"""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	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Dict=13   ,				__lowerCAmelCase	:  int=7   ,				__lowerCAmelCase	:  Optional[int]=True   ,				__lowerCAmelCase	:  str=True   ,				__lowerCAmelCase	:  List[str]=True   ,				__lowerCAmelCase	:  Tuple=True   ,				__lowerCAmelCase	:  Any=99   ,				__lowerCAmelCase	:  int=64   ,				__lowerCAmelCase	:  Dict=32   ,				__lowerCAmelCase	:  List[str]=5   ,				__lowerCAmelCase	:  str=4   ,				__lowerCAmelCase	:  str=37   ,				__lowerCAmelCase	:  Any="gelu"   ,				__lowerCAmelCase	:  Dict=0.1   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  int=512   ,				__lowerCAmelCase	:  Tuple=16   ,				__lowerCAmelCase	:  Dict=2   ,				__lowerCAmelCase	:  str=0.02   ,				__lowerCAmelCase	:  Tuple=3   ,				__lowerCAmelCase	:  Any=4   ,				__lowerCAmelCase	:  Optional[int]=None   ,				):
      _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      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _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      lowerCAmelCase_  (					self	:  List[Any]					):
      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=__lowerCAmelCase   ,				initializer_range=self.initializer_range   ,				)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      MobileBertModel(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase					)
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      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      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Any					):
      _UpperCAmelCase						 =      MobileBertForMaskedLM(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      MobileBertForNextSentencePrediction(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(
          __lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase   ,				)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, 2)					)


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      MobileBertForPreTraining(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(
          __lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase   ,				next_sentence_label=__lowerCAmelCase   ,				)
      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      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      MobileBertForQuestionAnswering(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(
          __lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				start_positions=__lowerCAmelCase   ,				end_positions=__lowerCAmelCase   ,				)
      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      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      self.num_labels
      _UpperCAmelCase						 =      MobileBertForSequenceClassification(__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      model.eval()
      _UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)


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


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      self.num_choices
      _UpperCAmelCase						 =      MobileBertForMultipleChoice(config=__lowerCAmelCase					)
      model.to(__lowerCAmelCase					)
      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(
          __lowerCAmelCase   ,				attention_mask=__lowerCAmelCase   ,				token_type_ids=__lowerCAmelCase   ,				labels=__lowerCAmelCase   ,				)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)					)


    def      lowerCAmelCase_  (					self	:  int					):
      _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				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							(
        (
            MobileBertModel,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _snake_case     :  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 {}
    )
    _snake_case     :  Tuple     							=							True


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[str]=False					):
      _UpperCAmelCase						 =      super()._prepare_for_class(__lowerCAmelCase   ,				__lowerCAmelCase   ,				return_labels=__lowerCAmelCase					)

      if return_labels:
        if model_class in get_values(__lowerCAmelCase					):
          _UpperCAmelCase						 =      torch.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)   ,				dtype=torch.long   ,				device=__lowerCAmelCase					)
          _UpperCAmelCase						 =      torch.zeros(
              self.model_tester.batch_size   ,				dtype=torch.long   ,				device=__lowerCAmelCase					)
      return inputs_dict


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      MobileBertModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				hidden_size=37					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase					)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return torch.tensor(
      lowercase   ,dtype=torch.long   ,device=lowercase   ,)


UpperCAmelCase__  		=						1E-3


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


    @slow
    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      MobileBertModel.from_pretrained("""google/mobilebert-uncased"""					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]					)
      with torch.no_grad():
        _UpperCAmelCase						 =      model(__lowerCAmelCase					)[0]
      _UpperCAmelCase						 =      torch.Size((1, 9, 512)					)
      self.assertEqual(output.shape   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      torch.tensor(
          [
              [
                  [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
                  [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
                  [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
              ]
          ]   ,				device=__lowerCAmelCase   ,				)

      # 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					)

"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  int   ,				*__lowerCAmelCase	:  List[str]   ,				**__lowerCAmelCase	:  List[str]					):
      warnings.warn(
          """The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ChineseCLIPImageProcessor instead."""   ,				__lowerCAmelCase   ,				)
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
