
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 			lowercase_		(      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						(
           (
               TFMobileBertModel,
               TFMobileBertForMaskedLM,
               TFMobileBertForNextSentencePrediction,
               TFMobileBertForPreTraining,
               TFMobileBertForQuestionAnswering,
               TFMobileBertForSequenceClassification,
               TFMobileBertForTokenClassification,
               TFMobileBertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       __lowerCamelCase				       =						(
           {
               "feature-extraction": TFMobileBertModel,
               "fill-mask": TFMobileBertForMaskedLM,
               "question-answering": TFMobileBertForQuestionAnswering,
               "text-classification": TFMobileBertForSequenceClassification,
               "token-classification": TFMobileBertForTokenClassification,
               "zero-shot": TFMobileBertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False


       def 	_snake_case				(    self						,       __A						,       __A						,       __A=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 			lowercase_		(      A       ):


              def __init__(    self						,       __A						,       __A=13						,       __A=7						,       __A=True						,       __A=True						,       __A=True						,       __A=True						,       __A=99						,       __A=32						,       __A=32						,       __A=2						,       __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						,       )						->    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 	_snake_case				(    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    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 	_snake_case				(    self				)						->    Optional[Any]:
                     SCREAMING_SNAKE_CASE_							:				str		=self.prepare_config_and_inputs()
                     (
                         (
                         SCREAMING_SNAKE_CASE_
                     )	,     (
                         SCREAMING_SNAKE_CASE_
                     )	,     (
                         SCREAMING_SNAKE_CASE_
                     )	,     (
                         SCREAMING_SNAKE_CASE_
                     )	,     (
                         SCREAMING_SNAKE_CASE_
                     )	,     (
                         SCREAMING_SNAKE_CASE_
                     )	,     (
                         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 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Any		=TFMobileBertModelTest.TFMobileBertModelTester(self				)
              SCREAMING_SNAKE_CASE_							:				int		=ConfigTester(self						,       config_class=__A						,       hidden_size=37				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              self.config_tester.run_common_tests()


       def 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 	_snake_case				(    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 			lowercase_		(      unittest.TestCase       ):


       @slow
       def 	_snake_case				(    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, 30_522]
              self.assertEqual(output.shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=tf.constant(
                  [
                      [
                          [-4.5_919_547, -9.248_295, -9.645_256],
                          [-6.7_306_175, -6.440_284, -6.6_052_837],
                          [-7.2_743_506, -6.7_847_915, -6.024_673],
                      ]
                  ]				)
              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() = }")
