





'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase					    =	{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

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

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase					    =	["CLIPFeatureExtractor"]
	__UpperCamelCase					    =	["CLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase					    =	[
	    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "CLIPModel",
	    "CLIPPreTrainedModel",
	    "CLIPTextModel",
	    "CLIPTextModelWithProjection",
	    "CLIPVisionModel",
	    "CLIPVisionModelWithProjection",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase					    =	[
	    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFCLIPModel",
	    "TFCLIPPreTrainedModel",
	    "TFCLIPTextModel",
	    "TFCLIPVisionModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase					    =	[
	    "FlaxCLIPModel",
	    "FlaxCLIPPreTrainedModel",
	    "FlaxCLIPTextModel",
	    "FlaxCLIPTextPreTrainedModel",
	    "FlaxCLIPVisionModel",
	    "FlaxCLIPVisionPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	__UpperCamelCase					    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




from collections.abc import Sequence
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Sequence[int] | None = None				)			->						int:
       if nums is None or not nums:
              raise ValueError('''Input sequence should not be empty'''				)

       SCREAMING_SNAKE_CASE_							:				Tuple		=nums[0]
       for i in range(1			,							len(UpperCAmelCase_				)				):
              SCREAMING_SNAKE_CASE_							:				Any		=nums[i]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=max(UpperCAmelCase_			,							ans + num			,							UpperCAmelCase_				)

       return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 _lowercase											=    int(input("""Enter number of elements : """).strip())
 _lowercase											=    list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
 print(max_subsequence_sum(array))
