





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ['''image_processor''', '''tokenizer''']
 __SCREAMING_SNAKE_CASE							=      '''LayoutLMv3ImageProcessor'''
 __SCREAMING_SNAKE_CASE							=      ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')



 def __init__(				self,__lowerCamelCase=None,__lowerCamelCase=None,**__lowerCamelCase					):
        A__		   =		None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.''',__lowerCamelCase,)
               A__		   =		kwargs.pop('''feature_extractor'''					)

        A__		   =		image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''					)
        if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''					)

        super().__init__(__lowerCamelCase,__lowerCamelCase					)



 def __call__(				self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = 0,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = True,__lowerCamelCase = None,**__lowerCamelCase,):
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''					)

        if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''					)

        # first, apply the image processor
        A__		   =		self.image_processor(images=__lowerCamelCase,return_tensors=__lowerCamelCase					)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
               A__		   =		features['''words''']

        A__		   =		self.tokenizer(
            text=text if text is not None else features['''words'''],text_pair=text_pair if text_pair is not None else None,boxes=boxes if boxes is not None else features['''boxes'''],word_labels=__lowerCamelCase,add_special_tokens=__lowerCamelCase,padding=__lowerCamelCase,truncation=__lowerCamelCase,max_length=__lowerCamelCase,stride=__lowerCamelCase,pad_to_multiple_of=__lowerCamelCase,return_token_type_ids=__lowerCamelCase,return_attention_mask=__lowerCamelCase,return_overflowing_tokens=__lowerCamelCase,return_special_tokens_mask=__lowerCamelCase,return_offsets_mapping=__lowerCamelCase,return_length=__lowerCamelCase,verbose=__lowerCamelCase,return_tensors=__lowerCamelCase,**__lowerCamelCase,)

        # add pixel values
        A__		   =		features.pop('''pixel_values'''					)
        if return_overflowing_tokens is True:
               A__		   =		self.get_overflowing_images(__lowerCamelCase,encoded_inputs['''overflow_to_sample_mapping''']					)
        A__		   =		images

        return encoded_inputs



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        A__		   =		[]
        for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]					)

        if len(__lowerCamelCase					) != len(__lowerCamelCase					):
               raise ValueError(
                   '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                   f" {len(__lowerCamelCase					)} and {len(__lowerCamelCase					)}"					)

        return images_with_overflow



 def 							UpperCamelCase     (				self,*__lowerCamelCase,**__lowerCamelCase					):
        return self.tokenizer.batch_decode(*__lowerCamelCase,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,*__lowerCamelCase,**__lowerCamelCase					):
        return self.tokenizer.decode(*__lowerCamelCase,**__lowerCamelCase					)



 @property
 def 							UpperCamelCase     (				self					):
        return ["input_ids", "bbox", "attention_mask", "pixel_values"]



 @property
 def 							UpperCamelCase     (				self					):
        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',__lowerCamelCase,)
        return self.image_processor_class




 @property
 def 							UpperCamelCase     (				self					):
        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',__lowerCamelCase,)
        return self.image_processor












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        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2					)
        A__		   =		[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        for example in examples:
               A__		   =		video_classifier(__lowerCamelCase					)

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



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__		   =		VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10}					)
        A__		   =		pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4					)

        A__		   =		hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		video_classifier(__lowerCamelCase,top_k=2					)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__		   =		video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,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					):
        pass





