
from typing import TYPE_CHECKING

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


lowercase__				:				Dict						=      {
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

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

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__				:				List[str]						=      ["LlamaTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__				:				int						=      [
				    "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

				lowercase__				:				Union[str, Any]						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












def   snake_case		(				UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							str ):
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				A									=   (boundary[1] - boundary[0]) / steps
				A									=   boundary[0]
				A									=   boundary[1]
				A									=   make_points(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				A									=   0.0
				y += (h / 3.0) * f(UpperCAmelCase )
				A									=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase )
								cnt += 1
				y += (h / 3.0) * f(UpperCAmelCase )
				return y







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int,    UpperCAmelCase						:							List[Any] ):
				A									=   a + h
				while x < (b - h):
								yield x
								A									=   x + h







def   snake_case		(				UpperCAmelCase						:							str ):  # enter your function here
				A									=   (x - 0) * (x - 0)
				return y







def   snake_case		(				):
				A									=   0.0  # Lower bound of integration
				A									=   1.0  # Upper bound of integration
				A									=   10.0  # define number of steps or resolution
				A									=   [a, b]  # define boundary of integration
				A									=   method_a(UpperCAmelCase,    UpperCAmelCase )
				print(f'y = {y}' )


if __name__ == "__main__":
					main()





