



def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[Any]					     =					[]
						lowerCAmelCase	:   str					     =					len(lowercase__  )
						for i in range(lowercase__  ):
												lowerCAmelCase	:   str					     =					[]
												for j in range(lowercase__  ):
																		lowerCAmelCase	:   List[str]					     =					0
																		for k in range(lowercase__  ):
																								lowerCAmelCase	:   Optional[int]					     =					val + matrix_a[i][k] * matrix_b[k][j]
																		list_a.append(lowercase__  )
												matrix_c.append(lowercase__  )
						return matrix_c







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						return [[int(row == column  ) for column in range(lowercase__  )] for row in range(lowercase__  )]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						if n <= 1:
												return n
						lowerCAmelCase	:   Optional[Any]					     =					identity(2  )
						lowerCAmelCase	:   Dict					     =					[[1, 1], [1, 0]]
						lowerCAmelCase	:   List[str]					     =					n - 1
						while n > 0:
												if n % 2 == 1:
																		lowerCAmelCase	:   str					     =					multiply(lowercase__			,lowercase__  )
												lowerCAmelCase	:   Any					     =					multiply(lowercase__			,lowercase__  )
												lowerCAmelCase	:   Optional[int]					     =					int(n / 2  )
						return res_matrix[0][0]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						if n <= 1:
												return n
						lowerCAmelCase	:   List[str]					     =					0
						lowerCAmelCase	:   List[str]					     =					1
						for _ in range(2			,n + 1  ):
												lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					fiba, fiba + fiba
						return fiba







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
												lowerCAmelCase	:   Tuple					     =					int("""""".join(c for c in ordinal if c in """0123456789"""  )  )  # 1000th --> 1000
												print(
												    F"""{ordinal} fibonacci number using matrix exponentiation is """
												    F"""{nth_fibonacci_matrix(lowercase__  )} and using bruteforce is """
												    F"""{nth_fibonacci_bruteforce(lowercase__  )}\n"""  )
						# from timeit import timeit
						# print(timeit("nth_fibonacci_matrix(1000000)",
						#              "from main import nth_fibonacci_matrix", number=5))
						# print(timeit("nth_fibonacci_bruteforce(1000000)",
						#              "from main import nth_fibonacci_bruteforce", number=5))
						# 2.3342058970001744
						# 57.256506615000035


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()









'''simple docstring'''





from typing import TYPE_CHECKING

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


__A				:				Union[str, Any]					=		{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""GPT2TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[int]					=		[
     """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPT2DoubleHeadsModel""",
     """GPT2ForQuestionAnswering""",
     """GPT2ForSequenceClassification""",
     """GPT2ForTokenClassification""",
     """GPT2LMHeadModel""",
     """GPT2Model""",
     """GPT2PreTrainedModel""",
     """load_tf_weights_in_gpt2""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[Any]					=		[
     """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFGPT2DoubleHeadsModel""",
     """TFGPT2ForSequenceClassification""",
     """TFGPT2LMHeadModel""",
     """TFGPT2MainLayer""",
     """TFGPT2Model""",
     """TFGPT2PreTrainedModel""",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Any					=		["""TFGPT2Tokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 __A				:				Tuple					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

