




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 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

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

     def 					a__	(  self		,     _a		,     _a				)						->							Any:
           for example in examples:
                 _A							:     Optional[int]			=						video_classifier(_a				)

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

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

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

           _A							:     Optional[int]			=						video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,     top_k=2		,     )
           self.assertEqual(
               nested_simplify(_a		,     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 					a__	(  self				)						->							str:
           pass







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

