





from typing import TYPE_CHECKING

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


snake_case_           =     {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =     ["BlipImageProcessor"]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =     [
        "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BlipModel",
        "BlipPreTrainedModel",
        "BlipForConditionalGeneration",
        "BlipForQuestionAnswering",
        "BlipVisionModel",
        "BlipTextModel",
        "BlipForImageTextRetrieval",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =     [
        "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFBlipModel",
        "TFBlipPreTrainedModel",
        "TFBlipForConditionalGeneration",
        "TFBlipForQuestionAnswering",
        "TFBlipVisionModel",
        "TFBlipTextModel",
        "TFBlipForImageTextRetrieval",
    ]

if TYPE_CHECKING:
    from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
    from .processing_blip import BlipProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_blip import BlipImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blip import (
            BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlipForConditionalGeneration,
            BlipForImageTextRetrieval,
            BlipForQuestionAnswering,
            BlipModel,
            BlipPreTrainedModel,
            BlipTextModel,
            BlipVisionModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blip import (
            TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFBlipForConditionalGeneration,
            TFBlipForImageTextRetrieval,
            TFBlipForQuestionAnswering,
            TFBlipModel,
            TFBlipPreTrainedModel,
            TFBlipTextModel,
            TFBlipVisionModel,
        )

else:
    import sys

    snake_case_           =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A_				:      int           =			pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
A_				:      int           =			dataset.iloc[:, 1:2].values
A_				:      Optional[Any]           =			dataset.iloc[:, 2].values


A_       ,						A_       ,						A_       ,						A_				:      str           =			train_test_split(X, y, test_size=0.2, random_state=0)


A_				:      Optional[Any]           =			PolynomialFeatures(degree=4)
A_				:      Tuple           =			poly_reg.fit_transform(X)
A_				:      str           =			LinearRegression()
pol_reg.fit(X_poly, y)

def 						A							(  ):




			'''simple docstring'''


			plt.scatter(snake_case__		,					snake_case__		,					color="""red"""     )
			plt.plot(snake_case__		,					pol_reg.predict(poly_reg.fit_transform(snake_case__     )     )		,					color="""blue"""     )
			plt.title("""Truth or Bluff (Linear Regression)"""     )
			plt.xlabel("""Position level"""     )
			plt.ylabel("""Salary"""     )
			plt.show()


if __name__ == "__main__":
						viz_polymonial()

						# Predicting a new result with Polymonial Regression
						pol_reg.predict(poly_reg.fit_transform([[5.5]]))
						# output should be 132148.43750003


