





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,
)


__lowercase       :Tuple       =  {
    "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:
				__lowercase       :str       =  ["CLIPTokenizerFast"]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Optional[int]       =  ["CLIPFeatureExtractor"]
				__lowercase       :Union[str, Any]       =  ["CLIPImageProcessor"]

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

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

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Optional[int]       =  [
				    "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

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






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) < k or k < 0:
												raise ValueError("Invalid Input"     )
						SCREAMING_SNAKE_CASE__     :				int   =					sum(array[:k]     )
						for i in range(len(_lowerCamelCase     ) - k     ):
												SCREAMING_SNAKE_CASE__     :				str   =					current_sum - array[i] + array[i + k]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max(_lowerCamelCase			,   _lowerCamelCase     )
						return max_sum


if __name__ == "__main__":
				from doctest import testmod
				from random import randint

				testmod()
				__lowercase       :List[str]       =  [randint(-1_000, 1_000) for i in range(100)]
				__lowercase       :Any       =  randint(0, 110)
				print(f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")