




from typing import TYPE_CHECKING

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


__UpperCamelCase					:       List[Any]    					=      {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Dict    					=      ["XGLMTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       List[Any]    					=      ["XGLMTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       int    					=      [
		    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "XGLMForCausalLM",
		    "XGLMModel",
		    "XGLMPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Optional[int]    					=      [
		    "FlaxXGLMForCausalLM",
		    "FlaxXGLMModel",
		    "FlaxXGLMPreTrainedModel",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Optional[Any]    					=      [
		    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFXGLMForCausalLM",
		    "TFXGLMModel",
		    "TFXGLMPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm import XGLMTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm_fast import XGLMTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				    TFXGLMPreTrainedModel,
				)


else:
		import sys

		__UpperCamelCase					:       Optional[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure)







from __future__ import annotations





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       float:
	a										=							sorted(numsa + numsa    )
	a				, a										=							divmod(len(__lowerCamelCase    )							,			2    )
	if mod == 1:
		return all_numbers[div]
	else:
		return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__UpperCamelCase					:       Tuple    					=      [float(x) for x in input("Enter the elements of first array: ").split()]
		__UpperCamelCase					:       List[Any]    					=      [float(x) for x in input("Enter the elements of second array: ").split()]
		print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')

