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 ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=13 ,					SCREAMING_SNAKE_CASE_=7 ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=99 ,					SCREAMING_SNAKE_CASE_=32 ,					SCREAMING_SNAKE_CASE_=5 ,					SCREAMING_SNAKE_CASE_=4 ,					SCREAMING_SNAKE_CASE_=37 ,					SCREAMING_SNAKE_CASE_="gelu" ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=0.1 ,					SCREAMING_SNAKE_CASE_=512 ,					SCREAMING_SNAKE_CASE_=16 ,					SCREAMING_SNAKE_CASE_=2 ,					SCREAMING_SNAKE_CASE_=0.02 ,					SCREAMING_SNAKE_CASE_=4 ,					)		->   Tuple:





       '''simple docstring'''





       UpperCamelCase__					    =   parent
       UpperCamelCase__					    =   batch_size
       UpperCamelCase__					    =   seq_length
       UpperCamelCase__					    =   is_training
       UpperCamelCase__					    =   use_attention_mask
       UpperCamelCase__					    =   use_token_type_ids
       UpperCamelCase__					    =   use_labels
       UpperCamelCase__					    =   vocab_size
       UpperCamelCase__					    =   hidden_size
       UpperCamelCase__					    =   num_hidden_layers
       UpperCamelCase__					    =   num_attention_heads
       UpperCamelCase__					    =   intermediate_size
       UpperCamelCase__					    =   hidden_act
       UpperCamelCase__					    =   hidden_dropout_prob
       UpperCamelCase__					    =   attention_probs_dropout_prob
       UpperCamelCase__					    =   max_position_embeddings
       UpperCamelCase__					    =   type_vocab_size
       UpperCamelCase__					    =   type_sequence_label_size
       UpperCamelCase__					    =   initializer_range
       UpperCamelCase__					    =   num_choices






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





       '''simple docstring'''





       UpperCamelCase__					    =   ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size						)

       UpperCamelCase__					    =   None
       if self.use_attention_mask:
            UpperCamelCase__					    =   random_attention_mask([self.batch_size, self.seq_length]						)

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

       UpperCamelCase__					    =   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=__UpperCamelCase ,					initializer_range=self.initializer_range ,					)

       return config, input_ids, token_type_ids, attention_mask






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   self.prepare_config_and_inputs()
       UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__					    =   config_and_inputs
       UpperCamelCase__					    =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class      _A (     a_						,			unittest.TestCase				):
  SCREAMING_SNAKE_CASE_						:						List[Any]    =(
      (
          FlaxAlbertModel,
          FlaxAlbertForPreTraining,
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   FlaxAlbertModelTester(self						)






  @slow
  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       for model_class_name in self.all_model_classes:
            UpperCamelCase__					    =   model_class_name.from_pretrained('''albert-base-v2'''						)
            UpperCamelCase__					    =   model(np.ones((1, 1)						)						)
            self.assertIsNotNone(__UpperCamelCase						)



@require_flax
class      _A (     unittest.TestCase				):






  @slow
  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       UpperCamelCase__					    =   FlaxAlbertModel.from_pretrained('''albert-base-v2'''						)
       UpperCamelCase__					    =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
       UpperCamelCase__					    =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       UpperCamelCase__					    =   model(__UpperCamelCase ,					attention_mask=__UpperCamelCase						)[0]
       UpperCamelCase__					    =   (1, 11, 768)
       self.assertEqual(output.shape ,					__UpperCamelCase						)
       UpperCamelCase__					    =   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] ,					__UpperCamelCase ,					atol=1E-4						)						)











import itertools
import string
from collections.abc import Generator, Iterable





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       iter(__snake_case							)
     while True:
          __lowerCAmelCase             =       tuple(itertools.islice(__snake_case       ,			__snake_case							)							)
          if not chunk:
               return
          yield chunk





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       "".join([c.upper() for c in dirty if c in string.ascii_letters]							)
     __lowerCAmelCase             =       ""

     if len(__snake_case							) < 2:
          return dirty

     for i in range(len(__snake_case							) - 1							):
          clean += dirty[i]

          if dirty[i] == dirty[i + 1]:
               clean += "X"

     clean += dirty[-1]

     if len(__snake_case							) & 1:
          clean += "X"

     return clean





def    __lowerCAmelCase   (    __snake_case							):
     # I and J are used interchangeably to allow
     # us to use a 5x5 table (25 letters)
     __lowerCAmelCase             =       "ABCDEFGHIKLMNOPQRSTUVWXYZ"
     # we're using a list instead of a '2d' array because it makes the math
     # for setting up the table and doing the actual encoding/decoding simpler
     __lowerCAmelCase             =       []

     # copy key chars into the table if they are in `alphabet` ignoring duplicates
     for char in key.upper():
          if char not in table and char in alphabet:
               table.append(__snake_case							)

    # fill the rest of the table in with the remaining alphabet chars
     for char in alphabet:
          if char not in table:
               table.append(__snake_case							)

     return table





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       prepare_input(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
          elif cola == cola:
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
          else:  # rectangle
               ciphertext += table[rowa * 5 + cola]
               ciphertext += table[rowa * 5 + cola]

     return ciphertext





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               plaintext += table[rowa * 5 + (cola - 1) % 5]
               plaintext += table[rowa * 5 + (cola - 1) % 5]
          elif cola == cola:
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
          else:  # rectangle
               plaintext += table[rowa * 5 + cola]
               plaintext += table[rowa * 5 + cola]

     return plaintext






