"""simple docstring"""
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
     BioGptForCausalLM,
     BioGptForSequenceClassification,
     BioGptForTokenClassification,
     BioGptModel,
     BioGptTokenizer,
 )
 from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=13    ,			lowercase=7    ,			lowercase=True    ,			lowercase=True    ,			lowercase=False    ,			lowercase=True    ,			lowercase=99    ,			lowercase=32    ,			lowercase=5    ,			lowercase=4    ,			lowercase=37    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=16    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=3    ,			lowercase=4    ,			lowercase=None    ,			)	->			Any:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

        a__:     Dict										=	None
        if self.use_input_mask:
          a__:     str										=	random_attention_mask([self.batch_size, self.seq_length])

        a__:     Dict										=	None
        if self.use_token_type_ids:
          a__:     List[Any]										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size)

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

        a__:     Union[str, Any]										=	self.get_config()

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



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return BioGptConfig(
            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    ,			)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     Any										=	BioGptModel(config=lowerCamelCase__)
        model.to(lowerCamelCase__)
        model.eval()
        a__:     List[Any]										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__)
        a__:     Union[str, Any]										=	model(lowerCamelCase__)
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			str:





        '''simple docstring'''





        a__:     str										=	BioGptForCausalLM(config=lowerCamelCase__)
        model.to(lowerCamelCase__)
        model.eval()
        a__:     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.vocab_size))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			*lowercase)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	BioGptModel(config=lowerCamelCase__)
        model.to(lowerCamelCase__)
        model.eval()

        # create attention mask
        a__:     List[Any]										=	torch.ones(input_ids.shape    ,			dtype=torch.long    ,			device=lowerCamelCase__)
        a__:     Dict										=	self.seq_length // 2
        a__:     Dict										=	0

        # first forward pass
        a__							,    a__:     Dict										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__).to_tuple()

        # create hypothetical next token and extent to next_input_ids
        a__:     Dict										=	ids_tensor((self.batch_size, 1)    ,			config.vocab_size)

        # change a random masked slice from input_ids
        a__:     Union[str, Any]										=	ids_tensor((1,)    ,			lowerCamelCase__).item() + 1
        a__:     Optional[Any]										=	ids_tensor((self.batch_size, 1)    ,			config.vocab_size).squeeze(-1)
        a__:     str										=	random_other_next_tokens

        # append to next input_ids and attn_mask
        a__:     int										=	torch.cat([input_ids, next_tokens]    ,			dim=-1)
        a__:     Optional[int]										=	torch.cat(
            [attn_mask, torch.ones((attn_mask.shape[0], 1)    ,			dtype=torch.long    ,			device=lowerCamelCase__)]    ,			dim=1    ,			)

        # get two different outputs
        a__:     Optional[Any]										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__)['last_hidden_state']
        a__:     Optional[Any]										=	model(lowerCamelCase__    ,			past_key_values=lowerCamelCase__    ,			attention_mask=lowerCamelCase__)['last_hidden_state']

        # select random slice
        a__:     List[Any]										=	ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
        a__:     List[str]										=	output_from_no_past[:, -1, random_slice_idx].detach()
        a__:     Tuple										=	output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(lowerCamelCase__    ,			lowerCamelCase__    ,			atol=1e-3))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			*lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     Any										=	BioGptModel(config=lowerCamelCase__).to(lowerCamelCase__).eval()

        a__:     Optional[Any]										=	torch.ones(input_ids.shape    ,			dtype=torch.long    ,			device=lowerCamelCase__)

        # first forward pass
        a__:     int										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__    ,			use_cache=lowerCamelCase__)

        a__							,    a__:     List[str]										=	outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        a__:     List[str]										=	ids_tensor((self.batch_size, 3)    ,			config.vocab_size)
        a__:     Any										=	ids_tensor((self.batch_size, 3)    ,			2)

        # append to next input_ids and
        a__:     str										=	torch.cat([input_ids, next_tokens]    ,			dim=-1)
        a__:     Dict										=	torch.cat([attention_mask, next_attn_mask]    ,			dim=-1)

        a__:     int										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__)['last_hidden_state']
        a__:     Tuple										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__    ,			past_key_values=lowerCamelCase__)[
            'last_hidden_state'
        ]

