from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertModel,
      )






@require_tf
class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   (
        (
            TFMobileBertModel,
            TFMobileBertForMaskedLM,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __a						=   (
        {
            "feature-extraction": TFMobileBertModel,
            "fill-mask": TFMobileBertForMaskedLM,
            "question-answering": TFMobileBertForQuestionAnswering,
            "text-classification": TFMobileBertForSequenceClassification,
            "token-classification": TFMobileBertForTokenClassification,
            "zero-shot": TFMobileBertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    __a						=   False
    __a						=   False
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase=False )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Any=   super()._prepare_for_class(__A			,			__A			,			return_labels=__A )

     if return_labels:
      if model_class in get_values(__A ):
       SCREAMING_SNAKE_CASE__: str=   tf.zeros(self.model_tester.batch_size			,			dtype=tf.intaa )

     return inputs_dict






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

         SCREAMING_SNAKE_CASE__: Union[str, Any]=   None
         if self.use_input_mask:
          SCREAMING_SNAKE_CASE__: List[Any]=   random_attention_mask([self.batch_size, self.seq_length] )

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

         SCREAMING_SNAKE_CASE__: Union[str, Any]=   None
         SCREAMING_SNAKE_CASE__: List[str]=   None
         SCREAMING_SNAKE_CASE__: List[str]=   None
         if self.use_labels:
          SCREAMING_SNAKE_CASE__: Any=   ids_tensor([self.batch_size]			,			self.type_sequence_label_size )
          SCREAMING_SNAKE_CASE__: str=   ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels )
          SCREAMING_SNAKE_CASE__: List[Any]=   ids_tensor([self.batch_size]			,			self.num_choices )

         SCREAMING_SNAKE_CASE__: Optional[int]=   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			,			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			,			initializer_range=self.initializer_range			,			embedding_size=self.embedding_size			,			)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[Any]:
         SCREAMING_SNAKE_CASE__: Union[str, Any]=   TFMobileBertModel(config=__A )
         SCREAMING_SNAKE_CASE__: Dict=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: Optional[int]=   model(__A )

         SCREAMING_SNAKE_CASE__: Union[str, Any]=   [input_ids, input_mask]
         SCREAMING_SNAKE_CASE__: List[Any]=   model(__A )

         SCREAMING_SNAKE_CASE__: Optional[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     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
         SCREAMING_SNAKE_CASE__: List[str]=   TFMobileBertForMaskedLM(config=__A )
         SCREAMING_SNAKE_CASE__: Union[str, Any]=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: int=   model(__A )
         self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size) )
        def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
         SCREAMING_SNAKE_CASE__: Tuple=   TFMobileBertForNextSentencePrediction(config=__A )
         SCREAMING_SNAKE_CASE__: List[str]=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: Any=   model(__A )
         self.parent.assertEqual(result.logits.shape			,			(self.batch_size, 2) )
        def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
         SCREAMING_SNAKE_CASE__: int=   TFMobileBertForPreTraining(config=__A )
         SCREAMING_SNAKE_CASE__: Union[str, Any]=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: List[Any]=   model(__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     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Dict:
         SCREAMING_SNAKE_CASE__: Tuple=   self.num_labels
         SCREAMING_SNAKE_CASE__: List[str]=   TFMobileBertForSequenceClassification(config=__A )
         SCREAMING_SNAKE_CASE__: Dict=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: List[str]=   model(__A )
         self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )
        def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       str:
         SCREAMING_SNAKE_CASE__: int=   self.num_choices
         SCREAMING_SNAKE_CASE__: Dict=   TFMobileBertForMultipleChoice(config=__A )
         SCREAMING_SNAKE_CASE__: str=   tf.tile(tf.expand_dims(__A			,			1 )			,			(1, self.num_choices, 1) )
         SCREAMING_SNAKE_CASE__: Dict=   tf.tile(tf.expand_dims(__A			,			1 )			,			(1, self.num_choices, 1) )
         SCREAMING_SNAKE_CASE__: Dict=   tf.tile(tf.expand_dims(__A			,			1 )			,			(1, self.num_choices, 1) )
         SCREAMING_SNAKE_CASE__: Any=   {
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         SCREAMING_SNAKE_CASE__: Optional[Any]=   model(__A )
         self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_choices) )
        def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Dict:
         SCREAMING_SNAKE_CASE__: Optional[Any]=   self.num_labels
         SCREAMING_SNAKE_CASE__: Optional[int]=   TFMobileBertForTokenClassification(config=__A )
         SCREAMING_SNAKE_CASE__: Optional[int]=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: int=   model(__A )
         self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels) )
        def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       List[str]:
         SCREAMING_SNAKE_CASE__: Dict=   TFMobileBertForQuestionAnswering(config=__A )
         SCREAMING_SNAKE_CASE__: Dict=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         SCREAMING_SNAKE_CASE__: Union[str, Any]=   model(__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     UpperCamelCase_					(	self )							->       Optional[Any]:
         SCREAMING_SNAKE_CASE__: str=   self.prepare_config_and_inputs()
         (
             SCREAMING_SNAKE_CASE__     
         ): Optional[Any]=   config_and_inputs
         SCREAMING_SNAKE_CASE__: Optional[Any]=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: Any=   TFMobileBertModelTest.TFMobileBertModelTester(self )
     SCREAMING_SNAKE_CASE__: int=   ConfigTester(self			,			config_class=__A			,			hidden_size=37 )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     self.config_tester.run_common_tests()
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_model(*__A )
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_masked_lm(*__A )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__A )
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__A )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_pretraining(*__A )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_question_answering(*__A )
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__A )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: int=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_token_classification(*__A )



    @slow
    def     UpperCamelCase_					(	self )							->       Dict:
     # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     for model_name in ["google/mobilebert-uncased"]:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   TFMobileBertModel.from_pretrained(__A )
      self.assertIsNotNone(__A )






@require_tf
class 		_lowerCamelCase		( unittest.TestCase     ):
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased''' )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   tf.constant([[0, 1, 2, 3, 4, 5]] )
     SCREAMING_SNAKE_CASE__: Any=   model(__A )[0]

     SCREAMING_SNAKE_CASE__: Optional[int]=   [1, 6, 30522]
     self.assertEqual(output.shape			,			__A )

     SCREAMING_SNAKE_CASE__: Dict=   tf.constant(
         [
             [
                 [-4.5919547, -9.248295, -9.645256],
                 [-6.7306175, -6.440284, -6.6052837],
                 [-7.2743506, -6.7847915, -6.024673],
             ]
         ] )
     tf.debugging.assert_near(output[:, :3, :3]			,			__A			,			atol=1e-4 )



from math import ceil, sqrt
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 1_0_0_0_0_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0

       for outer_width in range(3			,							(limit // 4) + 2				):
              if outer_width**2 > limit:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=max(ceil(sqrt(outer_width**2 - limit				)				)			,							1				)
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=1
              if (outer_width - hole_width_lower_bound) % 2:
                     hole_width_lower_bound += 1

              answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

       return answer


if __name__ == "__main__":
 print(F"{solution() = }")
