

from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
					import numpy as np
					from decord import VideoReader


if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

_UpperCAmelCase		:   str										=       logging.get_logger(__name__)







@add_end_docstrings(lowercase_			)
class  lowercase				(       lowercase_			):




				def __init__(					self      ,   *snake_case      ,   **snake_case       ):
											super().__init__(*snake_case      ,   **snake_case       )
											requires_backends(self      ,   'decord'       )
											self.check_model_type(snake_case       )




				def     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None       ):
											snake_case_							=    {}
											if frame_sampling_rate is not None:
																		snake_case_							=    frame_sampling_rate
											if num_frames is not None:
																		snake_case_							=    num_frames

											snake_case_							=    {}
											if top_k is not None:
																		snake_case_							=    top_k
											return preprocess_params, {}, postprocess_params




				def __call__(					self      ,   snake_case      ,   **snake_case       ):
											return super().__call__(snake_case      ,   **snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case=None      ,   snake_case=1       ):
											if num_frames is None:
																		snake_case_							=    self.model.config.num_frames

											if video.startswith('http://'       ) or video.startswith('https://'       ):
																		snake_case_							=    BytesIO(requests.get(snake_case       ).content       )

											snake_case_							=    VideoReader(snake_case       )
											videoreader.seek(0       )

											snake_case_							=    0
											snake_case_							=    num_frames * frame_sampling_rate - 1
											snake_case_							=    np.linspace(snake_case      ,   snake_case      ,   num=snake_case      ,   dtype=np.intaa       )

											snake_case_							=    videoreader.get_batch(snake_case       ).asnumpy()
											snake_case_							=    list(snake_case       )

											snake_case_							=    self.image_processor(snake_case      ,   return_tensors=self.framework       )
											return model_inputs




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.model(**snake_case       )
											return model_outputs




				def     a							(					self      ,   snake_case      ,   snake_case=5       ):
											if top_k > self.model.config.num_labels:
																		snake_case_							=    self.model.config.num_labels

											if self.framework == "pt":
																		snake_case_							=    model_outputs.logits.softmax(-1       )[0]
																		snake_case_				,    snake_case_							=    probs.topk(snake_case       )
											else:
																		raise ValueError(F'''Unsupported framework: {self.framework}'''       )

											snake_case_							=    scores.tolist()
											snake_case_							=    ids.tolist()
											return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case      ,   snake_case       )]







from PIL import Image





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    (259 * (level + 255)) / (255 * (259 - level))

							def contrast(UpperCamelCase__      ) -> int:
														return int(128 + factor * (c - 128)      )

							return img.point(UpperCamelCase__      )


if __name__ == "__main__":
					# Load image
					with Image.open("""image_data/lena.jpg""") as img:
										# Change contrast to 170
										_UpperCAmelCase		:   Tuple										=       change_contrast(img, 170)
										cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")




