





'''simple docstring'''

from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
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 (
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertModel,
  )






class        UpperCAmelCase     :
 def __init__(    self				:List[Any]						,	lowercase_				:List[Any]						,	lowercase_				:int=13						,	lowercase_				:List[Any]=7						,	lowercase_				:Dict=True						,	lowercase_				:List[str]=True						,	lowercase_				:Dict=True						,	lowercase_				:List[str]=True						,	lowercase_				:List[str]=99						,	lowercase_				:List[Any]=32						,	lowercase_				:Dict=2						,	lowercase_				:Optional[int]=4						,	lowercase_				:List[str]=37						,	lowercase_				:Dict="gelu"						,	lowercase_				:Tuple=0.1						,	lowercase_				:Any=0.1						,	lowercase_				:List[Any]=5_12						,	lowercase_				:Tuple=16						,	lowercase_				:Any=2						,	lowercase_				:Optional[Any]=0.0_2						,	lowercase_				:Optional[int]=3						,	lowercase_				:List[Any]=4						,	lowercase_				:str=None						,	)->							int:
     A__    =      parent
     A__    =      13
     A__    =      7
     A__    =      True
     A__    =      True
     A__    =      True
     A__    =      True
     A__    =      99
     A__    =      3_84
     A__    =      2
     A__    =      4
     A__    =      37
     A__    =      'gelu'
     A__    =      0.1
     A__    =      0.1
     A__    =      5_12
     A__    =      16
     A__    =      2
     A__    =      0.0_2
     A__    =      3
     A__    =      4
     A__    =      1_28
     A__    =      2
     A__    =      9
     A__    =      1
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Any							)->							List[str]:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

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

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

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

     A__    =      ConvBertConfig(
         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						,	return_dict=lowercase_						,	)

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:Optional[int]						,	lowercase_				:Any						,	lowercase_				:str						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str							)->							int:
     A__    =      TFConvBertModel(config=lowercase_							)
     A__    =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

     A__    =      [input_ids, input_mask]
     A__    =      model(lowercase_							)

     A__    =      model(lowercase_							)

     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Any						,	lowercase_				:Dict							)->							Union[str, Any]:
     A__    =      TFConvBertForMaskedLM(config=lowercase_							)
     A__    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:List[str]						,	lowercase_				:Union[str, Any]						,	lowercase_				:List[Any]						,	lowercase_				:Tuple						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:Tuple							)->							str:
     A__    =      self.num_labels
     A__    =      TFConvBertForSequenceClassification(config=lowercase_							)
     A__    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:int						,	lowercase_				:Optional[int]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:List[Any]							)->							Any:
     A__    =      self.num_choices
     A__    =      TFConvBertForMultipleChoice(config=lowercase_							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      {
         'input_ids': multiple_choice_inputs_ids,
         'attention_mask': multiple_choice_input_mask,
         'token_type_ids': multiple_choice_token_type_ids,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)							)
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Union[str, Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:str						,	lowercase_				:Tuple						,	lowercase_				:Dict						,	lowercase_				:Optional[int]						,	lowercase_				:Any							)->							Optional[Any]:
     A__    =      self.num_labels
     A__    =      TFConvBertForTokenClassification(config=lowercase_							)
     A__    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:List[str]						,	lowercase_				:Optional[int]						,	lowercase_				:List[Any]						,	lowercase_				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:int							)->							Any:
     A__    =      TFConvBertForQuestionAnswering(config=lowercase_							)
     A__    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     A__    =      model(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 					UpperCAmelCase_							(    self				:str							)->							Tuple:
     A__    =      self.prepare_config_and_inputs()
     (
         A__							
     )    =      config_and_inputs
     A__    =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict






@require_tf
class        UpperCAmelCase     (	__UpperCamelCase      ,	__UpperCamelCase      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         TFConvBertModel,
         TFConvBertForMaskedLM,
         TFConvBertForQuestionAnswering,
         TFConvBertForSequenceClassification,
         TFConvBertForTokenClassification,
         TFConvBertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 __lowercase       		=    (
     {
         """feature-extraction""": TFConvBertModel,
         """fill-mask""": TFConvBertForMaskedLM,
         """question-answering""": TFConvBertForQuestionAnswering,
         """text-classification""": TFConvBertForSequenceClassification,
         """token-classification""": TFConvBertForTokenClassification,
         """zero-shot""": TFConvBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     A__    =      TFConvBertModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:int							)->							List[Any]:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:Any							)->							str:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:int							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
     A__    =      True
     A__    =      True

     if hasattr(lowercase_						,	"use_cache"							):
         A__    =      True

     A__    =      getattr(self.model_tester						,	"encoder_seq_length"						,	self.model_tester.seq_length							)
     A__    =      getattr(self.model_tester						,	"key_length"						,	lowercase_							)

     for model_class in self.all_model_classes:
         A__    =      self._prepare_for_class(lowercase_						,	lowercase_							)
         A__    =      model_class(lowercase_							)
         A__    =      len(model(lowercase_							)							)

         with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(lowercase_						,	saved_model=lowercase_							)
             A__    =      os.path.join(lowercase_						,	"saved_model"						,	"1"							)
             A__    =      tf.keras.models.load_model(lowercase_							)
             A__    =      model(lowercase_							)

             if self.is_encoder_decoder:
                 A__    =      outputs['encoder_hidden_states']
                 A__    =      outputs['encoder_attentions']
             else:
                 A__    =      outputs['hidden_states']
                 A__    =      outputs['attentions']

             self.assertEqual(len(lowercase_							)						,	lowercase_							)

