



'''simple docstring'''

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

@add_end_docstrings(_a     )
class 							a__( _a     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							Any:
					super().__init__(*__UpperCamelCase					,   **__UpperCamelCase						)
					requires_backends(self					,   'vision'						)
					self.check_model_type(
					    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING						)






			def 						_lowercase  (     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Dict:
					snake_case__	     ={}
					snake_case__	     ={}

					if prompt is not None:
							snake_case__	     =prompt

					if generate_kwargs is not None:
							snake_case__	     =generate_kwargs
					if max_new_tokens is not None:
							if "generate_kwargs" not in forward_kwargs:
									snake_case__	     ={}
							if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
									raise ValueError(
									    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
									    ' please use only one'						)
							snake_case__	     =max_new_tokens
					return preprocess_params, forward_kwargs, {}






			def __call__(     self					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Dict:
					return super().__call__(__UpperCamelCase					,   **__UpperCamelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							int:
					snake_case__	     =load_image(__UpperCamelCase						)

					if prompt is not None:
							if not isinstance(__UpperCamelCase					,   __UpperCamelCase						):
									raise ValueError(
									    f"""Received an invalid text input, got - {type(__UpperCamelCase						)} - but expected a single string. """
									    'Note also that one single text can be provided for conditional image to text generation.'						)

							snake_case__	     =self.model.config.model_type

							if model_type == "git":
									snake_case__	     =self.image_processor(images=__UpperCamelCase					,   return_tensors=self.framework						)
									snake_case__	     =self.tokenizer(text=__UpperCamelCase					,   add_special_tokens=__UpperCamelCase						).input_ids
									snake_case__	     =[self.tokenizer.cls_token_id] + input_ids
									snake_case__	     =torch.tensor(__UpperCamelCase						).unsqueeze(0						)
									model_inputs.update({'input_ids': input_ids}						)

							elif model_type == "pix2struct":
									snake_case__	     =self.image_processor(images=__UpperCamelCase					,   header_text=__UpperCamelCase					,   return_tensors=self.framework						)

							elif model_type != "vision-encoder-decoder":
									# vision-encoder-decoder does not support conditional generation
									snake_case__	     =self.image_processor(images=__UpperCamelCase					,   return_tensors=self.framework						)
									snake_case__	     =self.tokenizer(__UpperCamelCase					,   return_tensors=self.framework						)
									model_inputs.update(__UpperCamelCase						)

							else:
									raise ValueError(f"""Model type {model_type} does not support conditional text generation"""						)

					else:
							snake_case__	     =self.image_processor(images=__UpperCamelCase					,   return_tensors=self.framework						)

					if self.model.config.model_type == "git" and prompt is None:
							snake_case__	     =None

					return model_inputs






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase=None						)							->							int:
					# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
					# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
					if (
					    "input_ids" in model_inputs
					    and isinstance(model_inputs['input_ids']					,   __UpperCamelCase						)
					    and all(x is None for x in model_inputs['input_ids']						)
					):
							snake_case__	     =None

					if generate_kwargs is None:
							snake_case__	     ={}
					# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
					#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
					#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
					#  in the `_prepare_model_inputs` method.
					snake_case__	     =model_inputs.pop(self.model.main_input_name						)
					snake_case__	     =self.model.generate(__UpperCamelCase					,   **__UpperCamelCase					,   **__UpperCamelCase						)
					return model_outputs






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							int:
					snake_case__	     =[]
					for output_ids in model_outputs:
							snake_case__	     ={
							    'generated_text': self.tokenizer.decode(
							        __UpperCamelCase					,   skip_special_tokens=__UpperCamelCase					,   )
							}
							records.append(__UpperCamelCase						)
					return records






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		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


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


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




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_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