

"""simple docstring"""




from typing import TYPE_CHECKING

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


__A  :					Union[str, Any]												=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					List[str]												=	['CamembertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					Union[str, Any]												=	['CamembertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					List[Any]												=	[
	    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CamembertForCausalLM',
	    'CamembertForMaskedLM',
	    'CamembertForMultipleChoice',
	    'CamembertForQuestionAnswering',
	    'CamembertForSequenceClassification',
	    'CamembertForTokenClassification',
	    'CamembertModel',
	    'CamembertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					Dict												=	[
	    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFCamembertForCausalLM',
	    'TFCamembertForMaskedLM',
	    'TFCamembertForMultipleChoice',
	    'TFCamembertForQuestionAnswering',
	    'TFCamembertForSequenceClassification',
	    'TFCamembertForTokenClassification',
	    'TFCamembertModel',
	    'TFCamembertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert import CamembertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert_fast import CamembertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_camembert import (
		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CamembertForCausalLM,
		    CamembertForMaskedLM,
		    CamembertForMultipleChoice,
		    CamembertForQuestionAnswering,
		    CamembertForSequenceClassification,
		    CamembertForTokenClassification,
		    CamembertModel,
		    CamembertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_camembert import (
		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCamembertForCausalLM,
		    TFCamembertForMaskedLM,
		    TFCamembertForMultipleChoice,
		    TFCamembertForQuestionAnswering,
		    TFCamembertForSequenceClassification,
		    TFCamembertForTokenClassification,
		    TFCamembertModel,
		    TFCamembertPreTrainedModel,
		)

else:
	import sys

	__A  :					str												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




import functools
from math import sqrt
from time import time
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							*lowercase__    ,							**lowercase__   ):
					"""simple docstring"""
					A    =      time()
					A    =      func(*lowercase__    ,							**lowercase__   )
					A    =      time()
					if int(end - start   ) > 0:
										print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""   )
					else:
										print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""   )
					return output
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if n < 0:
										raise Exception("n is negative"   )
					if n == 0:
										return [0]
					A    =      [0, 1]
					for _ in range(n - 1   ):
										fib.append(fib[-1] + fib[-2]   )
					return fib
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""

					def fib_recursive_term(lowercase__   ) -> int:
										if i < 0:
															raise Exception("n is negative"   )
										if i < 2:
															return i
										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )

					if n < 0:
										raise Exception("n is negative"   )
					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""

					@functools.cache
					def fib_recursive_term(lowercase__   ) -> int:
										if i < 0:
															raise Exception("n is negative"   )
										if i < 2:
															return i
										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )

					if n < 0:
										raise Exception("n is negative"   )
					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if n < 0:
										raise Exception("n is negative"   )
					# Cache must be outside recursuive function
					# other it will reset every time it calls itself.
					A    =      {0: 0, 1: 1, 2: 1}  # Prefilled cache

					def rec_fn_memoized(lowercase__   ) -> int:
										if num in cache:
															return cache[num]

										A    =      rec_fn_memoized(num - 1   ) + rec_fn_memoized(num - 2   )
										A    =      value
										return value

					return [rec_fn_memoized(lowercase__   ) for i in range(n + 1   )]
def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					if n < 0:
										raise Exception("n is negative"   )
					if n >= 1_475:
										raise Exception("n is too large"   )
					A    =      sqrt(5   )
					A    =      (1 + sqrt_a) / 2
					return [round(phi**i / sqrt_a   ) for i in range(n + 1   )]


if __name__ == "__main__":
	__A  :					Optional[Any]												=	30
	time_func(fib_iterative, num)
	time_func(fib_recursive, num)  # Around 3s runtime
	time_func(fib_recursive_cached, num)  # Around 0ms runtime
	time_func(fib_memoization, num)
	time_func(fib_binet, num)





