

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 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=True						,       a=True						,       a=True						,       a=99						,       a=64						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=16						,       a=2						,       a=0.02						,       a=3						,       a=4						,       a=None						,       ):
         lowercase__   :       str				=	parent
         lowercase__   :       Dict				=	batch_size
         lowercase__   :       str				=	seq_length
         lowercase__   :       List[Any]				=	is_training
         lowercase__   :       str				=	use_input_mask
         lowercase__   :       List[Any]				=	use_token_type_ids
         lowercase__   :       List[str]				=	use_labels
         lowercase__   :       List[Any]				=	vocab_size
         lowercase__   :       int				=	hidden_size
         lowercase__   :       Tuple				=	embedding_size
         lowercase__   :       Dict				=	num_hidden_layers
         lowercase__   :       int				=	num_attention_heads
         lowercase__   :       Union[str, Any]				=	intermediate_size
         lowercase__   :       str				=	hidden_act
         lowercase__   :       Optional[Any]				=	hidden_dropout_prob
         lowercase__   :       Any				=	attention_probs_dropout_prob
         lowercase__   :       Any				=	max_position_embeddings
         lowercase__   :       Dict				=	type_vocab_size
         lowercase__   :       Optional[Any]				=	type_sequence_label_size
         lowercase__   :       int				=	initializer_range
         lowercase__   :       int				=	num_labels
         lowercase__   :       List[str]				=	num_choices
         lowercase__   :       List[Any]				=	scope





    def 				snake_case_    (				self):
         lowercase__   :       int				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

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

         lowercase__   :       Dict				=	None
         if self.use_token_type_ids:
              lowercase__   :       Union[str, Any]				=	ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size)

         lowercase__   :       Tuple				=	None
         lowercase__   :       Union[str, Any]				=	None
         lowercase__   :       Dict				=	None
         if self.use_labels:
              lowercase__   :       Any				=	ids_tensor([self.batch_size]						,       self.type_sequence_label_size)
              lowercase__   :       Dict				=	ids_tensor([self.batch_size, self.seq_length]						,       self.num_labels)
              lowercase__   :       Optional[Any]				=	ids_tensor([self.batch_size]						,       self.num_choices)

         lowercase__   :       List[Any]				=	self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





    def 				snake_case_    (				self):
         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=a						,       initializer_range=self.initializer_range						,       )





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Optional[Any]				=	MobileBertModel(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Optional[Any]				=	model(a						,       attention_mask=a						,       token_type_ids=a)
         lowercase__   :       List[Any]				=	model(a						,       token_type_ids=a)
         lowercase__   :       Any				=	model(a)

         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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Any				=	MobileBertForMaskedLM(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Any				=	model(a						,       attention_mask=a						,       token_type_ids=a						,       labels=a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Optional[int]				=	MobileBertForNextSentencePrediction(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Optional[int]				=	model(
             a						,       attention_mask=a						,       token_type_ids=a						,       labels=a						,       )
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, 2))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       List[str]				=	MobileBertForPreTraining(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Union[str, Any]				=	model(
             a						,       attention_mask=a						,       token_type_ids=a						,       labels=a						,       next_sentence_label=a						,       )
         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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Tuple				=	MobileBertForQuestionAnswering(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Union[str, Any]				=	model(
             a						,       attention_mask=a						,       token_type_ids=a						,       start_positions=a						,       end_positions=a						,       )
         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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Tuple				=	self.num_labels
         lowercase__   :       Any				=	MobileBertForSequenceClassification(a)
         model.to(a)
         model.eval()
         lowercase__   :       Any				=	model(a						,       attention_mask=a						,       token_type_ids=a						,       labels=a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_labels))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Optional[Any]				=	self.num_labels
         lowercase__   :       List[str]				=	MobileBertForTokenClassification(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       List[str]				=	model(a						,       attention_mask=a						,       token_type_ids=a						,       labels=a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.num_labels))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Optional[Any]				=	self.num_choices
         lowercase__   :       Optional[int]				=	MobileBertForMultipleChoice(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Optional[Any]				=	input_ids.unsqueeze(1).expand(-1						,       self.num_choices						,       -1).contiguous()
         lowercase__   :       Dict				=	token_type_ids.unsqueeze(1).expand(-1						,       self.num_choices						,       -1).contiguous()
         lowercase__   :       List[Any]				=	input_mask.unsqueeze(1).expand(-1						,       self.num_choices						,       -1).contiguous()
         lowercase__   :       List[Any]				=	model(
             a						,       attention_mask=a						,       token_type_ids=a						,       labels=a						,       )
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_choices))





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			
         )   :       Any				=	config_and_inputs
         lowercase__   :       Optional[int]				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					(
        (
            MobileBertModel,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    __lowerCamelCase						:	Tuple														=					(
        {
            """feature-extraction""": MobileBertModel,
            """fill-mask""": MobileBertForMaskedLM,
            """question-answering""": MobileBertForQuestionAnswering,
            """text-classification""": MobileBertForSequenceClassification,
            """token-classification""": MobileBertForTokenClassification,
            """zero-shot""": MobileBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __lowerCamelCase						:	List[str]														=					True





    def 				snake_case_    (				self						,       a						,       a						,       a=False):
         lowercase__   :       Union[str, Any]				=	super()._prepare_for_class(a						,       a						,       return_labels=a)

         if return_labels:
              if model_class in get_values(a):
                   lowercase__   :       Optional[Any]				=	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)						,       dtype=torch.long						,       device=a)
                   lowercase__   :       Dict				=	torch.zeros(
                       self.model_tester.batch_size						,       dtype=torch.long						,       device=a)
         return inputs_dict





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	MobileBertModelTester(self)
         lowercase__   :       Any				=	ConfigTester(self						,       config_class=a						,       hidden_size=37)





    def 				snake_case_    (				self):
         self.config_tester.run_common_tests()





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_model(*a)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_masked_lm(*a)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a)





    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a)





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_pretraining(*a)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_question_answering(*a)





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a)





    def 				snake_case_    (				self):
         lowercase__   :       str				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_token_classification(*a)



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]      ):



     '''simple docstring'''





     return torch.tensor(
         SCREAMING_SNAKE_CASE_     ,					dtype=torch.long     ,					device=SCREAMING_SNAKE_CASE_     ,					)


snake_case_        =			1e-3


@require_torch
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	MobileBertModel.from_pretrained('google/mobilebert-uncased').to(a)
         lowercase__   :       int				=	_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]])
         with torch.no_grad():
              lowercase__   :       Any				=	model(a)[0]
         lowercase__   :       List[Any]				=	torch.Size((1, 9, 512))
         self.assertEqual(output.shape						,       a)
         lowercase__   :       List[str]				=	torch.tensor(
             [
                 [
                     [-2.4736526e07, 8.2691656e04, 1.6521838e05],
                     [-5.7541704e-01, 3.9056022e00, 4.4011507e00],
                     [2.6047359e00, 1.5677652e00, -1.7324188e-01],
                 ]
             ]						,       device=a						,       )

         # 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
         lowercase__   :       List[str]				=	torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE)
         lowercase__   :       int				=	torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE)

         self.assertTrue(lower_bound and upper_bound)




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

