
from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
	import numpy as np
	from decord import VideoReader


if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

UpperCAmelCase	      = logging.get_logger(__name__)







@add_end_docstrings(lowerCAmelCase_   )
class        A_   (			lowerCAmelCase_   ):




	'''simple docstring'''


	def __init__(       self		,    *snake_case		,    **snake_case       ):
		super().__init__(*snake_case_		,    **snake_case_       )
		requires_backends(self		,    'decord'       )
		self.check_model_type(snake_case_       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=None		,    snake_case=None		,    snake_case=None       ):
		lowercase       =    {}
		if frame_sampling_rate is not None:
			lowercase       =    frame_sampling_rate
		if num_frames is not None:
			lowercase       =    num_frames

		lowercase       =    {}
		if top_k is not None:
			lowercase       =    top_k
		return preprocess_params, {}, postprocess_params





	def __call__(       self		,    snake_case		,    **snake_case       ):
		return super().__call__(snake_case_		,    **snake_case_       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=None		,    snake_case=1       ):
		if num_frames is None:
			lowercase       =    self.model.config.num_frames

		if video.startswith('http://'       ) or video.startswith('https://'       ):
			lowercase       =    BytesIO(requests.get(snake_case_       ).content       )

		lowercase       =    VideoReader(snake_case_       )
		videoreader.seek(0       )

		lowercase       =    0
		lowercase       =    num_frames * frame_sampling_rate - 1
		lowercase       =    np.linspace(snake_case_		,    snake_case_		,    num=snake_case_		,    dtype=np.intaa       )

		lowercase       =    videoreader.get_batch(snake_case_       ).asnumpy()
		lowercase       =    list(snake_case_       )

		lowercase       =    self.image_processor(snake_case_		,    return_tensors=self.framework       )
		return model_inputs





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    self.model(**snake_case_       )
		return model_outputs





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=5       ):
		if top_k > self.model.config.num_labels:
			lowercase       =    self.model.config.num_labels

		if self.framework == "pt":
			lowercase       =    model_outputs.logits.softmax(-1       )[0]
			lowercase  ,					lowercase       =    probs.topk(snake_case_       )
		else:
			raise ValueError(F'''Unsupported framework: {self.framework}'''       )

		lowercase       =    scores.tolist()
		lowercase       =    ids.tolist()
		return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_		,    snake_case_       )]








'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




