from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__           =				{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__           =				['LlamaTokenizer']

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__           =				[
		    'OpenLlamaForCausalLM',
		    'OpenLlamaModel',
		    'OpenLlamaPreTrainedModel',
		    'OpenLlamaForSequenceClassification',
		]


if TYPE_CHECKING:
		from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from transformers import LlamaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from transformers import LlamaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_open_llama import (
				    OpenLlamaForCausalLM,
				    OpenLlamaForSequenceClassification,
				    OpenLlamaModel,
				    OpenLlamaPreTrainedModel,
				)


else:
		import sys

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






import os
from string import ascii_letters

lowerCamelCase__           =				ascii_letters + ' \t\n'



def 							_lowerCamelCase(				)  ->   dict[str, None]:
		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
		__snake_case		       =     {}
		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
				for word in dictionary_file.read().split("\n"	):
						__snake_case		       =     None
		return english_words


lowerCamelCase__           =				load_dictionary()



def 							_lowerCamelCase(				__snake_case	)  ->   float:
		__snake_case		       =     message.upper()
		__snake_case		       =     remove_non_letters(__snake_case	)
		__snake_case		       =     message.split()
		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
		return float(__snake_case	) / len(__snake_case	)



def 							_lowerCamelCase(				__snake_case	)  ->   str:
		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)



def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
		__snake_case		       =     message_letters_percentage >= letter_percentage
		return words_match and letters_match


if __name__ == "__main__":
		import doctest

		doctest.testmod()