        # select random slice
        a__:     List[str]										=	ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
        a__:     Tuple										=	output_from_no_past[:, -3:, random_slice_idx].detach()
        a__:     str										=	output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(lowerCamelCase__    ,			lowerCamelCase__    ,			atol=1e-3))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			*lowercase    ,			lowercase=False)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     int										=	BioGptForCausalLM(lowerCamelCase__)
        model.to(lowerCamelCase__)
        if gradient_checkpointing:
          model.gradient_checkpointing_enable()

        a__:     str										=	model(lowerCamelCase__    ,			labels=lowerCamelCase__)
        self.parent.assertEqual(result.loss.shape    ,			())
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))
        result.loss.backward()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			*lowercase)	->			List[Any]:





        '''simple docstring'''





        a__:     Any										=	BioGptModel(lowerCamelCase__)
        a__:     List[str]										=	model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
        for key in model.state_dict().keys():
          if "c_proj" in key and "weight" in key:
            self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std)    ,			0.001)
            self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)    ,			0.01)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			*lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.num_labels
        a__:     Optional[Any]										=	BioGptForTokenClassification(lowerCamelCase__)
        model.to(lowerCamelCase__)
        model.eval()
        a__:     int										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__    ,			token_type_ids=lowerCamelCase__)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels))



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	self.prepare_config_and_inputs()
        (
            (
            a__						
        )							,    (
            a__						
        )							,    (
            a__						
        )							,    (
            a__						
        )							,    (
            a__						
        )							,    (
            a__						
        )							,    (
            a__						
        )							,    						
        ):     Any										=	config_and_inputs
        a__:     Optional[int]										=	{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      a__        =		(BioGptForCausalLM,) if is_torch_available() else ()
      a__        =		(
          {
              """feature-extraction""": BioGptModel,
              """text-classification""": BioGptForSequenceClassification,
              """text-generation""": BioGptForCausalLM,
              """token-classification""": BioGptForTokenClassification,
              """zero-shot""": BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      a__        =		False



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     int										=	BioGptModelTester(self)
        a__:     Tuple										=	ConfigTester(self    ,			config_class=lowerCamelCase__    ,			hidden_size=37)



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        self.config_tester.run_common_tests()



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          a__:     str										=	type
          self.model_tester.create_and_check_model(*lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase__    ,			gradient_checkpointing=lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Any										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Any										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase__)



      @slow
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Any										=	BioGptForCausalLM.from_pretrained('microsoft/biogpt')
        model.to(lowerCamelCase__)
        a__:     Tuple										=	BioGptTokenizer.from_pretrained('microsoft/biogpt')

        a__:     List[Any]										=	'left'

        # Define PAD Token = EOS Token = 50256
        a__:     str										=	tokenizer.eos_token
        a__:     str										=	model.config.eos_token_id

        # use different length sentences to test batching
        a__:     Optional[int]										=	[
            'Hello, my dog is a little',
            'Today, I',
        ]

        a__:     Tuple										=	tokenizer(lowerCamelCase__    ,			return_tensors='pt'    ,			padding=lowerCamelCase__)
        a__:     int										=	inputs['input_ids'].to(lowerCamelCase__)

        a__:     Tuple										=	model.generate(
            input_ids=lowerCamelCase__    ,			attention_mask=inputs['attention_mask'].to(lowerCamelCase__)    ,			)

        a__:     Any										=	tokenizer(sentences[0]    ,			return_tensors='pt').input_ids.to(lowerCamelCase__)
        a__:     str										=	model.generate(input_ids=lowerCamelCase__)

        a__:     Optional[int]										=	inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
        a__:     Tuple										=	tokenizer(sentences[1]    ,			return_tensors='pt').input_ids.to(lowerCamelCase__)
        a__:     List[str]										=	model.generate(input_ids=lowerCamelCase__    ,			max_length=model.config.max_length - num_paddings)

        a__:     List[Any]										=	tokenizer.batch_decode(lowerCamelCase__    ,			skip_special_tokens=lowerCamelCase__)
        a__:     Optional[int]										=	tokenizer.decode(output_non_padded[0]    ,			skip_special_tokens=lowerCamelCase__)
        a__:     Union[str, Any]										=	tokenizer.decode(output_padded[0]    ,			skip_special_tokens=lowerCamelCase__)

        a__:     str										=	[
            'Hello, my dog is a little bit bigger than a little bit.',
            'Today, I have a good idea of how to use the information',
        ]
        self.assertListEqual(lowerCamelCase__    ,			lowerCamelCase__)
        self.assertListEqual(lowerCamelCase__    ,			[non_padded_sentence, padded_sentence])



