



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   (		_a      ):
 lowerCAmelCase__				    =     ['''image_processor''', '''tokenizer''']
 lowerCAmelCase__				    =     '''BlipImageProcessor'''
 lowerCAmelCase__				    =     '''AutoTokenizer'''




 def __init__(  self		, __UpperCAmelCase		, __UpperCAmelCase		, __UpperCAmelCase ):



    '''simple docstring'''
    super().__init__(__UpperCAmelCase		, __UpperCAmelCase )

    # add QFormer tokenizer
    __lowerCamelCase  							=			qformer_tokenizer




 def __call__(  self		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = True		, __UpperCAmelCase = False		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = 0		, __UpperCAmelCase = None		, __UpperCAmelCase = None		, __UpperCAmelCase = False		, __UpperCAmelCase = False		, __UpperCAmelCase = False		, __UpperCAmelCase = False		, __UpperCAmelCase = False		, __UpperCAmelCase = True		, __UpperCAmelCase = None		, **__UpperCAmelCase		, ):



    '''simple docstring'''



    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=__UpperCAmelCase		, add_special_tokens=__UpperCAmelCase		, padding=__UpperCAmelCase		, truncation=__UpperCAmelCase		, max_length=__UpperCAmelCase		, stride=__UpperCAmelCase		, pad_to_multiple_of=__UpperCAmelCase		, return_attention_mask=__UpperCAmelCase		, return_overflowing_tokens=__UpperCAmelCase		, return_special_tokens_mask=__UpperCAmelCase		, return_offsets_mapping=__UpperCAmelCase		, return_token_type_ids=__UpperCAmelCase		, return_length=__UpperCAmelCase		, verbose=__UpperCAmelCase		, return_tensors=__UpperCAmelCase		, **__UpperCAmelCase		, )
       encoding.update(__UpperCAmelCase )
       __lowerCamelCase  							=			self.qformer_tokenizer(
           text=__UpperCAmelCase		, add_special_tokens=__UpperCAmelCase		, padding=__UpperCAmelCase		, truncation=__UpperCAmelCase		, max_length=__UpperCAmelCase		, stride=__UpperCAmelCase		, pad_to_multiple_of=__UpperCAmelCase		, return_attention_mask=__UpperCAmelCase		, return_overflowing_tokens=__UpperCAmelCase		, return_special_tokens_mask=__UpperCAmelCase		, return_offsets_mapping=__UpperCAmelCase		, return_token_type_ids=__UpperCAmelCase		, return_length=__UpperCAmelCase		, verbose=__UpperCAmelCase		, return_tensors=__UpperCAmelCase		, **__UpperCAmelCase		, )
       __lowerCamelCase  							=			qformer_text_encoding.pop('''input_ids''' )
       __lowerCamelCase  							=			qformer_text_encoding.pop('''attention_mask''' )

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

    return encoding




 def 				lowerCamelCase					(  self		, *__UpperCAmelCase		, **__UpperCAmelCase ):



    '''simple docstring'''
    return self.tokenizer.batch_decode(*__UpperCAmelCase		, **__UpperCAmelCase )




 def 				lowerCamelCase					(  self		, *__UpperCAmelCase		, **__UpperCAmelCase ):



    '''simple docstring'''
    return self.tokenizer.decode(*__UpperCAmelCase		, **__UpperCAmelCase )




 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 				lowerCamelCase					(  self ):



    '''simple docstring'''
    __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 				lowerCamelCase					(  self		, __UpperCAmelCase		, **__UpperCAmelCase ):



    '''simple docstring'''



    if os.path.isfile(__UpperCAmelCase ):
       raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
    os.makedirs(__UpperCAmelCase		, exist_ok=__UpperCAmelCase )
    __lowerCamelCase  							=			os.path.join(__UpperCAmelCase		, '''qformer_tokenizer''' )
    self.qformer_tokenizer.save_pretrained(__UpperCAmelCase )
    return super().save_pretrained(__UpperCAmelCase		, **__UpperCAmelCase )




 @classmethod
 def 				lowerCamelCase					(  cls		, __UpperCAmelCase		, **__UpperCAmelCase ):



    '''simple docstring'''
    __lowerCamelCase  							=			AutoTokenizer.from_pretrained(__UpperCAmelCase		, subfolder='''qformer_tokenizer''' )
    __lowerCamelCase  							=			cls._get_arguments_from_pretrained(__UpperCAmelCase		, **__UpperCAmelCase )
    args.append(__UpperCAmelCase )
    return cls(*__UpperCAmelCase )













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     ):
			lowerCamelCase_     :     Any       			=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Any				=  hf_hub_download(
							    repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    List[Any]				=  VideoClassificationPipeline(model=__magic_name__					,							image_processor=__magic_name__					,							top_k=2    )
							snake_case_						:    str				=  [
							    example_video_filepath,
							    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
							]
							return video_classifier, examples






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							for example in examples:
											snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__    )

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






			@require_torch
			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Any				=  '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
							snake_case_						:    str				=  VideoMAEFeatureExtractor(
							    size={'''shortest_edge''': 10}					,							crop_size={'''height''': 10, '''width''': 10}    )
							snake_case_						:    int				=  pipeline(
							    '''video-classification'''					,							model=__magic_name__					,							feature_extractor=__magic_name__					,							frame_sampling_rate=4    )

							snake_case_						:    List[str]				=  hf_hub_download(repo_id='''nateraw/video-demo'''					,							filename='''archery.mp4'''					,							repo_type='''dataset'''    )
							snake_case_						:    Union[str, Any]				=  video_classifier(__magic_name__					,							top_k=2    )
							self.assertEqual(
							    nested_simplify(__magic_name__					,							decimals=4    )					,							[{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}]					,							)

							snake_case_						:    int				=  video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]					,							top_k=2					,							)
							self.assertEqual(
							    nested_simplify(__magic_name__					,							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 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							pass
