


import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )



class        a					(			unittest.TestCase   ):





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=13    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=7    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Tuple=99    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=32    ,	SCREAMING_SNAKE_CASE_					:				List[str]=5    ,	SCREAMING_SNAKE_CASE_					:				Any=4    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=37    ,	SCREAMING_SNAKE_CASE_					:				str="gelu"    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=0.1    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				Dict=512    ,	SCREAMING_SNAKE_CASE_					:				Any=16    ,	SCREAMING_SNAKE_CASE_					:				int=2    ,	SCREAMING_SNAKE_CASE_					:				str=0.02    ,	SCREAMING_SNAKE_CASE_					:				Tuple=4    ,	):
        __lowerCamelCase:				str     =	parent
        __lowerCamelCase:				Union[str, Any]     =	batch_size
        __lowerCamelCase:				List[Any]     =	seq_length
        __lowerCamelCase:				List[Any]     =	is_training
        __lowerCamelCase:				Any     =	use_attention_mask
        __lowerCamelCase:				str     =	use_token_type_ids
        __lowerCamelCase:				Optional[Any]     =	use_labels
        __lowerCamelCase:				List[str]     =	vocab_size
        __lowerCamelCase:				Union[str, Any]     =	hidden_size
        __lowerCamelCase:				Tuple     =	num_hidden_layers
        __lowerCamelCase:				Optional[int]     =	num_attention_heads
        __lowerCamelCase:				Dict     =	intermediate_size
        __lowerCamelCase:				str     =	hidden_act
        __lowerCamelCase:				Optional[Any]     =	hidden_dropout_prob
        __lowerCamelCase:				Optional[int]     =	attention_probs_dropout_prob
        __lowerCamelCase:				Dict     =	max_position_embeddings
        __lowerCamelCase:				str     =	type_vocab_size
        __lowerCamelCase:				str     =	type_sequence_label_size
        __lowerCamelCase:				List[Any]     =	initializer_range
        __lowerCamelCase:				Optional[int]     =	num_choices





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

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

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

        __lowerCamelCase:				List[str]     =	AlbertConfig(
            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    ,	is_decoder=SCREAMING_SNAKE_CASE_    ,	initializer_range=self.initializer_range    ,	)

        return config, input_ids, token_type_ids, attention_mask







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Dict     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Any     =	config_and_inputs
        __lowerCamelCase:				Optional[int]     =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict



@require_flax
class        a					(			_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Union[str, Any]						      =	(
         (
             FlaxAlbertModel,
             FlaxAlbertForPreTraining,
             FlaxAlbertForMaskedLM,
             FlaxAlbertForMultipleChoice,
             FlaxAlbertForQuestionAnswering,
             FlaxAlbertForSequenceClassification,
             FlaxAlbertForTokenClassification,
             FlaxAlbertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[Any]     =	FlaxAlbertModelTester(self							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        for model_class_name in self.all_model_classes:
           __lowerCamelCase:				Union[str, Any]     =	model_class_name.from_pretrained("""albert-base-v2"""							)
           __lowerCamelCase:				List[str]     =	model(np.ones((1, 1)							)							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



@require_flax
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				List[Any]     =	FlaxAlbertModel.from_pretrained("""albert-base-v2"""							)
        __lowerCamelCase:				int     =	np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
        __lowerCamelCase:				str     =	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)[0]
        __lowerCamelCase:				Dict     =	(1, 11, 768)
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	np.array(
            [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]							)

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)






_A		:				Tuple									=       '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				Optional[int]     =	input("""Enter message: """ )
   __lowerCamelCase:				Dict     =	input("""Enter key [alphanumeric]: """ )
   __lowerCamelCase:				List[Any]     =	input("""Encrypt/Decrypt [e/d]: """ )

   if mode.lower().startswith("""e""" ):
      __lowerCamelCase:				Optional[int]     =	"""encrypt"""
      __lowerCamelCase:				Optional[int]     =	encrypt_message(snake_case						,      snake_case )
   elif mode.lower().startswith("""d""" ):
      __lowerCamelCase:				Union[str, Any]     =	"""decrypt"""
      __lowerCamelCase:				Optional[Any]     =	decrypt_message(snake_case						,      snake_case )

   print(f'\n{mode.title()}ed message:' )
   print(snake_case )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """encrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """decrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str )       ->							str:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Optional[int]     =	0
   __lowerCamelCase:				Any     =	key.upper()

   for symbol in message:
      __lowerCamelCase:				int     =	LETTERS.find(symbol.upper() )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index] )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index] )

         num %= len(snake_case )

         if symbol.isupper():
            translated.append(LETTERS[num] )
         elif symbol.islower():
            translated.append(LETTERS[num].lower() )

         key_index += 1
         if key_index == len(snake_case ):
            __lowerCamelCase:				Union[str, Any]     =	0
      else:
         translated.append(snake_case )
   return "".join(snake_case )


if __name__ == "__main__":
   main()


