






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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def __init__(     self							:    Any      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    str=13      ,   __lowerCamelCase							:    List[Any]=7      ,   __lowerCamelCase							:    str=True      ,   __lowerCamelCase							:    int=True      ,   __lowerCamelCase							:    Any=True      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=99      ,   __lowerCamelCase							:    Optional[int]=32      ,   __lowerCamelCase							:    Any=5      ,   __lowerCamelCase							:    Dict=4      ,   __lowerCamelCase							:    Optional[Any]=37      ,   __lowerCamelCase							:    int="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    int=512      ,   __lowerCamelCase							:    List[Any]=16      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    List[Any]=0.0_2      ,   __lowerCamelCase							:    Optional[Any]=4      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						parent
             lowerCamelCase__	:       Optional[int]					=						batch_size
             lowerCamelCase__	:       List[Any]					=						seq_length
             lowerCamelCase__	:       str					=						is_training
             lowerCamelCase__	:       Dict					=						use_attention_mask
             lowerCamelCase__	:       List[str]					=						use_token_type_ids
             lowerCamelCase__	:       Tuple					=						use_labels
             lowerCamelCase__	:       List[str]					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[Any]					=						num_hidden_layers
             lowerCamelCase__	:       List[Any]					=						num_attention_heads
             lowerCamelCase__	:       Dict					=						intermediate_size
             lowerCamelCase__	:       int					=						hidden_act
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       str					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Dict					=						type_vocab_size
             lowerCamelCase__	:       Optional[Any]					=						type_sequence_label_size
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       Union[str, Any]					=						num_choices





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

             lowerCamelCase__	:       Dict					=						None
             if self.use_attention_mask:
                   lowerCamelCase__	:       List[str]					=						random_attention_mask([self.batch_size, self.seq_length] )

             lowerCamelCase__	:       Optional[int]					=						None
             if self.use_token_type_ids:
                   lowerCamelCase__	:       Tuple					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size )

             lowerCamelCase__	:       Optional[Any]					=						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=__lowerCamelCase      ,   initializer_range=self.initializer_range      ,   )

             return config, input_ids, token_type_ids, attention_mask





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.prepare_config_and_inputs()
             lowerCamelCase__	:       List[Any]					=						config_and_inputs
             lowerCamelCase__	:       List[str]					=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
             return config, inputs_dict







@require_flax
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (
           (
               FlaxAlbertModel,
               FlaxAlbertForPreTraining,
               FlaxAlbertForMaskedLM,
               FlaxAlbertForMultipleChoice,
               FlaxAlbertForQuestionAnswering,
               FlaxAlbertForSequenceClassification,
               FlaxAlbertForTokenClassification,
               FlaxAlbertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						FlaxAlbertModelTester(self )





       @slow
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__	:       Union[str, Any]					=						model_class_name.from_pretrained("albert-base-v2" )
                   lowerCamelCase__	:       Dict					=						model(np.ones((1, 1) ) )
                   self.assertIsNotNone(__lowerCamelCase )







@require_flax
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						FlaxAlbertModel.from_pretrained("albert-base-v2" )
             lowerCamelCase__	:       Optional[int]					=						np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
             lowerCamelCase__	:       List[str]					=						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
             lowerCamelCase__	:       Union[str, Any]					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase )[0]
             lowerCamelCase__	:       Tuple					=						(1, 11, 768)
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             lowerCamelCase__	:       Any					=						np.array(
                 [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )

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






from __future__ import annotations

def    lowercase_	(	_A    :					str					,      _A    :					list[str] | None = None					,      _A    :					dict[str, float] | None = None					,      _A    :					bool = False					,      ):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						cipher_alphabet or [chr(_A		) for i in range(97					,      123		)]

      # If the argument is None or the user provided an empty dictionary
      if not frequencies_dict:
            # Frequencies of letters in the english language (how much they show up)
            lowerCamelCase__	:       Dict					=						{
                "a": 0.08_497,
                "b": 0.01_492,
                "c": 0.02_202,
                "d": 0.04_253,
                "e": 0.11_162,
                "f": 0.02_228,
                "g": 0.02_015,
                "h": 0.06_094,
                "i": 0.07_546,
                "j": 0.00_153,
                "k": 0.01_292,
                "l": 0.04_025,
                "m": 0.02_406,
                "n": 0.06_749,
                "o": 0.07_507,
                "p": 0.01_929,
                "q": 0.00_095,
                "r": 0.07_587,
                "s": 0.06_327,
                "t": 0.09_356,
                "u": 0.02_758,
                "v": 0.00_978,
                "w": 0.02_560,
                "x": 0.00_150,
                "y": 0.01_994,
                "z": 0.00_077,
            }
      else:
            # Custom frequencies dictionary
            lowerCamelCase__	:       Optional[int]					=						frequencies_dict

      if not case_sensitive:
            lowerCamelCase__	:       str					=						ciphertext.lower()

      # Chi squared statistic values
      lowerCamelCase__	:       dict[int, tuple[float, str]]					=						{}

      # cycle through all of the shifts
      for shift in range(len(_A		)		):
            lowerCamelCase__	:       Optional[Any]					=						""

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        lowerCamelCase__	:       Dict					=						(alphabet_letters.index(letter.lower()		) - shift) % len(
                            _A		)
                        decrypted_with_shift += (
                            alphabet_letters[new_key].upper()
                            if case_sensitive and letter.isupper()
                            else alphabet_letters[new_key]
                        )
                  except ValueError:
                        # Append the character if it isn't in the alphabet
                        decrypted_with_shift += letter

            lowerCamelCase__	:       str					=						0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        lowerCamelCase__	:       List[str]					=						letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       List[str]					=						decrypted_with_shift.lower().count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       List[Any]					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       str					=						((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value
                  else:
                        if letter.lower() in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       Any					=						decrypted_with_shift.count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       str					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       int					=						((occurrences - expected) ** 2) / expected

                              # Add the margin of error to the total chi squared statistic
                              chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
            lowerCamelCase__	:       Optional[int]					=						(
                chi_squared_statistic,
                decrypted_with_shift,
            )

      # Get the most likely cipher by finding the cipher with the smallest chi squared
      # statistic
      def chi_squared_statistic_values_sorting_key(_A    :					int		) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      lowerCamelCase__	:       int					=						min(
          _A					,      key=_A					,      )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          lowerCamelCase__
      )					,     (
          lowerCamelCase__
      )					,     
      )	:       int					=						chi_squared_statistic_values[most_likely_cipher]

      # Return the data on the most likely shift
      return (
          most_likely_cipher,
          most_likely_cipher_chi_squared_value,
          decoded_most_likely_cipher,
      )


