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

import regex as re

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


_UpperCAmelCase					: str      =       logging.get_logger(__name__)


_UpperCAmelCase					: List[Any]      =       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCAmelCase					: Optional[int]      =       {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

_UpperCAmelCase					: Tuple      =       {"facebook/blenderbot_small-90M": 512}


def       A       (					lowercase )   ->  Any:
		'''simple docstring'''

		UpperCamelCase		=     set()
		UpperCamelCase		=     word[0]
		for char in word[1:]:
				pairs.add((prev_char, char) )
				UpperCamelCase		=     char

		UpperCamelCase		=     set(lowercase )
		return pairs


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       Optional[Any]	     =				VOCAB_FILES_NAMES
		__lowercase       :       Dict	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       List[str]	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       Dict	     =				["input_ids", "attention_mask"]



		def __init__( self				,      A_				,      A_				,      A_="__start__"				,      A_="__end__"				,      A_="__unk__"				,      A_="__null__"				,      **A_				,      )  ->					List[str]:
				"""simple docstring"""
				super().__init__(unk_token=A_				,      bos_token=A_				,      eos_token=A_				,      pad_token=A_				,      **A_       )

				with open(A_				,      encoding='utf-8'       ) as vocab_handle:
						UpperCamelCase		=     json.load(A_       )
				UpperCamelCase		=     {v: k for k, v in self.encoder.items()}
				with open(A_				,      encoding='utf-8'       ) as merges_handle:
						UpperCamelCase		=     merges_handle.read().split('\n'       )[1:-1]
				UpperCamelCase		=     [tuple(merge.split()       ) for merge in merges]
				UpperCamelCase		=     dict(zip(A_				,      range(len(A_       )       )       )       )
				UpperCamelCase		=     {}



		@property
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				return len(self.encoder       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return dict(self.encoder				,      **self.added_tokens_encoder       )



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase		=     re.sub('([.,!?()])'				,      r' \1'				,      A_       )
				UpperCamelCase		=     re.sub('(\')'				,      r' \1 '				,      A_       )
				UpperCamelCase		=     re.sub(r'\s{2,}'				,      ' '				,      A_       )
				if "\n" in token:
						UpperCamelCase		=     token.replace('\n'				,      ' __newln__'       )

				UpperCamelCase		=     token.split(' '       )
				UpperCamelCase		=     []
				for token in tokens:
						if not len(A_       ):
								continue

						UpperCamelCase		=     token.lower()
						UpperCamelCase		=     tuple(A_       )
						UpperCamelCase		=     tuple(list(word[:-1]       ) + [word[-1] + '</w>']       )
						UpperCamelCase		=     get_pairs(A_       )

						if not pairs:
								words.append(A_       )
								continue

						while True:
								UpperCamelCase		=     min(A_				,      key=lambda A_       : self.bpe_ranks.get(A_				,      float('inf'       )       )       )
								if bigram not in self.bpe_ranks:
										break
								UpperCamelCase	,  UpperCamelCase		=     bigram
								UpperCamelCase		=     []
								UpperCamelCase		=     0

								while i < len(A_       ):
										try:
												UpperCamelCase		=     word.index(A_				,      A_       )
												new_word.extend(word[i:j]       )
												UpperCamelCase		=     j
										except ValueError:
												new_word.extend(word[i:]       )
												break

										if word[i] == first and i < len(A_       ) - 1 and word[i + 1] == second:
												new_word.append(first + second       )
												i += 2
										else:
												new_word.append(word[i]       )
												i += 1
								UpperCamelCase		=     tuple(A_       )
								UpperCamelCase		=     new_word
								if len(A_       ) == 1:
										break
								else:
										UpperCamelCase		=     get_pairs(A_       )
						UpperCamelCase		=     '@@ '.join(A_       )
						UpperCamelCase		=     word[:-4]

						UpperCamelCase		=     word
						words.append(A_       )
				return " ".join(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     []

				UpperCamelCase		=     re.findall(r'\S+\n?'				,      A_       )

				for token in words:
						split_tokens.extend(list(self.bpe(A_       ).split(' '       )       )       )
				return split_tokens



		def __UpperCamelCase ( self				,      A_       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     token.lower()
				return self.encoder.get(A_				,      self.encoder.get(self.unk_token       )       )



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				return self.decoder.get(A_				,      self.unk_token       )



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     ' '.join(A_       ).replace('@@ '				,      ''       ).strip()
				return out_string



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     os.path.join(
				    A_				,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
				UpperCamelCase		=     os.path.join(
				    A_				,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

				with open(A_				,      'w'				,      encoding='utf-8'       ) as f:
						f.write(json.dumps(self.encoder				,      indent=2				,      sort_keys=A_				,      ensure_ascii=A_       ) + '\n'       )

				UpperCamelCase		=     0
				with open(A_				,      'w'				,      encoding='utf-8'       ) as writer:
						writer.write('#version: 0.2\n'       )
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,      key=lambda A_       : kv[1]       ):
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
										    ' Please check that the tokenizer is not corrupted!'       )
										UpperCamelCase		=     token_index
								writer.write(' '.join(A_       ) + '\n'       )
								index += 1

				return vocab_file, merge_file


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 				lowercase					(		unittest.TestCase   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

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



		@require_torch
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				UpperCamelCase		=     VideoMAEFeatureExtractor(
				    size={'shortest_edge': 10}				,      crop_size={'height': 10, 'width': 10}       )
				UpperCamelCase		=     pipeline(
				    'video-classification'				,      model=A_				,      feature_extractor=A_				,      frame_sampling_rate=4       )

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

				UpperCamelCase		=     video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]				,      top_k=2				,      )
				self.assertEqual(
				    nested_simplify(A_				,      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       )  ->					Tuple:
				"""simple docstring"""
				pass
