent.assertEqual(result.prediction_logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
           self.parent.assertEqual(result.seq_relationship_logits.shape     ,       (self.batch_size, 2)       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Any     ,       lowercase		:			Dict     ,       lowercase		:			str     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			int     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertForQuestionAnswering(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       start_positions=lowercase     ,       end_positions=lowercase     ,       )
           self.parent.assertEqual(result.start_logits.shape     ,       (self.batch_size, self.seq_length)       )
           self.parent.assertEqual(result.end_logits.shape     ,       (self.batch_size, self.seq_length)       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			List[str]     ,       lowercase		:			str     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	MobileBertForSequenceClassification(lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	MobileBertForTokenClassification(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)       )
       def 					A   (			self		:			int     ,       lowercase		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			List[Any]     ,       lowercase		:			Tuple     ,       lowercase		:			str     ,       lowercase		:			Tuple     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_choices
           UpperCAmelCase     =	MobileBertForMultipleChoice(config=lowercase       )
           model.to(lowercase       )
           model.eval()
           UpperCAmelCase     =	input_ids.unsqueeze(1       ).expand(-1     ,       self.num_choices     ,       -1       ).contiguous()
           UpperCAmelCase     =	token_type_ids.unsqueeze(1       ).expand(-1     ,       self.num_choices     ,       -1       ).contiguous()
           UpperCAmelCase     =	input_mask.unsqueeze(1       ).expand(-1     ,       self.num_choices     ,       -1       ).contiguous()
           UpperCAmelCase     =	model(
               lowercase     ,       attention_mask=lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase     ,       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_choices)       )







       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  
           )     =	config_and_inputs
           UpperCAmelCase     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






@require_torch
class     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  Dict            =      (
           (
               MobileBertModel,
               MobileBertForMaskedLM,
               MobileBertForMultipleChoice,
               MobileBertForNextSentencePrediction,
               MobileBertForPreTraining,
               MobileBertForQuestionAnswering,
               MobileBertForSequenceClassification,
               MobileBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       __a       :  List[str]            =      (
           {
               """feature-extraction""": MobileBertModel,
               """fill-mask""": MobileBertForMaskedLM,
               """question-answering""": MobileBertForQuestionAnswering,
               """text-classification""": MobileBertForSequenceClassification,
               """token-classification""": MobileBertForTokenClassification,
               """zero-shot""": MobileBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __a       :  Any            =      True
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			Any     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	super()._prepare_for_class(lowercase     ,       lowercase     ,       return_labels=lowercase       )

           if return_labels:
               if model_class in get_values(lowercase       ):
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=lowercase       )
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=lowercase       )
           return inputs_dict
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       hidden_size=37       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_model(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowercase       )
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_pretraining(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_question_answering(*lowercase       )
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowercase       )







       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mobilebert_for_token_classification(*lowercase       )






def 		snake_case_							(_a			:							List[Any]  ):
    return torch.tensor(
        _a					,     dtype=torch.long					,     device=_a					,     )


A									=1E-3







@require_torch
@require_sentencepiece
@require_tokenizers
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileBertModel.from_pretrained('''google/mobilebert-uncased'''       ).to(lowercase       )
           UpperCAmelCase     =	_long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       )
           with torch.no_grad():
               UpperCAmelCase     =	model(lowercase       )[0]
           UpperCAmelCase     =	torch.Size((1, 9, 512)       )
           self.assertEqual(output.shape     ,       lowercase       )
           UpperCAmelCase     =	torch.tensor(
               [
                   [
                       [-2.4_736_526E07, 8.2_691_656E04, 1.6_521_838E05],
                       [-5.7_541_704E-01, 3.9_056_022E00, 4.4_011_507E00],
                       [2.6_047_359E00, 1.5_677_652E00, -1.7_324_188E-01],
                   ]
               ]     ,       device=lowercase     ,       )

           # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
           # ~1 difference, it's therefore not a good idea to measure using addition.
           # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
           # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
           UpperCAmelCase     =	torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE       )
           UpperCAmelCase     =	torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE       )

           self.assertTrue(lower_bound and upper_bound       )




