
"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_snake_case          =						{
    '''E''': 12.70,
    '''T''': 9.06,
    '''A''': 8.17,
    '''O''': 7.51,
    '''I''': 6.97,
    '''N''': 6.75,
    '''S''': 6.33,
    '''H''': 6.09,
    '''R''': 5.99,
    '''D''': 4.25,
    '''L''': 4.03,
    '''C''': 2.78,
    '''U''': 2.76,
    '''M''': 2.41,
    '''W''': 2.36,
    '''F''': 2.23,
    '''G''': 2.02,
    '''Y''': 1.97,
    '''P''': 1.93,
    '''B''': 1.29,
    '''V''': 0.98,
    '''K''': 0.77,
    '''J''': 0.15,
    '''X''': 0.15,
    '''Q''': 0.10,
    '''Z''': 0.07,
}
_snake_case          =						'''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
_snake_case          =						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					tuple						):






							"""simple docstring"""



							return x[0]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



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

							_lowerCAmelCase  	=			{}

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

							_lowerCAmelCase  	=			list(freq_to_letter_str.items()						)
							freq_pairs.sort(key=SCREAMING_SNAKE_CASE     ,  reverse=SCREAMING_SNAKE_CASE						)

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

							return "".join(SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			get_frequency_order(SCREAMING_SNAKE_CASE						)
							_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()








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case          =						{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						['''RoFormerTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RoFormerForCausalLM''',
	    '''RoFormerForMaskedLM''',
	    '''RoFormerForMultipleChoice''',
	    '''RoFormerForQuestionAnswering''',
	    '''RoFormerForSequenceClassification''',
	    '''RoFormerForTokenClassification''',
	    '''RoFormerLayer''',
	    '''RoFormerModel''',
	    '''RoFormerPreTrainedModel''',
	    '''load_tf_weights_in_roformer''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRoFormerForCausalLM''',
	    '''TFRoFormerForMaskedLM''',
	    '''TFRoFormerForMultipleChoice''',
	    '''TFRoFormerForQuestionAnswering''',
	    '''TFRoFormerForSequenceClassification''',
	    '''TFRoFormerForTokenClassification''',
	    '''TFRoFormerLayer''',
	    '''TFRoFormerModel''',
	    '''TFRoFormerPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FlaxRoFormerForMaskedLM''',
	    '''FlaxRoFormerForMultipleChoice''',
	    '''FlaxRoFormerForQuestionAnswering''',
	    '''FlaxRoFormerForSequenceClassification''',
	    '''FlaxRoFormerForTokenClassification''',
	    '''FlaxRoFormerModel''',
	    '''FlaxRoFormerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
	from .tokenization_roformer import RoFormerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roformer_fast import RoFormerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roformer import (
		    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoFormerForCausalLM,
		    RoFormerForMaskedLM,
		    RoFormerForMultipleChoice,
		    RoFormerForQuestionAnswering,
		    RoFormerForSequenceClassification,
		    RoFormerForTokenClassification,
		    RoFormerLayer,
		    RoFormerModel,
		    RoFormerPreTrainedModel,
		    load_tf_weights_in_roformer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roformer import (
		    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerLayer,
		    TFRoFormerModel,
		    TFRoFormerPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roformer import (
		    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		    FlaxRoFormerPreTrainedModel,
		)


else:
	import sys

	_snake_case          =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