             A__    =      getattr(
                 self.model_tester						,	"expected_num_hidden_layers"						,	self.model_tester.num_hidden_layers + 1							)

             self.assertEqual(len(lowercase_							)						,	lowercase_							)
             self.assertListEqual(
                 list(output_hidden_states[0].shape[-2:]							)						,	[self.model_tester.seq_length, self.model_tester.hidden_size]						,	)

             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)
             self.assertListEqual(
                 list(output_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]						,	)
 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							int:
     A__    =      TFConvBertModel.from_pretrained("YituTech/conv-bert-base"							)
     self.assertIsNotNone(lowercase_							)



 def 					UpperCAmelCase_							(    self				:int							)->							Dict:
     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
     A__    =      True
     A__    =      getattr(self.model_tester						,	"decoder_seq_length"						,	self.model_tester.seq_length							)
     A__    =      getattr(self.model_tester						,	"encoder_seq_length"						,	self.model_tester.seq_length							)
     A__    =      getattr(self.model_tester						,	"key_length"						,	lowercase_							)
     A__    =      getattr(self.model_tester						,	"key_length"						,	lowercase_							)

     def check_decoder_attentions_output(lowercase_				:Optional[Any]							):
         A__    =      len(lowercase_							)
         self.assertEqual(out_len % 2						,	0							)
         A__    =      outputs.decoder_attentions
         self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)
         self.assertListEqual(
             list(decoder_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]						,	)

     def check_encoder_attentions_output(lowercase_				:str							):
         A__    =      [
             t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
         ]
         self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)
         self.assertListEqual(
             list(attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]						,	)

     for model_class in self.all_model_classes:
         A__    =      True
         A__    =      False
         A__    =      model_class(lowercase_							)
         A__    =      model(self._prepare_for_class(lowercase_						,	lowercase_							)							)
         A__    =      len(lowercase_							)
         self.assertEqual(config.output_hidden_states						,	lowercase_							)
         check_encoder_attentions_output(lowercase_							)

         if self.is_encoder_decoder:
             A__    =      model_class(lowercase_							)
             A__    =      model(self._prepare_for_class(lowercase_						,	lowercase_							)							)
             self.assertEqual(config.output_hidden_states						,	lowercase_							)
             check_decoder_attentions_output(lowercase_							)

         # Check that output attentions can also be changed via the config
         del inputs_dict["output_attentions"]
         A__    =      True
         A__    =      model_class(lowercase_							)
         A__    =      model(self._prepare_for_class(lowercase_						,	lowercase_							)							)
         self.assertEqual(config.output_hidden_states						,	lowercase_							)
         check_encoder_attentions_output(lowercase_							)

         # Check attention is always last and order is fine
         A__    =      True
         A__    =      True
         A__    =      model_class(lowercase_							)
         A__    =      model(self._prepare_for_class(lowercase_						,	lowercase_							)							)

         self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,	len(lowercase_							)							)
         self.assertEqual(model.config.output_hidden_states						,	lowercase_							)
         check_encoder_attentions_output(lowercase_							)






@require_tf
class        UpperCAmelCase     (	unittest.TestCase    ):
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							str:
     A__    =      TFConvBertModel.from_pretrained("YituTech/conv-bert-base"							)
     A__    =      tf.constant([[0, 1, 2, 3, 4, 5]]							)
     A__    =      model(lowercase_							)[0]

     A__    =      [1, 6, 7_68]
     self.assertEqual(output.shape						,	lowercase_							)

     A__    =      tf.constant(
         [
             [
                 [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
                 [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
                 [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
             ]
         ]							)
     tf.debugging.assert_near(output[:, :3, :3]						,	lowercase_						,	atol=1E-4							)










'''simple docstring'''

def 						UpperCamelCase    (							):
    A__    =      [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]

    A__    =      6
    A__    =      1
    A__    =      19_01

    A__    =      0

    while year < 20_01:
        day += 7

        if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
            if day > days_per_month[month - 1] and month != 2:
                month += 1
                A__    =      day - days_per_month[month - 2]
            elif day > 29 and month == 2:
                month += 1
                A__    =      day - 29
        else:
            if day > days_per_month[month - 1]:
                month += 1
                A__    =      day - days_per_month[month - 2]

        if month > 12:
            year += 1
            A__    =      1

        if year < 20_01 and day == 1:
            sundays += 1
    return sundays


if __name__ == "__main__":
  print(solution())



