"""simple docstring"""




import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class       a_      (     unittest.TestCase			):
					UpperCamelCase_ :	List[Any]			=    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   hf_hub_download(
							    repo_id="""nateraw/video-demo"""  ,						filename="""archery.mp4"""  ,						repo_type="""dataset"""     )
							lowerCAmelCase__        =   VideoClassificationPipeline(model=snake_case__  ,						image_processor=snake_case__  ,						top_k=2     )
							lowerCAmelCase__        =   [
							    example_video_filepath,
							    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
							]
							return video_classifier, examples




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     str     ):
							for example in examples:
									lowerCAmelCase__        =   video_classifier(snake_case__     )

									self.assertEqual(
									    snake_case__  ,						[
									        {"""score""": ANY(snake_case__     ), """label""": ANY(snake_case__     )},
									        {"""score""": ANY(snake_case__     ), """label""": ANY(snake_case__     )},
									    ]  ,						)




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
							lowerCAmelCase__        =   VideoMAEFeatureExtractor(
							    size={"""shortest_edge""": 10}  ,						crop_size={"""height""": 10, """width""": 10}     )
							lowerCAmelCase__        =   pipeline(
							    """video-classification"""  ,						model=snake_case__  ,						feature_extractor=snake_case__  ,						frame_sampling_rate=4     )

							lowerCAmelCase__        =   hf_hub_download(repo_id="""nateraw/video-demo"""  ,						filename="""archery.mp4"""  ,						repo_type="""dataset"""     )
							lowerCAmelCase__        =   video_classifier(snake_case__  ,						top_k=2     )
							self.assertEqual(
							    nested_simplify(snake_case__  ,						decimals=4     )  ,						[{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}]  ,						)

							lowerCAmelCase__        =   video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]  ,						top_k=2  ,						)
							self.assertEqual(
							    nested_simplify(snake_case__  ,						decimals=4     )  ,						[
							        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
							        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
							    ]  ,						)




					@require_tf
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							pass





"""simple docstring"""




import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class       a_      :




					def __init__(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   256
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   cva.imread(snake_case__  ,						0     )
							lowerCAmelCase__        =   copy.deepcopy(self.img     )
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   plt.hist(self.img.ravel()  ,						256  ,						[0, 256]  ,						label="""x"""     )
							lowerCAmelCase__        =   np.sum(snake_case__     )
							for i in range(len(snake_case__     )     ):
									lowerCAmelCase__        =   x[i] / self.k
									self.sk += prk
									lowerCAmelCase__        =   (self.L - 1) * self.sk
									if self.rem != 0:
											lowerCAmelCase__        =   int(last % last     )
									lowerCAmelCase__        =   int(last + 1 if self.rem >= 0.5 else last     )
									self.last_list.append(snake_case__     )
									lowerCAmelCase__        =   int(np.ma.count(self.img     ) / self.img[1].size     )
									lowerCAmelCase__        =   self.img[1].size
							for i in range(self.number_of_cols     ):
									for j in range(self.number_of_rows     ):
											lowerCAmelCase__        =   self.img[j][i]
											if num != self.last_list[num]:
													lowerCAmelCase__        =   self.last_list[num]
							cva.imwrite("""output_data/output.jpg"""  ,						self.img     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							plt.hist(self.img.ravel()  ,						256  ,						[0, 256]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							cva.imshow("""Output-Image"""  ,						self.img     )
							cva.imshow("""Input-Image"""  ,						self.original_image     )
							cva.waitKey(5000     )
							cva.destroyAllWindows()


if __name__ == "__main__":
			__lowerCAmelCase    :							Dict													=						os.path.join(os.path.basename(__file__), "image_data/input.jpg")
			__lowerCAmelCase    :							Optional[int]													=						ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()




