





import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def     a_       (					lowerCAmelCase_ :     str      ):
   __lowerCAmelCase				      =      tmp_path / 'file.csv'
   __lowerCAmelCase				      =      textwrap.dedent(
       '\\n        header1,header2\n        1,2\n        10,20\n        '      )
   with open(lowerCAmelCase_,      'w'      ) as f:
      f.write(lowerCAmelCase_      )
   return str(lowerCAmelCase_      )

@pytest.fixture
def     a_       (					lowerCAmelCase_ :     Dict      ):
   __lowerCAmelCase				      =      tmp_path / 'malformed_file.csv'
   __lowerCAmelCase				      =      textwrap.dedent(
       '\\n        header1,header2\n        1,2\n        10,20,\n        '      )
   with open(lowerCAmelCase_,      'w'      ) as f:
      f.write(lowerCAmelCase_      )
   return str(lowerCAmelCase_      )

@pytest.fixture
def     a_       (					lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     str      ):
   __lowerCAmelCase				      =      tmp_path / 'csv_with_image.csv'
   __lowerCAmelCase				      =      textwrap.dedent(
       F"""\
        image
        {image_file}
        """      )
   with open(lowerCAmelCase_,      'w'      ) as f:
      f.write(lowerCAmelCase_      )
   return str(lowerCAmelCase_      )

@pytest.fixture
def     a_       (					lowerCAmelCase_ :     Optional[int]      ):
   __lowerCAmelCase				      =      tmp_path / 'csv_with_label.csv'
   __lowerCAmelCase				      =      textwrap.dedent(
       '\\n        label\n        good\n        bad\n        good\n        '      )
   with open(lowerCAmelCase_,      'w'      ) as f:
      f.write(lowerCAmelCase_      )
   return str(lowerCAmelCase_      )

@pytest.fixture
def     a_       (					lowerCAmelCase_ :     str      ):
   __lowerCAmelCase				      =      tmp_path / 'csv_with_int_list.csv'
   __lowerCAmelCase				      =      textwrap.dedent(
       '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '      )
   with open(lowerCAmelCase_,      'w'      ) as f:
      f.write(lowerCAmelCase_      )
   return str(lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     List[Any],      lowerCAmelCase_ :     Any      ):
   __lowerCAmelCase				      =      Csv()
   __lowerCAmelCase				      =      csv._generate_tables([[csv_file, malformed_csv_file]]      )
   with pytest.raises(lowerCAmelCase_,      match='Error tokenizing data'      ):
      for _ in generator:
         pass
   assert any(
       record.levelname == 'ERROR'
       and 'Failed to read file' in record.message
       and os.path.basename(lowerCAmelCase_      ) in record.message
       for record in caplog.records      )

@require_pil
def     a_       (					lowerCAmelCase_ :     List[str]      ):
   with open(lowerCAmelCase_,      encoding='utf-8'      ) as f:
      __lowerCAmelCase				      =      f.read().splitlines()[1]
   __lowerCAmelCase				      =      Csv(encoding='utf-8',      features=Features({'image': Image()}      )      )
   __lowerCAmelCase				      =      csv._generate_tables([[csv_file_with_image]]      )
   __lowerCAmelCase				      =      pa.concat_tables([table for _, table in generator]      )
   assert pa_table.schema.field('image'      ).type == Image()()
   __lowerCAmelCase				      =      pa_table.to_pydict()['image']
   assert generated_content == [{"path": image_file, "bytes": None}]

def     a_       (					lowerCAmelCase_ :     Dict      ):
   with open(lowerCAmelCase_,      encoding='utf-8'      ) as f:
      __lowerCAmelCase				      =      f.read().splitlines()[1:]
   __lowerCAmelCase				      =      Csv(encoding='utf-8',      features=Features({'label': ClassLabel(names=['good', 'bad']      )}      )      )
   __lowerCAmelCase				      =      csv._generate_tables([[csv_file_with_label]]      )
   __lowerCAmelCase				      =      pa.concat_tables([table for _, table in generator]      )
   assert pa_table.schema.field('label'      ).type == ClassLabel(names=['good', 'bad']      )()
   __lowerCAmelCase				      =      pa_table.to_pydict()['label']
   assert generated_content == [ClassLabel(names=['good', 'bad']      ).straint(lowerCAmelCase_      ) for label in labels]




def     a_       (					lowerCAmelCase_ :     List[str]      ):
   __lowerCAmelCase				      =      Csv(encoding='utf-8',      sep=',',      converters={'int_list': lambda lowerCAmelCase_      : [int(lowerCAmelCase_      ) for i in x.split()]}      )
   __lowerCAmelCase				      =      csv._generate_tables([[csv_file_with_int_list]]      )
   __lowerCAmelCase				      =      pa.concat_tables([table for _, table in generator]      )
   assert pa.types.is_list(pa_table.schema.field('int_list'      ).type      )
   __lowerCAmelCase				      =      pa_table.to_pydict()['int_list']
   assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]








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 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							snake_case  :   List[str]      =							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      hf_hub_download(
														    repo_id="""nateraw/video-demo"""	,			filename="""archery.mp4"""	,			repo_type="""dataset"""	)
														UpperCamelCase__					  =      VideoClassificationPipeline(model=__lowerCAmelCase	,			image_processor=__lowerCAmelCase	,			top_k=2	)
														UpperCamelCase__					  =      [
														    example_video_filepath,
														    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
														]
														return video_classifier, examples






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														for example in examples:
																					UpperCamelCase__					  =      video_classifier(__lowerCAmelCase	)

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






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

														UpperCamelCase__					  =      hf_hub_download(repo_id="""nateraw/video-demo"""	,			filename="""archery.mp4"""	,			repo_type="""dataset"""	)
														UpperCamelCase__					  =      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"""}]	,			)

														UpperCamelCase__					  =      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 					_lowerCamelCase		(    self	):
														pass




