
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 __snake_case		(      unittest.TestCase     ):
    _a		:						Optional[int]=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







           for example in examples:
                  lowercase							:     int       	=      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      ):



           '''simple docstring'''







           lowercase							:     Any       	=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
           lowercase							:     str       	=      VideoMAEFeatureExtractor(
               size={"""shortest_edge""": 10}		,crop_size={"""height""": 10, """width""": 10}      )
           lowercase							:     List[Any]       	=      pipeline(
               """video-classification"""		,model=snake_case		,feature_extractor=snake_case		,frame_sampling_rate=4      )

           lowercase							:     Dict       	=      hf_hub_download(repo_id="""nateraw/video-demo"""		,filename="""archery.mp4"""		,repo_type="""dataset"""      )
           lowercase							:     Any       	=      video_classifier(snake_case		,top_k=2      )
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}]		,)

           lowercase							:     str       	=      video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,top_k=2		,)
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[
                   [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
                   [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
               ]		,)
    @require_tf
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Any:
       assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     Optional[Any]       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase							:     Any       	=      SqlDatasetReader(
                  """dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""					,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     int       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       lowercase							:     str       	=      features.copy() if features else default_expected_features
       lowercase							:     Optional[Any]       	=      (
           Features({feature: Value(SCREAMING_SNAKE_CASE__				) for feature, dtype in features.items()}				) if features is not None else None
       )
       lowercase							:     Optional[int]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__				)				) as con:
              lowercase							:     Optional[int]       	=      con.cursor()
              cur.execute("""SELECT * FROM dataset"""				)
              for row in cur:
                     yield row







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Any       	=      tmp_path / """cache"""
       lowercase							:     int       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=1				).write()

       lowercase							:     List[str]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Dict       	=      tmp_path / """cache"""
       lowercase							:     List[str]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=2				).write()

       lowercase							:     Optional[int]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     str       	=      tmp_path / """cache"""
       lowercase							:     Optional[int]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     Optional[Any]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       with pytest.raises(SCREAMING_SNAKE_CASE__				):
              SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=0				).write()
