






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__UpperCAmelCase     							=						logging.get_logger(__name__)


__UpperCAmelCase     							=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__UpperCAmelCase     							=						{
    'allenai/led-base-16384': 16384,
}



class   lowerCamelCase				(A__      ):







		'''simple docstring'''



		_snake_case   :   Any							=  VOCAB_FILES_NAMES
		_snake_case   :   Optional[Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Tuple							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Tuple							=  LEDTokenizer
		_snake_case   :   Union[str, Any]							=  ["input_ids", "attention_mask"]



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="replace"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<mask>"    ,		_UpperCamelCase=False    ,		_UpperCamelCase=True    ,		**_UpperCamelCase    ,		)     ->							int:
							super().__init__(
							    __A    ,		__A    ,		tokenizer_file=__A    ,		errors=__A    ,		bos_token=__A    ,		eos_token=__A    ,		sep_token=__A    ,		cls_token=__A    ,		unk_token=__A    ,		pad_token=__A    ,		mask_token=__A    ,		add_prefix_space=__A    ,		trim_offsets=__A    ,		**__A    ,		)

							UpperCAmelCase_   :				str						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
							if pre_tok_state.get('add_prefix_space'    ,		__A       ) != add_prefix_space:
												UpperCAmelCase_   :				Union[str, Any]						=						getattr(__A    ,		pre_tok_state.pop('type'       )       )
												UpperCAmelCase_   :				Any						=						add_prefix_space
												UpperCAmelCase_   :				Dict						=						pre_tok_class(**__A       )

							UpperCAmelCase_   :				Optional[Any]						=						add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCAmelCase_   :				int						=						"""post_processor"""
							UpperCAmelCase_   :				Dict						=						getattr(self.backend_tokenizer    ,		__A    ,		__A       )
							if tokenizer_component_instance:
												UpperCAmelCase_   :				Tuple						=						json.loads(tokenizer_component_instance.__getstate__()       )

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
																	UpperCAmelCase_   :				Tuple						=						tuple(state['sep']       )
												if "cls" in state:
																	UpperCAmelCase_   :				Dict						=						tuple(state['cls']       )

												UpperCAmelCase_   :				Dict						=						False

												if state.get('add_prefix_space'    ,		__A       ) != add_prefix_space:
																	UpperCAmelCase_   :				str						=						add_prefix_space
																	UpperCAmelCase_   :				str						=						True

												if state.get('trim_offsets'    ,		__A       ) != trim_offsets:
																	UpperCAmelCase_   :				Tuple						=						trim_offsets
																	UpperCAmelCase_   :				Tuple						=						True

												if changes_to_apply:
																	UpperCAmelCase_   :				Tuple						=						getattr(__A    ,		state.pop('type'       )       )
																	UpperCAmelCase_   :				List[str]						=						component_class(**__A       )
																	setattr(self.backend_tokenizer    ,		__A    ,		__A       )



		@property
		# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
		def  __UpperCAmelCase      (							self       )     ->							str:
							if self._mask_token is None:
												if self.verbose:
																	logger.error('Using mask_token, but it is not set yet.'       )
												return None
							return str(self._mask_token       )



		@mask_token.setter
		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				Dict						=						AddedToken(__A    ,		lstrip=__A    ,		rstrip=__A       ) if isinstance(__A    ,		__A       ) else value
							UpperCAmelCase_   :				int						=						value



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							BatchEncoding:
							UpperCAmelCase_   :				Optional[Any]						=						kwargs.get('is_split_into_words'    ,		__A       )

							if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
												    'to use it with pretokenized inputs.'       )

							return super()._batch_encode_plus(*__A    ,		**__A       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							BatchEncoding:
							UpperCAmelCase_   :				str						=						kwargs.get('is_split_into_words'    ,		__A       )

							if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
												    'to use it with pretokenized inputs.'       )

							return super()._encode_plus(*__A    ,		**__A       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							UpperCAmelCase_   :				Tuple						=						self._tokenizer.model.save(__A    ,		name=__A       )
							return tuple(__A       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Tuple:
							UpperCAmelCase_   :				List[Any]						=						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Any						=						[self.sep_token_id]
							UpperCAmelCase_   :				Optional[Any]						=						[self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep       ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = PaddingStrategy.DO_NOT_PAD    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		)     ->							dict:
							UpperCAmelCase_   :				Tuple						=						super()._pad(
							    encoded_inputs=__A    ,		max_length=__A    ,		padding_strategy=__A    ,		pad_to_multiple_of=__A    ,		return_attention_mask=__A    ,		)

							# Load from model defaults
							if return_attention_mask is None:
												UpperCAmelCase_   :				List[str]						=						"""attention_mask""" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
												UpperCAmelCase_   :				List[str]						=						encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												UpperCAmelCase_   :				Dict						=						len(encoded_inputs['global_attention_mask']       ) != len(__A       )

												if needs_to_be_padded:
																	UpperCAmelCase_   :				Any						=						len(__A       ) - len(encoded_inputs['global_attention_mask']       )

																	if self.padding_side == "right":
																						# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																						UpperCAmelCase_   :				Tuple						=						(
																						    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																						)
																	elif self.padding_side == "left":
																						UpperCAmelCase_   :				Optional[int]						=						[-1] * difference + encoded_inputs[
																						    """global_attention_mask"""
																						]
																	else:
																						raise ValueError('Invalid padding strategy:' + str(self.padding_side       )       )

							return encoded_inputs













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   :   List[str]							=  MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Dict:
							for example in examples:
												UpperCAmelCase_   :				str						=						video_classifier(_UpperCamelCase       )

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



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
							UpperCAmelCase_   :				Optional[Any]						=						VideoMAEFeatureExtractor(
							    size={'shortest_edge': 1_0}    ,		crop_size={'height': 1_0, 'width': 1_0}       )
							UpperCAmelCase_   :				str						=						pipeline(
							    'video-classification'    ,		model=_UpperCamelCase    ,		feature_extractor=_UpperCamelCase    ,		frame_sampling_rate=4       )

							UpperCAmelCase_   :				Any						=						hf_hub_download(repo_id='nateraw/video-demo'    ,		filename='archery.mp4'    ,		repo_type='dataset'       )
							UpperCAmelCase_   :				List[str]						=						video_classifier(_UpperCamelCase    ,		top_k=2       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase    ,		decimals=4       )    ,		[{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}]    ,		)

							UpperCAmelCase_   :				Tuple						=						video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]    ,		top_k=2    ,		)
							self.assertEqual(
							    nested_simplify(_UpperCamelCase    ,		decimals=4       )    ,		[
							        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
							        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
							    ]    ,		)



		@require_tf
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass






