



'''simple docstring'''




import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    MobileBertForMaskedLM,
						    MobileBertForMultipleChoice,
						    MobileBertForNextSentencePrediction,
						    MobileBertForPreTraining,
						    MobileBertForQuestionAnswering,
						    MobileBertForSequenceClassification,
						    MobileBertForTokenClassification,
						    MobileBertModel,
						)






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : str							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Optional[Any]=1_3							,     lowerCAmelCase__     : Optional[Any]=7							,     lowerCAmelCase__     : List[Any]=True							,     lowerCAmelCase__     : Tuple=True							,     lowerCAmelCase__     : Optional[Any]=True							,     lowerCAmelCase__     : Any=True							,     lowerCAmelCase__     : Tuple=9_9							,     lowerCAmelCase__     : Dict=6_4							,     lowerCAmelCase__     : Union[str, Any]=3_2							,     lowerCAmelCase__     : Optional[int]=5							,     lowerCAmelCase__     : Dict=4							,     lowerCAmelCase__     : Any=3_7							,     lowerCAmelCase__     : List[Any]="gelu"							,     lowerCAmelCase__     : List[str]=0.1							,     lowerCAmelCase__     : Union[str, Any]=0.1							,     lowerCAmelCase__     : str=5_1_2							,     lowerCAmelCase__     : str=1_6							,     lowerCAmelCase__     : Any=2							,     lowerCAmelCase__     : str=0.02							,     lowerCAmelCase__     : Any=3							,     lowerCAmelCase__     : str=4							,     lowerCAmelCase__     : Union[str, Any]=None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		parent
					__SCREAMING_SNAKE_CASE       :		Tuple           =		batch_size
					__SCREAMING_SNAKE_CASE       :		Any           =		seq_length
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		is_training
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		use_input_mask
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		use_token_type_ids
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		use_labels
					__SCREAMING_SNAKE_CASE       :		Any           =		vocab_size
					__SCREAMING_SNAKE_CASE       :		str           =		hidden_size
					__SCREAMING_SNAKE_CASE       :		str           =		embedding_size
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		num_hidden_layers
					__SCREAMING_SNAKE_CASE       :		str           =		num_attention_heads
					__SCREAMING_SNAKE_CASE       :		str           =		intermediate_size
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		hidden_act
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE       :		str           =		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		max_position_embeddings
					__SCREAMING_SNAKE_CASE       :		List[str]           =		type_vocab_size
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		type_sequence_label_size
					__SCREAMING_SNAKE_CASE       :		str           =		initializer_range
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		num_labels
					__SCREAMING_SNAKE_CASE       :		Any           =		num_choices
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		scope

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		None
					if self.use_input_mask:
							__SCREAMING_SNAKE_CASE       :		str           =		random_attention_mask([self.batch_size, self.seq_length]						)

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

					__SCREAMING_SNAKE_CASE       :		Tuple           =		None
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		None
					__SCREAMING_SNAKE_CASE       :		Dict           =		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		ids_tensor([self.batch_size]							,     self.type_sequence_label_size						)
							__SCREAMING_SNAKE_CASE       :		Any           =		ids_tensor([self.batch_size, self.seq_length]							,     self.num_labels						)
							__SCREAMING_SNAKE_CASE       :		int           =		ids_tensor([self.batch_size]							,     self.num_choices						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					return MobileBertConfig(
					    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							,     embedding_size=self.embedding_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							,     )

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		MobileBertModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Dict           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		model(lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		model(lowerCAmelCase__						)

					self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)						)
					self.parent.assertEqual(result.pooler_output.shape							,     (self.batch_size, self.hidden_size)						)

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		MobileBertForMaskedLM(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Tuple           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		MobileBertForNextSentencePrediction(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		str           =		model(
					    lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     labels=lowerCAmelCase__							,     )
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, 2)						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		MobileBertForPreTraining(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model(
					    lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     labels=lowerCAmelCase__							,     next_sentence_label=lowerCAmelCase__							,     )
					self.parent.assertEqual(result.prediction_logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)						)
					self.parent.assertEqual(result.seq_relationship_logits.shape							,     (self.batch_size, 2)						)

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		MobileBertForQuestionAnswering(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Dict           =		model(
					    lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     start_positions=lowerCAmelCase__							,     end_positions=lowerCAmelCase__							,     )
					self.parent.assertEqual(result.start_logits.shape							,     (self.batch_size, self.seq_length)						)
					self.parent.assertEqual(result.end_logits.shape							,     (self.batch_size, self.seq_length)						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.num_labels
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		MobileBertForSequenceClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Any           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		self.num_labels
					__SCREAMING_SNAKE_CASE       :		Any           =		MobileBertForTokenClassification(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.num_labels)						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.num_choices
					__SCREAMING_SNAKE_CASE       :		Tuple           =		MobileBertForMultipleChoice(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Any           =		input_ids.unsqueeze(1						).expand(-1							,     self.num_choices							,     -1						).contiguous()
					__SCREAMING_SNAKE_CASE       :		Dict           =		token_type_ids.unsqueeze(1						).expand(-1							,     self.num_choices							,     -1						).contiguous()
					__SCREAMING_SNAKE_CASE       :		List[str]           =		input_mask.unsqueeze(1						).expand(-1							,     self.num_choices							,     -1						).contiguous()
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model(
					    lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     token_type_ids=lowerCAmelCase__							,     labels=lowerCAmelCase__							,     )
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_choices)						)



