
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.bert.modeling_flax_bert import (
					    FlaxBertForMaskedLM,
					    FlaxBertForMultipleChoice,
					    FlaxBertForNextSentencePrediction,
					    FlaxBertForPreTraining,
					    FlaxBertForQuestionAnswering,
					    FlaxBertForSequenceClassification,
					    FlaxBertForTokenClassification,
					    FlaxBertModel,
					)





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Any							,A :			List[str]=13							,A :			int=7							,A :			str=True							,A :			Dict=True							,A :			Union[str, Any]=True							,A :			int=True							,A :			List[Any]=99							,A :			List[str]=32							,A :			Optional[Any]=5							,A :			Optional[int]=4							,A :			List[Any]=37							,A :			Tuple="gelu"							,A :			List[str]=0.1							,A :			List[str]=0.1							,A :			Optional[int]=5_12							,A :			List[str]=16							,A :			Union[str, Any]=2							,A :			int=0.02							,A :			List[Any]=4							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			seq_length
					__A             =			is_training
					__A             =			use_attention_mask
					__A             =			use_token_type_ids
					__A             =			use_labels
					__A             =			vocab_size
					__A             =			hidden_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			intermediate_size
					__A             =			hidden_act
					__A             =			hidden_dropout_prob
					__A             =			attention_probs_dropout_prob
					__A             =			max_position_embeddings
					__A             =			type_vocab_size
					__A             =			type_sequence_label_size
					__A             =			initializer_range
					__A             =			num_choices






	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size			)

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

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

					__A             =			BertConfig(
					    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=A							,initializer_range=self.initializer_range							,)

					return config, input_ids, token_type_ids, attention_mask






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			self.prepare_config_and_inputs()
					__A      ,							__A      ,							__A      ,							__A             =			config_and_inputs
					__A             =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict







	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.prepare_config_and_inputs()
					__A      ,							__A      ,							__A      ,							__A             =			config_and_inputs

					__A             =			True
					__A             =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
					__A             =			ids_tensor([self.batch_size, self.seq_length]							,vocab_size=2			)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)





@require_flax
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					True

	snake_case_    =					(
	    (
	        FlaxBertModel,
	        FlaxBertForPreTraining,
	        FlaxBertForMaskedLM,
	        FlaxBertForMultipleChoice,
	        FlaxBertForQuestionAnswering,
	        FlaxBertForNextSentencePrediction,
	        FlaxBertForSequenceClassification,
	        FlaxBertForTokenClassification,
	        FlaxBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)






	def     UpperCamelCase_  (	self :			str			):
					__A             =			FlaxBertModelTester(self			)







	@slow
	def     UpperCamelCase_  (	self :			Tuple			):
					# Only check this for base model, not necessary for all model classes.
					# This will also help speed-up tests.
					__A             =			FlaxBertModel.from_pretrained("bert-base-cased"			)
					__A             =			model(np.ones((1, 1)			)			)
					self.assertIsNotNone(A			)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

    # print entropy
				print(F'''{round(-1 * my_fir_sum						):.1f}'''						)

				# two len string
				__A             =			sum(two_char_strings.values()						)
				__A             =			0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__A             =			cha + cha
												if sequence in two_char_strings:
																__A             =			two_char_strings[sequence]
																__A             =			int(a_						) / all_sum
																my_sec_sum += prob * math.loga(a_						)

    # print second entropy
				print(F'''{round(-1 * my_sec_sum						):.1f}'''						)

				# print the difference between them
				print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)						):.1f}'''						)


def 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0		,       len(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""simple docstring"""



				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()
