
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_UpperCAmelCase					: Union[str, Any]      =       datasets.utils.logging.get_logger(__name__)


@dataclass
class 				lowercase					(		datasets.BuilderConfig   ):
		__lowercase       :       int	     =				10_000
		__lowercase       :       Optional[List[str]]	     =				None
		__lowercase       :       Optional[datasets.Features]	     =				None






class 				lowercase					(		datasets.ArrowBasedBuilder   ):
		__lowercase       :       List[Any]	     =				ParquetConfig



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				return datasets.DatasetInfo(features=self.config.features       )



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				if not self.config.data_files:
						raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''       )
				UpperCamelCase		=     dl_manager.download_and_extract(self.config.data_files       )
				if isinstance(A_				,      (str, list, tuple)       ):
						UpperCamelCase		=     data_files
						if isinstance(A_				,      A_       ):
								UpperCamelCase		=     [files]
						# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
						UpperCamelCase		=     [dl_manager.iter_files(A_       ) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,      gen_kwargs={'files': files}       )]
				UpperCamelCase		=     []
				for split_name, files in data_files.items():
						if isinstance(A_				,      A_       ):
								UpperCamelCase		=     [files]
						# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
						UpperCamelCase		=     [dl_manager.iter_files(A_       ) for file in files]
						# Infer features is they are stoed in the arrow schema
						if self.info.features is None:
								for file in itertools.chain.from_iterable(A_       ):
										with open(A_				,      'rb'       ) as f:
												UpperCamelCase		=     datasets.Features.from_arrow_schema(pq.read_schema(A_       )       )
										break
						splits.append(datasets.SplitGenerator(name=A_				,      gen_kwargs={'files': files}       )       )
				return splits



		def __UpperCamelCase ( self				,      A_       )  ->					pa.Table:
				"""simple docstring"""
				if self.info.features is not None:
						# more expensive cast to support nested features with keys in a different order
						# allows str <-> int/float or str to Audio for example
						UpperCamelCase		=     table_cast(A_				,      self.info.features.arrow_schema       )
				return pa_table



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
						if sorted(field.name for field in schema       ) != sorted(self.config.columns       ):
								raise ValueError(
								    F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''       )
				for file_idx, file in enumerate(itertools.chain.from_iterable(A_       )       ):
						with open(A_				,      'rb'       ) as f:
								UpperCamelCase		=     pq.ParquetFile(A_       )
								try:
										for batch_idx, record_batch in enumerate(
										    parquet_file.iter_batches(batch_size=self.config.batch_size				,      columns=self.config.columns       )       ):
												UpperCamelCase		=     pa.Table.from_batches([record_batch]       )
												# Uncomment for debugging (will print the Arrow table size and elements)
												# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
												# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
												yield F'''{file_idx}_{batch_idx}''', self._cast_table(A_       )
								except ValueError as e:
										logger.error(F'''Failed to read file \'{file}\' with error {type(A_       )}: {e}'''       )
										raise


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   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

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



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

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

				UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
