






import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				)

class 		_snake_case	(	unittest.TestCase       ):






			def __init__(							self		,       a		,       a=13		,       a=7		,       a=True		,       a=True		,       a=True		,       a=True		,       a=99		,       a=32		,       a=5		,       a=4		,       a=37		,       a="gelu"		,       a=0.1		,       a=0.1		,       a=512		,       a=16		,       a=2		,       a=0.02		,       a=4		,       )   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			parent
						SCREAMING_SNAKE_CASE							=			batch_size
						SCREAMING_SNAKE_CASE							=			seq_length
						SCREAMING_SNAKE_CASE							=			is_training
						SCREAMING_SNAKE_CASE							=			use_attention_mask
						SCREAMING_SNAKE_CASE							=			use_token_type_ids
						SCREAMING_SNAKE_CASE							=			use_labels
						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			intermediate_size
						SCREAMING_SNAKE_CASE							=			hidden_act
						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			max_position_embeddings
						SCREAMING_SNAKE_CASE							=			type_vocab_size
						SCREAMING_SNAKE_CASE							=			type_sequence_label_size
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			num_choices






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size)

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

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

						SCREAMING_SNAKE_CASE							=			RobertaConfig(
						    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  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			config_and_inputs
						SCREAMING_SNAKE_CASE							=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						SCREAMING_SNAKE_CASE							=			self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			config_and_inputs

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

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



@require_flax
class 		_snake_case	(	A__ ,     unittest.TestCase       ):
			_lowercase		:     List[Any]        =				True

			_lowercase		:     List[Any]        =				(
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaxRobertaModelTester(self)







			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE							=			model_class_name.from_pretrained('roberta-base'		,       from_pt=lowerCamelCase__)
									SCREAMING_SNAKE_CASE							=			model(np.ones((1, 1)))
									self.assertIsNotNone(lowerCamelCase__)










from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       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:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

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

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

    # 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      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       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(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""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()


