

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        UpperCAmelCase__		(       unittest.TestCase       ):






  """simple docstring"""


  a           =					MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






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






  def       lowercase_		(							self						:		Optional[Any]	,   __lowerCamelCase						:		List[str]	,   __lowerCamelCase						:		Any					)		->							str:
       for example in examples:
            SCREAMING_SNAKE_CASE__    =    video_classifier(__lowerCamelCase					)

            self.assertEqual(
                __lowerCamelCase	,   [
                    {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                    {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                ]	,   )






  @require_torch
  def       lowercase_		(							self						:		Optional[Any]					)		->							Any:
       SCREAMING_SNAKE_CASE__    =    '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
       SCREAMING_SNAKE_CASE__    =    VideoMAEFeatureExtractor(
           size={'''shortest_edge''': 10}	,   crop_size={'''height''': 10, '''width''': 10}					)
       SCREAMING_SNAKE_CASE__    =    pipeline(
           '''video-classification'''	,   model=__lowerCamelCase	,   feature_extractor=__lowerCamelCase	,   frame_sampling_rate=4					)

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

       SCREAMING_SNAKE_CASE__    =    video_classifier(
           [
               video_file_path,
               video_file_path,
           ]	,   top_k=2	,   )
       self.assertEqual(
           nested_simplify(__lowerCamelCase	,   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       lowercase_		(							self						:		Tuple					)		->							Any:
       pass









import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						get_gpu_count()

        if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
        else:
               args.insert(0,'''run_glue_deebert.py''' )
               with patch.object(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )





