


"""simple docstring"""


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
	import torch



class 			lowerCamelCase__						(   __UpperCAmelCase			):
						lowerCAmelCase       =     '''char'''
						lowerCAmelCase       =     '''bpe'''
						lowerCAmelCase       =     '''wp'''

UpperCamelCase						:				Any             =      (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class 			lowerCamelCase__						(   __UpperCAmelCase			):
						lowerCAmelCase       =     ['''image_processor''', '''char_tokenizer''']
						lowerCAmelCase       =     '''ViTImageProcessor'''
						lowerCAmelCase       =     '''MgpstrTokenizer'''







						def __init__(							self   :    List[Any]			,			_lowercase   :    Any=None			,			_lowercase   :    Optional[Any]=None			,			**_lowercase   :    str		):
								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.'			,			__SCREAMING_SNAKE_CASE			,			)
										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`.'		)

								A					       = tokenizer
								A					       = AutoTokenizer.from_pretrained('gpt2'		)
								A					       = AutoTokenizer.from_pretrained('bert-base-uncased'		)

								super().__init__(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE		)







						def __call__(							self   :    Tuple			,			_lowercase   :    Dict=None			,			_lowercase   :    List[str]=None			,			_lowercase   :    int=None			,			**_lowercase   :    Optional[int]		):

								if images is None and text is None:
										raise ValueError('You need to specify either an `images` or `text` input to process.'		)

								if images is not None:
										A					       = self.image_processor(__SCREAMING_SNAKE_CASE			,			return_tensors=__SCREAMING_SNAKE_CASE			,			**__SCREAMING_SNAKE_CASE		)
								if text is not None:
										A					       = self.char_tokenizer(__SCREAMING_SNAKE_CASE			,			return_tensors=__SCREAMING_SNAKE_CASE			,			**__SCREAMING_SNAKE_CASE		)

								if text is None:
										return inputs
								elif images is None:
										return encodings
								else:
										A					       = encodings['input_ids']
										return inputs







						def __a						(							self   :    List[Any]			,			_lowercase   :    Optional[int]		):
								A       , A       , A					       = sequences
								A					       = char_preds.size(0		)

								A       , A					       = self._decode_helper(__SCREAMING_SNAKE_CASE			,			'char'		)
								A       , A					       = self._decode_helper(__SCREAMING_SNAKE_CASE			,			'bpe'		)
								A       , A					       = self._decode_helper(__SCREAMING_SNAKE_CASE			,			'wp'		)

								A					       = []
								A					       = []
								for i in range(__SCREAMING_SNAKE_CASE		):
										A					       = [char_scores[i], bpe_scores[i], wp_scores[i]]
										A					       = [char_strs[i], bpe_strs[i], wp_strs[i]]
										A					       = scores.index(max(__SCREAMING_SNAKE_CASE		)		)
										final_strs.append(strs[max_score_index]		)
										final_scores.append(scores[max_score_index]		)

								A					       = {}
								A					       = final_strs
								A					       = final_scores
								A					       = char_strs
								A					       = bpe_strs
								A					       = wp_strs
								return out







						def __a						(							self   :    List[str]			,			_lowercase   :    Optional[int]			,			_lowercase   :    List[Any]		):

								if format == DecodeType.CHARACTER:
										A					       = self.char_decode
										A					       = 1
										A					       = '[s]'
								elif format == DecodeType.BPE:
										A					       = self.bpe_decode
										A					       = 2
										A					       = '#'
								elif format == DecodeType.WORDPIECE:
										A					       = self.wp_decode
										A					       = 102
										A					       = '[SEP]'
								else:
										raise ValueError(f'Format {format} is not supported.'		)

								A       , A					       = [], []
								A					       = pred_logits.size(0		)
								A					       = pred_logits.size(1		)
								A       , A					       = pred_logits.topk(1			,			dim=-1			,			largest=__SCREAMING_SNAKE_CASE			,			sorted=__SCREAMING_SNAKE_CASE		)
								A					       = preds_index.view(-1			,			__SCREAMING_SNAKE_CASE		)[:, 1:]
								A					       = decoder(__SCREAMING_SNAKE_CASE		)
								A       , A					       = torch.nn.functional.softmax(__SCREAMING_SNAKE_CASE			,			dim=2		).max(dim=2		)
								A					       = preds_max_prob[:, 1:]

								for index in range(__SCREAMING_SNAKE_CASE		):
										A					       = preds_str[index].find(__SCREAMING_SNAKE_CASE		)
										A					       = preds_str[index][:pred_eos]
										A					       = preds_index[index].cpu().tolist()
										A					       = pred_index.index(__SCREAMING_SNAKE_CASE		) if eos_token in pred_index else -1
										A					       = preds_max_prob[index][: pred_eos_index + 1]
										A					       = pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
										dec_strs.append(__SCREAMING_SNAKE_CASE		)
										conf_scores.append(__SCREAMING_SNAKE_CASE		)

								return dec_strs, conf_scores







						def __a						(							self   :    Tuple			,			_lowercase   :    List[str]		):
								A					       = [seq.replace(' '			,			''		) for seq in self.char_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE		)]
								return decode_strs







						def __a						(							self   :    Dict			,			_lowercase   :    Tuple		):
								return self.bpe_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE		)







						def __a						(							self   :    List[Any]			,			_lowercase   :    Tuple		):
								A					       = [seq.replace(' '			,			''		) for seq in self.wp_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE		)]
								return decode_strs









"""simple docstring"""


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       =     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







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







						def __a						(							self   :    Tuple			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[Any]		):
								for example in examples:
										A					       = video_classifier(_lowercase		)

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







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

								A					       = hf_hub_download(repo_id='nateraw/video-demo'			,			filename='archery.mp4'			,			repo_type='dataset'		)
								A					       = video_classifier(_lowercase			,			top_k=2		)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}]			,			)

								A					       = video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]			,			top_k=2			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
								        [{'score': 0.5_1_9_9, 'label': 'LABEL_0'}, {'score': 0.4_8_0_1, 'label': 'LABEL_1'}],
								    ]			,			)







						@require_tf
						def __a						(							self   :    Dict		):
								pass