      @slow
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     List[Any]										=	BioGptModel.from_pretrained(lowerCamelCase__)
          self.assertIsNotNone(lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     int										=	self.model_tester.prepare_config_and_inputs_for_common()
        a__:     Optional[int]										=	3
        a__:     Any										=	input_dict['input_ids']
        a__:     Any										=	input_ids.ne(1).to(lowerCamelCase__)
        a__:     str										=	ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
        a__:     List[Any]										=	BioGptForSequenceClassification(lowerCamelCase__)
        model.to(lowerCamelCase__)
        model.eval()
        a__:     Union[str, Any]										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__    ,			labels=lowerCamelCase__)
        self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__							,    a__:     str										=	self.model_tester.prepare_config_and_inputs_for_common()
        a__:     Any										=	3
        a__:     str										=	'multi_label_classification'
        a__:     Optional[int]										=	input_dict['input_ids']
        a__:     Any										=	input_ids.ne(1).to(lowerCamelCase__)
        a__:     Tuple										=	ids_tensor(
            [self.model_tester.batch_size, config.num_labels]    ,			self.model_tester.type_sequence_label_size).to(torch.float)
        a__:     Union[str, Any]										=	BioGptForSequenceClassification(lowerCamelCase__)
        model.to(lowerCamelCase__)
        model.eval()
        a__:     Optional[Any]										=	model(lowerCamelCase__    ,			attention_mask=lowerCamelCase__    ,			labels=lowerCamelCase__)
        self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))







@require_torch
class    __snake_case       (     unittest.TestCase				):



      @slow
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	BioGptForCausalLM.from_pretrained('microsoft/biogpt')
        a__:     List[Any]										=	torch.tensor([[2, 48_05, 9, 6_56, 21]])
        a__:     str										=	model(lowerCamelCase__)[0]

        a__:     str										=	4_23_84

        a__:     Optional[Any]										=	torch.Size((1, 5, vocab_size))
        self.assertEqual(output.shape    ,			lowerCamelCase__)

        a__:     Optional[Any]										=	torch.tensor(
            [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]])

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,			lowerCamelCase__    ,			atol=1e-4))



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Tuple										=	BioGptTokenizer.from_pretrained('microsoft/biogpt')
        a__:     Tuple										=	BioGptForCausalLM.from_pretrained('microsoft/biogpt')
        model.to(lowerCamelCase__)

        torch.manual_seed(0)
        a__:     str										=	tokenizer('COVID-19 is'    ,			return_tensors='pt').to(lowerCamelCase__)
        a__:     Union[str, Any]										=	model.generate(
            **lowerCamelCase__    ,			min_length=1_00    ,			max_length=10_24    ,			num_beams=5    ,			early_stopping=lowerCamelCase__    ,			)
        a__:     Tuple										=	tokenizer.decode(output_ids[0]    ,			skip_special_tokens=lowerCamelCase__)

        a__:     Dict										=	(
            'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
            ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
            ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
            ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
            ' more than 800,000 deaths.'
        )
        self.assertEqual(lowerCamelCase__    ,			lowerCamelCase__)








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__A        =    {
    "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,
}
__A        =    "ETAOINSHRDLCUMWFGYPBVKJXQZ"
__A        =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       dict[str, int]:





				"""simple docstring"""


				__lowerCamelCase		       =    {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_		(       UpperCamelCase__				:      tuple      )     ->       str:





				"""simple docstring"""


				return x[0]







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    get_letter_count(UpperCamelCase__      )
				__lowerCamelCase		       =    {
				    freq: [] for letter, freq in letter_to_freq.items()
				}
				for letter in LETTERS:
								freq_to_letter[letter_to_freq[letter]].append(UpperCamelCase__      )

				__lowerCamelCase		       =    {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find ,     reverse=UpperCamelCase__      )
								__lowerCamelCase		       =    ''.join(freq_to_letter[freq]      )

				__lowerCamelCase		       =    list(freq_to_letter_str.items()      )
				freq_pairs.sort(key=UpperCamelCase__ ,     reverse=UpperCamelCase__      )

				__lowerCamelCase		       =    [freq_pair[1] for freq_pair in freq_pairs]

				return "".join(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    get_frequency_order(UpperCamelCase__      )
				__lowerCamelCase		       =    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()


