



'''simple docstring'''




from __future__ import annotations



def 	a			( lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__ = False						,      ):
			'''simple docstring'''





			A_	:						Any	=     cipher_alphabet or [chr(lowerCamelCase__    ) for i in range(97						,      1_23    )]

			# 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)
						A_	:						Union[str, Any]	=     {
						    """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
						A_	:						Optional[Any]	=     frequencies_dict

			if not case_sensitive:
						A_	:						Optional[int]	=     ciphertext.lower()

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

			# cycle through all of the shifts
			for shift in range(len(lowerCamelCase__    )    ):
						A_	:						Tuple	=     """"""

						# decrypt the message with the shift
						for letter in ciphertext:
									try:
												# Try to index the letter in the alphabet
												A_	:						Optional[Any]	=     (alphabet_letters.index(letter.lower()    ) - shift) % len(
												    lowerCamelCase__    )
												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

						A_	:						int	=     0.0

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

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

															# Complete the chi squared statistic formula
															A_	:						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
															A_	:						str	=     decrypted_with_shift.count(lowerCamelCase__    )

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

															# Complete the chi squared statistic formula
															A_	:						Tuple	=     ((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
						A_	:						Any	=     (
						    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(lowerCamelCase__    ) -> tuple[float, str]:
						return chi_squared_statistic_values[key]

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

			# Get all the data from the most likely cipher (key, decoded message)
			(
			    (
			    A_
			),    (
			    A_
			),    
			)	:						Optional[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,
			)




'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)







class 	_lowerCAmelCase							(			unittest.TestCase ):


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


	def     _a	(self							):
				A_	:						List[str]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

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

				A_	:						Union[str, Any]	=     DistilBertConfig(
				    vocab_size=self.vocab_size       ,      dim=self.hidden_size       ,      n_layers=self.num_hidden_layers       ,      n_heads=self.num_attention_heads       ,      hidden_dim=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      dropout=self.hidden_dropout_prob       ,      attention_dropout=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      initializer_range=self.initializer_range       ,      tie_weights_=lowercase       ,      )

				return config, input_ids, attention_mask


	def     _a	(self							):
				A_	:						List[str]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						Any	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_flax
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)


	def     _a	(self							):
				A_	:						Tuple	=     FlaxDistilBertModelTester(self							)


	@slow
	def     _a	(self							):
				for model_class_name in self.all_model_classes:
							A_	:						Union[str, Any]	=     model_class_name.from_pretrained("""distilbert-base-uncased"""							)
							A_	:						Any	=     model(np.ones((1, 1)							)							)
							self.assertIsNotNone(lowercase							)







@require_flax
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						List[str]	=     FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
				A_	:						Optional[Any]	=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
				A_	:						int	=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
				A_	:						Optional[int]	=     model(lowercase       ,      attention_mask=lowercase							)[0]
				A_	:						Optional[Any]	=     (1, 11, 768)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Union[str, Any]	=     np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

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