


"""simple docstring"""


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase						:				Any             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      "▁"

UpperCamelCase						:				List[str]             =      {"vocab_file": "prophetnet.tokenizer"}

UpperCamelCase						:				int             =      {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

UpperCamelCase						:				Optional[int]             =      {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

UpperCamelCase						:				int             =      {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}







def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = collections.OrderedDict()
		with open(UpperCamelCase__ ,			'r' ,			encoding='utf-8'	) as reader:
				A					       = reader.readlines()
		for index, token in enumerate(UpperCamelCase__	):
				A					       = token.rstrip('\n'	)
				A					       = index
		return vocab



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     VOCAB_FILES_NAMES
						lowerCAmelCase       =     PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase       =     ["""input_ids""", """attention_mask"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    Dict			,			_lowercase   :    Union[str, Any]="[SEP]"			,			_lowercase   :    Dict="[SEP]"			,			_lowercase   :    int="[SEP]"			,			_lowercase   :    List[Any]="[UNK]"			,			_lowercase   :    List[Any]="[PAD]"			,			_lowercase   :    Optional[Any]="[CLS]"			,			_lowercase   :    Union[str, Any]="[MASK]"			,			_lowercase   :    Optional[Dict[str, Any]] = None			,			**_lowercase   :    List[Any]			,			):
								A					       = {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_lowercase			,			eos_token=_lowercase			,			sep_token=_lowercase			,			unk_token=_lowercase			,			pad_token=_lowercase			,			cls_token=_lowercase			,			mask_token=_lowercase			,			sp_model_kwargs=self.sp_model_kwargs			,			**_lowercase			,			)

								try:
										import sentencepiece as spm
								except ImportError:
										logger.warning(
										    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
										    ' pip install sentencepiece'		)
										raise

								A					       = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(_lowercase		)		)
								A					       = vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# put special tokens and [unused] tokens into the vocab
								A					       = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

								for i in range(10		):
										A					       = f'[unused{i}]'
										A					       = 5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								A					       = 12
								A					       = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
								for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(_lowercase		)







						def __getstate__(							self   :    Optional[int]		):
								A					       = self.__dict__.copy()
								A					       = None
								return state







						def __setstate__(							self   :    Optional[int]			,			_lowercase   :    Dict		):
								A					       = d
								try:
										import sentencepiece as spm
								except ImportError:
										logger.warning(
										    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
										    ' pip install sentencepiece'		)
										raise

								# for backward compatibility
								if not hasattr(self			,			'sp_model_kwargs'		):
										A					       = {}

								A					       = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







						def __a						(							self   :    str			,			_lowercase   :    List[int]			,			_lowercase   :    Optional[List[int]] = None			,			_lowercase   :    bool = False		):

								if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_lowercase			,			token_ids_a=_lowercase			,			already_has_special_tokens=_lowercase		)

								if token_ids_a is None:
										return ([0] * len(_lowercase		)) + [1]
								return ([0] * len(_lowercase		)) + [1] + ([0] * len(_lowercase		)) + [1]







						def __a						(							self   :    str			,			_lowercase   :    List[int]			,			_lowercase   :    Optional[List[int]] = None		):
								A					       = [self.sep_token_id]

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







						@property
						def __a						(							self   :    int		):
								return len(self.sp_model		) + self.fairseq_offset







						def __a						(							self   :    List[Any]		):
								A					       = {self.convert_ids_to_tokens(_lowercase		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







						def __a						(							self   :    List[Any]			,			_lowercase   :    str		):
								return self.sp_model.encode(_lowercase			,			out_type=_lowercase		)







						def __a						(							self   :    Dict			,			_lowercase   :    List[str]		):
								if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
								A					       = self.sp_model.PieceToId(_lowercase		)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







						def __a						(							self   :    Optional[int]			,			_lowercase   :    str		):
								if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset		)







						def __a						(							self   :    List[str]			,			_lowercase   :    List[Any]		):
								A					       = ''.join(_lowercase		).replace(_lowercase			,			' '		).strip()
								return out_string







						def __a						(							self   :    Optional[Any]			,			_lowercase   :    str			,			_lowercase   :    Optional[str] = None		):
								if not os.path.isdir(_lowercase		):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
										return
								A					       = os.path.join(
								    _lowercase			,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowercase		) and os.path.isfile(self.vocab_file		):
										copyfile(self.vocab_file			,			_lowercase		)
								elif not os.path.isfile(self.vocab_file		):
										with open(_lowercase			,			'wb'		) as fi:
												A					       = self.sp_model.serialized_model_proto()
												fi.write(_lowercase		)

								return (out_vocab_file,)







						def __a						(							self   :    Optional[Any]			,			_lowercase   :    List[int]			,			_lowercase   :    Optional[List[int]] = None		):

								if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
								A					       = [self.sep_token_id]
								return token_ids_a + sep + token_ids_a + sep









"""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





