





'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__		:     List[str]			=							{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

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

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     Union[str, Any]			=							[
       'LlamaForCausalLM',
       'LlamaModel',
       'LlamaPreTrainedModel',
       'LlamaForSequenceClassification',
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

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

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

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   lowercase__		:     Optional[Any]			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








from collections.abc import Callable

import numpy as np






def  lowerCamelCase_	(      lowerCAmelCase__	:	Callable			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float				)       ->							np.array:

							'''simple docstring'''






							A      						=    int(np.ceil((x_end - xa) / step_size				)				)
							A      						=    np.zeros((n + 1,)				)
							A      						=    ya
							A      						=    xa

							for k in range(lowerCAmelCase__				):
														A      						=    y[k] + step_size * ode_func(lowerCAmelCase__			,	y[k]				)
														A      						=    y[k] + (
														    (step_size / 2) * (ode_func(lowerCAmelCase__			,	y[k]				) + ode_func(x + step_size			,	lowerCAmelCase__				))
														)
														x += step_size

							return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()