
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				["image_processor", "tokenizer"]
    snake_case__ =				"BlipImageProcessor"
    snake_case__ =				"AutoTokenizer"


    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Any:
         super().__init__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         # add QFormer tokenizer
         lowerCAmelCase__          =			qformer_tokenizer


    def __call__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					ImageInput = None       ,     SCREAMING_SNAKE_CASE__					:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str, PaddingStrategy] = False       ,     SCREAMING_SNAKE_CASE__					:					Union[bool, str, TruncationStrategy] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     SCREAMING_SNAKE_CASE__					:					int = 0       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     )				->	BatchFeature:
         if images is None and text is None:
              raise ValueError("You have to specify at least images or text."       )

         lowerCAmelCase__          =			BatchFeature()

         if text is not None:
              lowerCAmelCase__          =			self.tokenizer(
                  text=SCREAMING_SNAKE_CASE__       ,     add_special_tokens=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ,     pad_to_multiple_of=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     return_overflowing_tokens=SCREAMING_SNAKE_CASE__       ,     return_special_tokens_mask=SCREAMING_SNAKE_CASE__       ,     return_offsets_mapping=SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       ,     return_length=SCREAMING_SNAKE_CASE__       ,     verbose=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
              encoding.update(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			self.qformer_tokenizer(
                  text=SCREAMING_SNAKE_CASE__       ,     add_special_tokens=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     truncation=SCREAMING_SNAKE_CASE__       ,     max_length=SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ,     pad_to_multiple_of=SCREAMING_SNAKE_CASE__       ,     return_attention_mask=SCREAMING_SNAKE_CASE__       ,     return_overflowing_tokens=SCREAMING_SNAKE_CASE__       ,     return_special_tokens_mask=SCREAMING_SNAKE_CASE__       ,     return_offsets_mapping=SCREAMING_SNAKE_CASE__       ,     return_token_type_ids=SCREAMING_SNAKE_CASE__       ,     return_length=SCREAMING_SNAKE_CASE__       ,     verbose=SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			qformer_text_encoding.pop("input_ids"       )
              lowerCAmelCase__          =			qformer_text_encoding.pop("attention_mask"       )

         if images is not None:
              lowerCAmelCase__          =			self.image_processor(SCREAMING_SNAKE_CASE__       ,     return_tensors=SCREAMING_SNAKE_CASE__       )
              encoding.update(SCREAMING_SNAKE_CASE__       )

         return encoding


    def        a				(	self					:					Any       ,     *SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Any:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Dict       )				->	Any:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def        a				(	self					:					Any       )				->	List[str]:
         lowerCAmelCase__          =			self.tokenizer.model_input_names
         lowerCAmelCase__          =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         if os.path.isfile(SCREAMING_SNAKE_CASE__       ):
              raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'       )
         os.makedirs(SCREAMING_SNAKE_CASE__       ,     exist_ok=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			os.path.join(SCREAMING_SNAKE_CASE__       ,     "qformer_tokenizer"       )
         self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__       )
         return super().save_pretrained(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @classmethod
    def        a				(	cls					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       ,     subfolder="qformer_tokenizer"       )
         lowerCAmelCase__          =			cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
         args.append(SCREAMING_SNAKE_CASE__       )
         return cls(*SCREAMING_SNAKE_CASE__       )






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 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


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


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Tuple:
         for example in examples:
              lowerCAmelCase__          =			video_classifier(SCREAMING_SNAKE_CASE__       )

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


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

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

         lowerCAmelCase__          =			video_classifier(
             [
                 video_file_path,
                 video_file_path,
             ]       ,     top_k=2       ,     )
         self.assertEqual(
             nested_simplify(SCREAMING_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        a				(	self					:					Optional[Any]       )				->	Optional[int]:
         pass