			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






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






@require_torch
class 						_UpperCamelCase  (							lowerCamelCase__    ,					lowerCamelCase__    ,					unittest.TestCase    ):


			'''simple docstring'''

			_A      :		Tuple			    =		(
			    (
			        MobileBertModel,
			        MobileBertForMaskedLM,
			        MobileBertForMultipleChoice,
			        MobileBertForNextSentencePrediction,
			        MobileBertForPreTraining,
			        MobileBertForQuestionAnswering,
			        MobileBertForSequenceClassification,
			        MobileBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			_A      :		List[str]			    =		(
			    {
			        '''feature-extraction''': MobileBertModel,
			        '''fill-mask''': MobileBertForMaskedLM,
			        '''question-answering''': MobileBertForQuestionAnswering,
			        '''text-classification''': MobileBertForSequenceClassification,
			        '''token-classification''': MobileBertForTokenClassification,
			        '''zero-shot''': MobileBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			_A      :		int			    =		True

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : Union[str, Any]=False						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		super()._prepare_for_class(lowerCAmelCase__							,     lowerCAmelCase__							,     return_labels=lowerCAmelCase__						)

					if return_labels:
							if model_class in get_values(lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		torch.zeros(
									    (self.model_tester.batch_size, self.model_tester.seq_length)							,     dtype=torch.long							,     device=lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE       :		Tuple           =		torch.zeros(
									    self.model_tester.batch_size							,     dtype=torch.long							,     device=lowerCAmelCase__						)
					return inputs_dict

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		MobileBertModelTester(self						)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		ConfigTester(self							,     config_class=lowerCAmelCase__							,     hidden_size=3_7						)

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					self.config_tester.run_common_tests()

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase__						)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase__						)



			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase__						)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[Any]					):
		return torch.tensor(
		    _lowerCamelCase       ,      dtype=torch.long       ,      device=_lowerCamelCase       ,      )


UpperCamelCase__       :	Tuple    = 1E-3






@require_torch
@require_sentencepiece
@require_tokenizers
class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		MobileBertModel.from_pretrained("""google/mobilebert-uncased"""						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		_long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]						)
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE       :		int           =		model(lowerCAmelCase__						)[0]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		torch.Size((1, 9, 5_1_2)						)
					self.assertEqual(output.shape							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		torch.tensor(
					    [
					        [
					            [-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
					            [-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
					            [2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
					        ]
					    ]							,     device=lowerCAmelCase__							,     )

					# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
					# ~1 difference, it's therefore not a good idea to measure using addition.
					# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
					# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE						)

					self.assertTrue(lower_bound and upper_bound						)




'''simple docstring'''




import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCamelCase__       :	List[Any]    = {
    '''E''': 1_2.7_0,
    '''T''': 9.0_6,
    '''A''': 8.1_7,
    '''O''': 7.5_1,
    '''I''': 6.9_7,
    '''N''': 6.7_5,
    '''S''': 6.3_3,
    '''H''': 6.0_9,
    '''R''': 5.9_9,
    '''D''': 4.2_5,
    '''L''': 4.0_3,
    '''C''': 2.7_8,
    '''U''': 2.7_6,
    '''M''': 2.4_1,
    '''W''': 2.3_6,
    '''F''': 2.2_3,
    '''G''': 2.0_2,
    '''Y''': 1.9_7,
    '''P''': 1.9_3,
    '''B''': 1.2_9,
    '''V''': 0.9_8,
    '''K''': 0.7_7,
    '''J''': 0.1_5,
    '''X''': 0.1_5,
    '''Q''': 0.1_0,
    '''Z''': 0.0_7,
}
UpperCamelCase__       :	Optional[Any]    = '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
UpperCamelCase__       :	Dict    = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		int           =		{letter: 0 for letter in string.ascii_uppercase}
		for letter in message.upper():
				if letter in LETTERS:
						letter_count[letter] += 1

		return letter_count


def 				lowerCAmelCase_   (	_lowerCamelCase:   tuple					):
		return x[0]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		get_letter_count(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		dict[int, list[str]]           =		{
		    freq: [] for letter, freq in letter_to_freq.items()
		}
		for letter in LETTERS:
				freq_to_letter[letter_to_freq[letter]].append(_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		dict[int, str]           =		{}

		for freq in freq_to_letter:
				freq_to_letter[freq].sort(key=ETAOIN.find       ,      reverse=_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		"""""".join(freq_to_letter[freq]					)

		__SCREAMING_SNAKE_CASE       :		str           =		list(freq_to_letter_str.items()					)
		freq_pairs.sort(key=_lowerCamelCase       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		list[str]           =		[freq_pair[1] for freq_pair in freq_pairs]

		return "".join(_lowerCamelCase					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_frequency_order(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		for common_letter in ETAOIN[:6]:
				if common_letter in freq_order[:6]:
						match_score += 1

		for uncommon_letter in ETAOIN[-6:]:
				if uncommon_letter in freq_order[-6:]:
						match_score += 1

		return match_score


if __name__ == "__main__":
						import doctest

						doctest.testmod()