



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[str]     =			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING





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





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):


						for example in examples:
									lowerCamelCase__ :      Any								=		video_classifier(_UpperCamelCase				)

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





			@require_torch
			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
						lowerCamelCase__ :      Any								=		VideoMAEFeatureExtractor(
						    size={"""shortest_edge""": 10}							,    crop_size={"""height""": 10, """width""": 10}				)
						lowerCamelCase__ :      str								=		pipeline(
						    """video-classification"""							,    model=_UpperCamelCase							,    feature_extractor=_UpperCamelCase							,    frame_sampling_rate=4				)

						lowerCamelCase__ :      Dict								=		hf_hub_download(repo_id="""nateraw/video-demo"""							,    filename="""archery.mp4"""							,    repo_type="""dataset"""				)
						lowerCamelCase__ :      str								=		video_classifier(_UpperCamelCase							,    top_k=2				)
						self.assertEqual(
						    nested_simplify(_UpperCamelCase							,    decimals=4				)							,    [{"""score""": 0.51_99, """label""": """LABEL_0"""}, {"""score""": 0.48_01, """label""": """LABEL_1"""}]							,    )

						lowerCamelCase__ :      List[Any]								=		video_classifier(
						    [
						        video_file_path,
						        video_file_path,
						    ]							,    top_k=2							,    )
						self.assertEqual(
						    nested_simplify(_UpperCamelCase							,    decimals=4				)							,    [
						        [{"""score""": 0.51_99, """label""": """LABEL_0"""}, {"""score""": 0.48_01, """label""": """LABEL_1"""}],
						        [{"""score""": 0.51_99, """label""": """LABEL_0"""}, {"""score""": 0.48_01, """label""": """LABEL_1"""}],
						    ]							,    )





			@require_tf
			def        _snake_case    (self				):
						pass








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



