



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
      from transformers import (
          AutoConfig,
          BertConfig,
          GPTaConfig,
          TaConfig,
          TFAutoModel,
          TFAutoModelForCausalLM,
          TFAutoModelForMaskedLM,
          TFAutoModelForPreTraining,
          TFAutoModelForQuestionAnswering,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSequenceClassification,
          TFAutoModelWithLMHead,
          TFBertForMaskedLM,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertModel,
          TFGPTaLMHeadModel,
          TFRobertaForMaskedLM,
          TFTaForConditionalGeneration,
      )
      from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
      from transformers import (
          AutoModel,
          AutoModelForCausalLM,
          AutoModelForMaskedLM,
          AutoModelForPreTraining,
          AutoModelForQuestionAnswering,
          AutoModelForSeqaSeqLM,
          AutoModelForSequenceClassification,
          AutoModelWithLMHead,
          BertForMaskedLM,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          BertModel,
          GPTaLMHeadModel,
          RobertaForMaskedLM,
          TaForConditionalGeneration,
      )



@is_pt_tf_cross_test
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    for model_name in ["bert-base-uncased"]:
     UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[Any]			=			TFAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[str]			=			AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    for model_name in ["bert-base-uncased"]:
     UpperCAmelCase			:      Optional[int]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Optional[int]			=			TFAutoModelForPreTraining.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Optional[Any]			=			AutoModelForPreTraining.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      int			=			TFAutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			TFAutoModelForCausalLM.from_pretrained(
         _SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[Any]			=			AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			AutoModelForCausalLM.from_pretrained(
         _SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Union[str, Any]			=			TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[str]			=			AutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      int			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[str]			=			TFAutoModelForMaskedLM.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			:      Any			=			TFAutoModelForMaskedLM.from_pretrained(
         _SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[str]			=			AutoModelForMaskedLM.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			:      Any			=			AutoModelForMaskedLM.from_pretrained(
         _SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      Dict			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[Any]			=			TFAutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			:      Optional[Any]			=			TFAutoModelForSeqaSeqLM.from_pretrained(
         _SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Dict			=			AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			AutoModelForSeqaSeqLM.from_pretrained(
         _SCREAMING_SNAKE_CASE   ,  output_loading_info=_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    for model_name in ["bert-base-uncased"]:
     UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Union[str, Any]			=			TFAutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      int			=			AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    for model_name in ["bert-base-uncased"]:
     UpperCAmelCase			:      Optional[int]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      List[Any]			=			TFAutoModelForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      Optional[int]			=			AutoModelForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertEqual(model.num_parameters()   ,  14410		)
    self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE		)   ,  14410		)

    UpperCAmelCase			:      Dict			=			AutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertEqual(model.num_parameters()   ,  14410		)
    self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE		)   ,  14410		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_pt=_SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertEqual(model.num_parameters()   ,  14410		)
    self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE		)   ,  14410		)

    UpperCAmelCase			:      Optional[Any]			=			AutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE   ,  from_tf=_SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertEqual(model.num_parameters()   ,  14410		)
    self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE		)   ,  14410		)





"""simple docstring"""
import math
from datetime import datetime, timedelta

def    _snake_case		(		UpperCamelCase		:	int		):
 UpperCAmelCase			:      Any			=			year % 19
 UpperCAmelCase			:      Any			=			year % 4
 UpperCAmelCase			:      str			=			year % 7
 UpperCAmelCase			:      Union[str, Any]			=			math.floor(year / 100		)
 UpperCAmelCase			:      Optional[Any]			=			math.floor((13 + 8 * leap_day_inhibits) / 25		)
 UpperCAmelCase			:      int			=			leap_day_inhibits / 4
 UpperCAmelCase			:      int			=			(
     15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
 ) % 30
 UpperCAmelCase			:      Tuple			=			(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

 # days to be added to March 21
 UpperCAmelCase			:      int			=			(19 * metonic_cycle + secular_moon_shift) % 30

 # PHM -> Paschal Full Moon
 UpperCAmelCase			:      Tuple			=			(
     2 * julian_leap_year
     + 4 * non_leap_year
     + 6 * days_to_add
     + century_starting_point
 ) % 7

 if days_to_add == 29 and days_from_phm_to_sunday == 6:
  return datetime(UpperCamelCase    ,	4    ,	19		)
 elif days_to_add == 28 and days_from_phm_to_sunday == 6:
  return datetime(UpperCamelCase    ,	4    ,	18		)
 else:
  return datetime(UpperCamelCase    ,	3    ,	22		) + timedelta(
      days=int(days_to_add + days_from_phm_to_sunday		)		)


if __name__ == "__main__":
      for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
            A:     Any										=	"will be" if year > datetime.now().year else "was"
            print(f"""Easter in {year} {tense} {gauss_easter(year)}""")
